%% Genetic Programming Bibliography
%%$Revision: 1.517 $ $Date: 2002/12/28 21:04:15 $ $Locker:  $
%%Created by W.B.Langdon@cs.ucl.ac.uk January 1995
%%Based on J.Koza's GP bibliography of 14 March 1994
%% To add references to your papers see
%% ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/biblio/gp-submit.html





















































































































@InProceedings{abbattista:1999:SAGAACS,
  author =       "Fabio Abbattista and Valeria Carofiglio and Mario
                 Koppen",
  title =        "Scout Algorithms and Genetic Algorithms: {A}
                 Comparative Study",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "769",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{aicsu-91:abbot,
  author =       "R. J. Abbott",
  title =        "Niches as a {GA} divide-and-conquer strategy",
  booktitle =    "Proceedings of the Second Annual AI Symposium for the
                 California State University",
  year =         "1991",
  editor =       "Art Chapman and Leonard Myers",
  publisher =    "California State University",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{abernathy:2000:UGASBCRB,
  author =       "Neil Abernathy",
  title =        "Using a Genetic Algorithm to Select Beam
                 Configurations for Radiosurgery of the Brain",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "1--7",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InCollection{abrams:2000:CSAMPR,
  author =       "Zoe Abrams",
  title =        "Complimentary Selection as an Alternative Method for
                 Population Reproduction",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "8--15",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{abramson:1996:cccGP,
  author =       "Myriam Abramson and Lawrence Hunter",
  title =        "Classification using Cultural Co-Evolution and Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "249--254",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://lhc.nlm.nih.gov/pub/hunter/gp96.ps",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{adamopoulos:1999:FMEPUGONN,
  author =       "Adam V. Adamopoulos and Efstratios F. Georgopoulos and
                 Spiridon D. Likothanassis and Photios A. Anninos",
  title =        "Forecasting the MagnetoEncephaloGram ({MEG}) of
                 Epileptic Patients Using Genetically Optimized Neural
                 Networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1457--1462",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{adams:2002:CSDPSAGP,
  author =       "Thomas P. Adams",
  title =        "Creation of Simple, Deadline, and Priority Scheduling
                 Algorithms using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "1--10",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp memory, SETMO..SETM4 ECJ8,
                 iteration (over job queue) branch and selection branch,
                 communicate via memory. Run time used throughout (ie by
                 functions and terminals) to identify jobs (ie
                 scheduling tasks). 32 tasks shortest job first",
}

@InProceedings{adorni:1998:cpapc,
  author =       "Giovanni Adorni and Federico Bergenti and Stefano
                 Cagnoni",
  title =        "A cellular-programming approach to pattern
                 classification",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "142--150",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "In this paper we discuss the capability of the
                 cellular programming approach to produce non-uniform
                 cellular automata performing two-dimensional pattern
                 classification. More precisely, after an introduction
                 to the evolutionary cellular automata model, we
                 describe a general approach suitable for designing
                 cellular classifiers. The approach is based on a set of
                 non-uniform cellular automata performing specific
                 classification tasks, which have been designed by means
                 of a cellular evolutionary algorithm.

                 The proposed approach is discussed together with some
                 preliminary results obtained on a benchmark data set
                 consisting of car-plate digits.",
  notes =        "EuroGP'98",
}

@InProceedings{adorni:1999:GPgkcsrcmsc,
  author =       "Giovanni Adorni and Stefano Cagnoni and Monica
                 Mordonini",
  title =        "Genetic Programming of a Goal­Keeper Control Strategy
                 for the RoboCup Middle Size Competition",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "109--119",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Robot goalkeeper (.4m) controlled by twin cameras using
                 GP. Able to intercept football sometimes.",
}

@InProceedings{adorni:2001:wsc6,
  author =       "Giovanni Adorni and Stefano Cagnoni",
  title =        "Design of Explicitly or Implicitly Parallel
                 Low-resolution Character Recognition Algorithms by
                 Means of Genetic Programming",
  booktitle =    "Soft Computing and Industry Recent Applications",
  year =         "2001",
  editor =       "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and
                 Takeshi Furuhashi and Frank Hoffmann",
  pages =        "387--398",
  month =        "10--24 " # sep,
  publisher =    "Springer-Verlag",
  note =         "Published 2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-85233-539-4",
  notes =        "WSC6
                 http://www.springer.de/cgi/svcat/search_book.pl?isbn=1-85233-539-4",
}

@InProceedings{agapie:1999:RSCC,
  author =       "Alexandru Agapie",
  title =        "Random Systems with Complete Connections",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "770",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{agapow:1996:cbecv,
  author =       "Paul-Michael Agapow",
  title =        "Computational Brittleness and the Evolution of
                 Computer Viruses",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  booktitle =    "Parallel Problem Solving From Nature IV. Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  publisher =    "Springer-Verlag",
  volume =       "1141",
  series =       "LNCS",
  pages =        "2--11",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
                 (UNIX Sun RISC) {"}programs are far less brittle than
                 expected{"}.",
}

@InCollection{agarwal:2000:GPWPPAPE,
  author =       "Ashish Agarwal",
  title =        "Genetic Programming for Wafer Property Prediction
                 After Plasma Enhanced",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "16--24",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@Article{agnelli:2002:PRL,
  author =       "Davide Agnelli and Alessandro Bollini and Luca
                 Lombardi",
  title =        "Image classification: an evolutionary approach",
  journal =      "Pattern Recognition Letters",
  year =         "2002",
  number =       "1-3",
  volume =       "23",
  pages =        "303--309",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V15-443K10X-6/1/7af8206767ca79f9898fec720a84c656",
  abstract =     "Evolutionary algorithms are proving viable in solving
                 complex optimization problems such as those typical of
                 supervised learning approaches to image understanding.
                 This paper presents an evolutionary approach to image
                 classification and discusses some experimental results,
                 suggesting that genetic programming could provide a
                 convenient alternative to standard supervised learning
                 methods.",
}

@InProceedings{aguilar:1998:rcmmcfssdft,
  author =       "Jose L. Aguilar and Mariela Cerrada",
  title =        "Reliability-Centered Maintenance Methodology-Based
                 Fuzzy Classifier System Design for Fault Tolerance",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "621",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{aguilar:1999:ABGAMOP,
  author =       "Jose Aguilar and Pablo Miranda",
  title =        "Approaches Based on Genetic Algorithms for
                 Multiobjective Optimization Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "3--10",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{aguilar:1999:TGADRSLS,
  author =       "Jesus Aguilar and Jose Riquelme and Miguel Toro",
  title =        "Three Geometric Approaches for representing Decision
                 Rules in a Supervised Learning System",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "771",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99).
                 See also aguilar:1999:T",
}

@InProceedings{aguilar:1999:T,
  author =       "Jesus Aguilar and Jose Riquelme and Miguel Toro",
  title =        "Three geometric approaches for representing decision
                 rules in a supervised learning system",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "8--15",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, data mining, supervised learning,
                 hyper rectangles, rotated hyper rectangles, hyper
                 ellipse",
  abstract =     "hyperrectangles, rotated hyperrectangles and
                 hyperellipses",
  notes =        "GECCO-99LB",
}

@InProceedings{aguilar3:2001:gecco,
  title =        "Fuzzy Classifier System and Genetic Programming on
                 System Identification Problems",
  author =       "Jose Aguilar and Mariela Cerrada",
  pages =        "1245--1251",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, real world
                 applications",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{aguirre:1999:EH,
  author =       "Arturo Hernandez Aguirre and Carlos A. Coello Coello
                 and Bill P. Buckles",
  title =        "A Genetic Programming Approach to Logic Function
                 Synthesis by Means of Multiplexers",
  booktitle =    "Proceedings of the The First NASA/DOD Workshop on
                 Evolvable Hardware",
  year =         "1999",
  editor =       "Adrian Stoica and Didier Keymeulen and Jason Lohn",
  pages =        "46--53",
  address =      "Pasadena, California",
  month =        "19-21 " # jul,
  organisation = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISBN =         "0-7695-0256-3",
  URL =          "http://computer.org/proceedings/eh/0256/02560046abs.htm",
  abstract =     "This paper presents an approach based on the use of
                 genetic programming to synthesize logic functions. The
                 proposed approach uses the 1-control line multiplexer
                 as the only design unit, defining any logic function
                 (defined by a truth table) through the replication of
                 this single unit. Our fitness function first explores
                 the search space trying to find a feasible design and
                 then concentrates in the minimization of such (fully
                 feasible) circuit. The proposed approach is illustrated
                 using several sample Boolean functions.",
  notes =        "EH-1999",
}

@InProceedings{aguirre:1999:CCMOGA,
  author =       "Hernan E. Aguirre and Kiyoshi Tanaka and Tatsuo
                 Sugimura",
  title =        "Cooperative Crossover and Mutation Operators in
                 Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "772",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ahluwalia:1996:ccpGP,
  author =       "Manu Ahluwalia and Terence C. Fogarty",
  title =        "Co-Evolving Classification Programs using Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "419",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@InProceedings{Ahluwalia:1997:,
  author =       "Manu Ahluwalia and Larry Bell and Terence C. Fogarty",
  title =        "Co-evolving Functions in Genetic Programming: {A}
                 Comparison in {ADF} Selection Strategies",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "3--8",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{ahluwalia:1997:cfGPea,
  author =       "Manu Ahluwalia and Larry Bull and Terence C. Fogarty",
  title =        "Co-evolving Functions in Genetic Programming: An
                 Emergent Approach using {ADF}s and {GL}i{B}",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "1--6",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{ahluwalia:1998:cfGP:ADF+GLiB,
  author =       "M. Ahluwalia and L. Bull",
  title =        "Co-evolving Functions in Genetic Programming: Dynamic
                 {ADF} Creation using {GL}i{B}",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "809--818",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 University of the West of England, UK",
}

@InProceedings{ahluwalia:1999:AGPCS,
  author =       "Manu Ahluwalia and Larry Bull",
  title =        "A Genetic Programming-based Classifier System",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "11--18",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ahluwalia:1999:CFGPCK,
  author =       "Manu Ahluwalia and Larry Bull",
  title =        "Coevolving Functions in Genetic Programming:
                 Classification using {K}-nearest-neighbour",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "947--952",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{Ahluwalia:2001:SA,
  author =       "Manu Ahluwalia and Larry Bull",
  title =        "Coevolving functions in genetic programming",
  journal =      "Journal of Systems Architecture",
  volume =       "47",
  pages =        "573--585",
  year =         "2001",
  number =       "7",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, ADF,
                 Classification, EDF, Feature selection/extraction,
                 Hierarchical programs, Knn, Speciation",
  URL =          "http://www.sciencedirect.com/science/article/B6V1F-43RV156-3/1/16dd3ab5502922479ef7bb1ca4f7b9c3",
  abstract =     "In this paper we introduce a new approach to the use
                 of automatically defined functions (ADFs) within
                 genetic programming. The technique consists of evolving
                 a number of separate sub-populations of functions which
                 can be used by a population of evolving main programs.
                 We present and refine a set of mechanisms by which the
                 number and constitution of the function sub-populations
                 can be defined and compare their performance on two
                 well-known classification tasks. A final version of the
                 general approach, for use explicitly on classification
                 tasks, is then presented. It is shown that in all cases
                 the coevolutionary approach performs better than
                 traditional genetic programming with and without
                 ADFs.",
}

@Article{Aho97,
  author =       "Hannu Ahonen and Paulo A. {de Souza Jr.} and
                 Vijayendra Kumar Garg",
  title =        "A genetic algorithm for fitting Lorentzian line shapes
                 in {M}össbauer spectra",
  journal =      "Nuclear Instruments and Methods in Physics Research
                 B",
  year =         "1997",
  volume =       "124",
  pages =        "633--638",
  month =        "5 " # may,
  email =        "souza@iacgu7.chemie.uni-mainz.de",
  keywords =     "genetic algorithms",
  ISSN =         "0168583X",
  abstract =     "A genetic algorithm was implemented for finding an
                 approximative solution to the problem of fitting a
                 combination of Lorentzian lines to a measured Mössbauer
                 spectrum. This iterative algorithm exploits the idea of
                 letting several solutions (individuals) compete with
                 each other for the opportunity of being selected to
                 create new solutions (reproduction). Each solution was
                 represend as a string of binary digits (chromossome).
                 In addition, the bits in the new solutions may be
                 switched randomly from zero to one or conversely
                 (mutation). The input of the program that implements
                 the genetic algorithm consists of the measured
                 spectrum, the maximum velocity, the peak positions and
                 the expected number of Lorentzian lines in the
                 spectrum. Each line is represented with the help of
                 three variables, which correspond to its intensity,
                 full line width at hald maxima and peak position. An
                 additional parameter was associated to the background
                 level in the spectrum. A chi-2 test was used for
                 determining the quality of each parameter combination
                 (fitness). The results obtained seem to be very
                 promising and encourage to further development of the
                 algorithm and its implementation.",
}

@InProceedings{aiyarak:1997:GPtootn,
  author =       "P. Aiyarak and A. S. Saket and M. C. Sinclair",
  title =        "Genetic Programming Approaches for Minimum Cost
                 Topology Optimisation of Optical Telecommunication
                 Networks",
  booktitle =    "Second International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1997",
  address =      "University of Strathclyde, Glasgow, UK",
  publisher_address = "Savoy Place, London, WC2R 0BL, UK",
  month =        "1-4 " # sep,
  publisher =    "IEE",
  email =        "mcs@essex.ac.uk",
  keywords =     "genetic algorithms, genetic programming,
                 telecommunication networks, topology",
  abstract =     "This paper compares the relative efficiency of three
                 approaches for the minimum-cost topology optimisation
                 of the COST 239 European Optical Network (EON) using
                 genetic programming. The GP was run for the central
                 nine nodes using three approaches: relational function
                 set, decision trees, and connected nodes. Only the best
                 two, decision trees and connected nodes, were run for
                 the full EON. The results are also compared with
                 earlier genetic algorithm work on the EON.",
  notes =        "GALESIA'97",
}

@InCollection{akalin:2002:DCOFSGGP,
  author =       "Frederick R. Akalin",
  title =        "Developing a Computer-Controller Opponent for a
                 First-Person Simulation Game using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "11--20",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{Akbarzadeh:1998:wcci,
  author =       "M. R. {Akbarzadeh-T} and E. Tunstel and K. Kumbla and
                 M. Jamshidi",
  title =        "Soft computing paradigms for hybrid fuzzy controllers:
                 experiments and applications",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "1200--1205",
  volume =       "2",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 neurocontrollers, fuzzy control, hierarchical systems,
                 mobile robots, path planning, brushless DC motors,
                 machine control, manipulators, soft computing
                 paradigms, hybrid fuzzy controllers, neural networks,
                 genetic algorithms, genetic programs, fuzzy logic-based
                 schemes, added intelligence, adaptation, learning
                 ability, direct drive motor, genetic algorithm-fuzzy
                 hierarchical controller, flexible robot link, genetic
                 programming-fuzzy behavior-based controller, mobile
                 robot navigation task",
  ISBN =         "0-7803-4863-X",
  URL =          "http://ieeexplore.ieee.org/iel4/5612/15018/00686289.pdf?isNumber=15018",
  size =         "6 pages",
  abstract =     "Neural networks (NN), genetic algorithms (GA), and
                 genetic programs (GP) are often augmented with fuzzy
                 logic-based schemes to enhance artificial intelligence
                 of a given system. Such hybrid combinations are
                 expected to exhibit added intelligence, adaptation, and
                 learning ability. In the paper, implementation of three
                 hybrid fuzzy controllers are discussed and verified by
                 experimental results. These hybrid controllers consist
                 of a hierarchical NN-fuzzy controller applied to a
                 direct drive motor, a GA-fuzzy hierarchical controller
                 applied to a flexible robot link, and a GP-fuzzy
                 behavior-based controller applied to a mobile robot
                 navigation task. It is experimentally shown that all
                 three architectures are capable of significantly
                 improving the system response.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@Article{Akbarzadeh-T:2000:CEE,
  author =       "M.-R. {Akbarzadeh-T} and K. Kumbla and E. Tunstel and
                 M. Jamshidi",
  title =        "Soft computing for autonomous robotic systems",
  journal =      "Computers \& Electrical Engineering",
  volume =       "26",
  pages =        "5--32",
  year =         "2000",
  number =       "1",
  keywords =     "genetic algorithms, genetic programming, Soft
                 computing, Neural networks, Fuzzy logic, Robotic
                 control, Articial intelligence",
  URL =          "http://www.sciencedirect.com/science/article/B6V25-3Y6GXY5-2/1/6a6f9ff946815d4e95fe3884c98e74e5",
  size =         "28 pages",
  abstract =     "Neural networks (NN), genetic algorithms (GA), and
                 genetic programming (GP) are augmented with fuzzy
                 logic-based schemes to enhance artificial intelligence
                 of automated systems. Such hybrid combinations exhibit
                 added reasoning, adaptation, and learning ability. In
                 this expository article, three dominant hybrid
                 approaches to intelligent control are experimentally
                 applied to address various robotic control issues which
                 are currently under investigation at the NASA Center
                 for Autonomous Control Engineering. The hybrid
                 controllers consist of a hierarchical NN-fuzzy
                 controller applied to a direct drive motor, a GA-fuzzy
                 hierarchical controller applied to position control of
                 a flexible robot link, and a GP-fuzzy behavior based
                 controller applied to a mobile robot navigation task.
                 Various strong characteristics of each of these hybrid
                 combinations are discussed and utilized in these
                 control architectures. The NN-fuzzy architecture takes
                 advantage of NN for handling complex data patterns, the
                 GA-fuzzy architecture utilizes the ability of GA to
                 optimize parameters of membership functions for
                 improved system response, and the GP-fuzzy architecture
                 utilizes the symbolic manipulation capability of GP to
                 evolve fuzzy rule-sets.",
}

@InProceedings{akira:2000:moelGP,
  author =       "Yoshida Akira",
  title =        "Intraspecific Evolution of Learning by Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "209--224",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@TechReport{Alander:1995:ibGP,
  author =       "Jarmo T. Alander",
  title =        "An Indexed Bibliography of Genetic Programming",
  institution =  "Department of Information Technology and Industrial
                 Management, University of Vaasa",
  year =         "1995",
  type =         "Report Series no",
  number =       "94-1-GP",
  address =      "Finland",
  URL =          "ftp://ftp.uwasa.fi/cs/report94-1/gaGPbib.ps.Z",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "220 references. Indexed by subject, publication type
                 and author",
  notes =        "http url reference not working Jan 95. ftp ok. Part of
                 Alander's index of genetic algorithm publications
                 (older versions, ie up to ~1993, are available via ftp,
                 see ENCORE sites). New version dated May 18, 1995.

                 See also Jarmo T. Alander. An indexed bibliography of
                 genetic algorithms: Years 1957-1993. Art of CAD Ltd.,
                 Vaasa (Finland), 1994. (over 3000 GA references).",
  size =         "46 pages",
}

@Book{Alander:1994:bib,
  author =       "Jarmo T. Alander",
  title =        "An Indexed Bibliography of Genetic Algorithms: Years
                 1957--1993",
  year =         "1994",
  publisher =    "Art of CAD ltd",
  address =      "Vaasa, Finland",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "All GAs some 3000+ references",
}

@InProceedings{alba:1996:tGPrdflc,
  author =       "Enrique Alba and Carlos Cotta and Jose J. Troyo",
  title =        "Type-Constrained Genetic Programming for Rule-Base
                 Definition in Fuzzy Logic Controllers",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "255--260",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{alba:1999:ERASPSPDGA,
  author =       "Enrique Alba and Carlos Cotta and Jose M. Troya",
  title =        "Entropic and Real-Time Analysis of the Search with
                 Panmictic, Structured, and Parallel Distributed Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "773",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{alba:1999:T,
  author =       "Enrique Alba and Jose M. Troya",
  title =        "Tackling epistasis with panmictic and structured
                 genetic algorithms",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "1--7",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, NK",
  notes =        "GECCO-99LB",
}

@InProceedings{albuquerque:2000:irfl,
  author =       "Paul Albuquerque and Bastien Chopard and Christian
                 Mazza and Marco Tomassini",
  title =        "On the Impact of the Representation on Fitness
                 Landscapes",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "1--15",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "In this paper we study the role of program
                 representation on the properties of a type of Genetic
                 Programming (GP) algorithm. In a specific case, which
                 we believe to be generic of standard GP, we show that
                 the way individuals are coded is an essential concept
                 which impacts the fitness landscape. We give evidence
                 that the ruggedness of the landscape affects the
                 behavior of the algorithm and we find that, below a
                 critical population, whose size is
                 representation-dependent, premature convergence
                 occurs.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InCollection{alderson:1999:TTCNDUEM,
  author =       "David Alderson",
  title =        "Toward a Technique for Cooperative Network Design
                 Using Evolutionary Methods",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "1--10",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{aler:1998:5parity,
  author =       "Ricardo Aler",
  title =        "Immediate transference of global improvements to all
                 individuals in a population in Genetic Programming
                 compared to Automatically Defined Functions for the
                 {EVEN}-5 {PARITY} problem",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "60--70",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "Koza has shown how automatically defined functions
                 (ADFs) can reduce computational effort in the GP
                 paradigm. In Koza's ADF, as well as in standard GP, an
                 improvement in a part of a program (an ADF or a main
                 body) can only be transferred via crossover. In this
                 article, we consider whether it is a good idea to
                 transfer immediately improvements found by a single
                 individual to the whole population. A system that
                 implements this idea has been proposed and tested for
                 the EVEN-5-PARITY and EVEN-6-PARITY problems. Results
                 are very encouraging: computational effort is reduced
                 (compared to Koza's ADFs) and the system seems to be
                 less prone to early stagnation. Finally, our work
                 suggests further research where less extreme approaches
                 to our idea could be tested.",
  notes =        "EuroGP'98",
}

@InProceedings{aler:1998:ehp,
  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  title =        "Evolved Heuristics for Planning",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "745--754",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 EvoCK compared with PRODIGY. HAMLET. Blocksworld
                 domain.",
}

@InProceedings{icml98-ricardo,
  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  title =        "Genetic Programming and Deductive-Inductive Learning:
                 {A} Multistrategy Approach",
  booktitle =    "Proceedings of the Fifteenth International Conference
                 on Machine Learning, ICML'98",
  year =         "1998",
  editor =       "Jude Shavlik",
  pages =        "10--18",
  address =      "Madison, Wisconsin, USA",
  month =        jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, Learning in
                 Planning, Multistrategy learning",
  ISBN =         "1-55860-556-8",
  URL =          "http://grial.uc3m.es/~grial/aler/ML98-42.ps",
  size =         "9 pages",
  abstract =     "Genetic Programming (GP) is a machine learning
                 technique that was not conceived to use domain
                 knowledge for generating new candidate solutions. It
                 has been shown that GP can benefit from domain
                 knowledge obtained by other machine learning methods
                 with more powerful heuristics. However, it is not
                 obvious that a combination of GP and a knowledge
                 intensive machine learning method can work better than
                 the knowledge intensive method alone. In this paper we
                 present a multistrategy approach where an already
                 multistrategy approach ({\sc hamlet} combines
                 analytical and inductive learning) and an evolutionary
                 technique based on GP (EvoCK) are combined for the task
                 of learning control rules for problem solving in
                 planning. Results show that both methods complement
                 each other, supplying to the other method what the
                 other method lacks and obtaining better results than
                 using each method alone.",
  notes =        "ICML'98 http://www.cs.wisc.edu/icml98/ blocksworld
                 many random problems generated in order to train
                 system. No crossover, steady state population size = 2,
                 tournament size = 2",
}

@PhdThesis{aler:thesis,
  author =       "Ricardo Aler Mur",
  title =        "Programación Genética de Heurísticas para
                 Planificación",
  school =       "Facultad de Informática de la Universidad Politécnica
                 de Madrid",
  year =         "1999",
  address =      "Spain",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Planning,
                 Problem Solving, Rule Based System",
  size =         "200 pages",
  abstract =     "The aim of this thesis is to use and extend the
                 machine learning genetic programming (GP) paradigm to
                 learn control knowledge for domain independent
                 planning. GP will be used as a standalone technique and
                 as part of a multi-strategy system.

                 Planning is the problem of finding a sequence of steps
                 to transform an initial state in a final state. Finding
                 a correct plan is NP-hard. A solution proposed by
                 Artificial Intelligence is to augment a domain
                 independent planner with control knowledge, to improve
                 its efficiency. Machine learning techniques are used
                 for that purpose. However, although a lot has been
                 achieved, the domain independent planning problem has
                 not been solved completely, therefore there is still
                 room for research.

                 The reason for using GP to learn planning control
                 knowledge is twofold. First, it is intended for
                 exploring the control knowledge space in a less biased
                 way than other techniques. Besides, learning search
                 control knowledge with GP will consider the planning
                 system, the domain theory, planning search and
                 efficiency measures in a global manner, all at the same
                 time. Second, GP flexibility will be used to add useful
                 biases and characteristics to another learning method
                 that lacks them (that is, a multi-strategy GP based
                 system). In the present work, Prodigy will be used as
                 the base planner and Hamlet will be used as the
                 learning system to which useful characteristics will be
                 added through GP. In other words, GP will be used to
                 solve some of Hamlet limitations by adding new
                 biases/characteristics to Hamlet.

                 In addition to the main goal, this thesis will design
                 and experiment with methods to add background knowledge
                 to a GP system, without modifying its basic algorithm.
                 The first method seeds the initial population with
                 individuals obtained by another method (Hamlet).
                 Actually, this is the multi-strategy system discussed
                 in the later paragraph. The second method uses a new
                 genetic operator (instance based crossover) that is
                 able to use instances/examples to bias its search, like
                 other machine learning techniques.

                 To test the validity of the methods proposed, extensive
                 empirical and statistical validation will be carried
                 out.",
  notes =        "In Spanish: Genetic Programming of Heuristics for
                 Planning School of Computer Science at Polytechnic
                 University of Madrid Author: Ricardo Aler Mur
                 Supervisors: Daniel Borrajo Millán and Pedro Isasi
                 Viñuela

                 ",
}

@InProceedings{aler:2000:G,
  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  title =        "{GP} fitness functions to evolve heuristics for
                 planning",
  booktitle =    "Evolutionary Methods for AI Planning",
  year =         "2000",
  editor =       "Martin Middendorf",
  pages =        "189--195",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{aler:2001:glckg,
  author =       "Ricardo Aler and Daniel Borrajo and Pedro lsasi",
  title =        "Grammars for Learning Control Knowledge with {GP}",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1220--1227",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 EVOCK, PRODIGY 4.0, blocksworld",
}

@Article{aler:2001:ECJ,
  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  title =        "Learning to Solve Planning Problems Efficiently by
                 Means of Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "2001",
  volume =       "9",
  number =       "4",
  pages =        "387--420",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming, genetic
                 planning, evolving heuristics, planning, search. EvoCK,
                 STGP, blocks world, logistics, Prodigy4.0, STRIPS,
                 PDL40.",
  abstract =     "Declarative problem solving, such as planning, poses
                 interesting challenges for Genetic Programming (GP).
                 There have been recent attempts to apply GP to planning
                 that fit two approaches: (a) using GP to search in plan
                 space or (b) to evolve a planner. In this article, we
                 propose to evolve only the heuristics to make a
                 particular planner more efficient. This approach is
                 more feasible than (b) because it does not have to
                 build a planner from scratch but can take advantage of
                 already existing planning systems. It is also more
                 efficient than (a) because once the heuristics have
                 been evolved, they can be used to solve a whole class
                 of different planning problems in a planning domain,
                 instead of running GP for every new planning problem.
                 Empirical results show that our approach (EVOCK) is
                 able to evolve heuristics in two planning domains (the
                 blocks world and the logistics domain) that improve
                 PRODIGY4.0 performance. Additionally, we experiment
                 with a new genetic operator Instance-Based Crossover
                 that is able to use traces of the base planner as raw
                 genetic material to be injected into the evolving
                 population.",
}

@Article{aler:2002:AI,
  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  title =        "Using genetic programming to learn and improve control
                 knowledge",
  journal =      "Artificial Intelligence",
  year =         "2002",
  volume =       "141",
  number =       "1-2",
  pages =        "29--56",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Speedup
                 learning, Multi-strategy learning, Planning",
  abstract =     "The purpose of this article is to present a
                 multi-strategy approach to learn heuristics for
                 planning. This multi-strategy system, called -,
                 combines a learning algorithm specialised in planning
                 () and a genetic programming (GP) based system (:
                 Evolution of Control Knowledge). Both systems are able
                 to learn heuristics for planning on their own, but both
                 of them have weaknesses. Based on previous experience
                 and some experiments performed in this article, it is
                 hypothesised that handicaps are due to its
                 example-driven operators and not having a way to
                 evaluate the usefulness of its control knowledge. It is
                 also hypothesized that even if control knowledge is
                 sometimes incorrect, it might be easily correctable.
                 For this purpose, a GP-based stage is added, because of
                 its complementary biases: GP genetic operators are not
                 example-driven and it can use a fitness function to
                 evaluate control knowledge. and are combined by seeding
                 initial population with control knowledge. It is also
                 useful for to start from a knowledge-rich population
                 instead of a random one. By adding the GP stage to ,
                 the number of solved problems increases from 58% to 85%
                 in the blocks world and from 50% to 87% in the
                 logistics domain (0% to 38% and 0% to 42% for the
                 hardest instances of problems considered).",
  notes =        "Hamlet, EvoCK, PRODIGY 4.0",
}

@InProceedings{alganova:2000:efemvlf,
  author =       "Tatiana Kalganova",
  title =        "An Extrinsic Function-Level Evolvable Hardware
                 Approach",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "60--75",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "The function level evolvable hardware approach to
                 synthesize the combinational multi-valued and binary
                 logic functions is proposed in first time. The new
                 representation of logic gate in extrinsic EHW allows us
                 to describe behaviour of any multi-input multi-output
                 logic function. The circuit is represented in the form
                 of connections and functionalities of a rectangular
                 array of building blocks. Each building block can
                 implement primitive logic function or any multi-input
                 multi-output logic function defined in advance. The
                 method has been tested on evolving logic circuits using
                 half adder, full adder and multiplier. The
                 effectiveness of this approach is investigated for
                 multi-valued and binary arithmetical functions. For
                 these functions either method appears to be much more
                 efficient than similar approach with two-input
                 one-output cell representation.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InCollection{almgren:2000:CADGP,
  author =       "Magnus Almgren",
  title =        "Communicating Agents Developed with Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "25--32",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InCollection{kinnear:altenberg,
  author =       "Lee Altenberg",
  title =        "The Evolution of Evolvability in Genetic Programming",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  year =         "1994",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  pages =        "47--74",
  chapter =      "3",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dynamics.org/~altenber/PAPERS/EEGP/",
  abstract =     "The notion of ``evolvability'' --- the ability of a
                 population to produce variants fitter than any yet
                 existing --- is developed as it applies to genetic
                 algorithms. A theoretical analysis of the dynamics of
                 genetic programming predicts the existence of a novel,
                 emergent selection phenomenon: the evolution of
                 evolvability. This is produced by the proliferation,
                 within programs, of blocks of code that have a higher
                 chance of increasing fitness when added to programs.
                 Selection can then come to mold the {\em variational}
                 aspects of the way evolved programs are represented. A
                 model of code proliferation within programs is analyzed
                 to illustrate this effect. The mathematical and
                 conceptual framework includes: the definition of
                 evolvability as a measure of performance for genetic
                 algorithms; application of Price's {\em Covariance and
                 Selection Theorem} to show how the fitness function,
                 representation, and genetic operators must interact to
                 produce evolvability --- namely, that genetic operators
                 produce offspring with fitnesses specifically
                 correlated with their parent's fitnesses; how blocks of
                 code emerge as a new level of replicator, proliferating
                 as a function of their ``constructional fitness'',
                 which is distinct from their schema fitness; and how
                 programs may change from innovative code to
                 conservative code as the populations mature. Several
                 new selection techniques and genetic operators are
                 proposed in order to give better control over the
                 evolution of evolvability and improved evolutionary
                 performance.

                 Copyright 1996 Lee Altenberg",
  notes =        "

                 Price's Covariance and Selection Theorem 1970 Nature
                 227 pages 520-521 Fisher's Theorem 1930 {"}The
                 Genetical Theory of Natural Selection, Clarendon Press,
                 Oxford, UK pages 30-37 Generally better theory for GP
                 -> additional fitness (of blocks)

                 Also known as Altenberg:1994EEGP",
  size =         "29 pages",
}

@InProceedings{Altenberg:1994EBR,
  author =       "Lee Altenberg",
  year =         "1994",
  pages =        "182--187",
  title =        "Evolving better representations through selective
                 genome growth",
  booktitle =    "Proceedings of the 1st IEEE Conference on Evolutionary
                 Computation",
  publisher =    "IEEE",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher_address = "Piscataway, NJ, USA",
  volume =       "1",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dynamics.org/~altenber/PAPERS/EBR/",
  abstract =     "The choice of how to represent the search space for a
                 genetic algorithm (GA) is critical to the GA's
                 performance. Representations are usually engineered by
                 hand and fixed for the duration of the GA run. Here a
                 new method is described in which the degrees of freedom
                 of the representation --- i.e. the genes -- are
                 increased incrementally. The phenotypic effects of the
                 new genes are randomly drawn from a space of different
                 functional effects. Only those genes that initially
                 increase fitness are kept. The genotype-phenotype map
                 that results from this selection during the
                 constructional of the genome allows better adaptation.
                 This effect is illustrated with the NK landscape model.
                 The resulting genotype-phenotype maps are much less
                 epistatic than generic maps would be. They have
                 extremely low values of ``K'' --- the number of fitness
                 components affected by each gene. Moreover, these maps
                 are exquisitely tuned to the specifics of the random
                 fitness functions, and achieve fitnesses many standard
                 deviations above generic NK landscapes with the same
                 \gp\ maps. The evolved maps create adaptive landscapes
                 that are much smoother than generic NK landscapes ever
                 are. Thus a caveat should be made when making arguments
                 about the applicability of generic properties of
                 complex systems to evolved systems. This method may
                 help to solve the problem of choice of representations
                 in genetic algorithms.

                 Copyright 1996 Lee Altenberg",
  notes =        "

                 ",
}

@InProceedings{Altenberg:1994EPIGP,
  author =       "Lee Altenberg",
  year =         "1994",
  title =        "Emergent phenomena in genetic programming",
  booktitle =    "Evolutionary Programming --- Proceedings of the Third
                 Annual Conference",
  editor =       "Anthony V. Sebald and Lawrence J. Fogel",
  publisher =    "World Scientific Publishing",
  pages =        "233--241",
  address =      "San Diego, CA, USA",
  month =        "24-26 " # feb,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "981-02-1810-9",
  URL =          "http://dynamics.org/~altenber/PAPERS/EPIGP/",
  abstract =     "Evolutionary computation systems exhibit various
                 emergent phenomena, primary of which is adaptation. In
                 genetic programming, because of the indeterminate
                 nature of the representation, the evolution of both
                 recombination distributions and representations can
                 emerge from the population dynamics. A review of ideas
                 on these phenomena is presented, including theory on
                 the evolution of evolvability through differential
                 proliferation of subexpressions within programs. An
                 analysis is given of a model of genetic programming
                 dynamics that is supportive of the ``Soft Brood
                 Selection'' conjecture, which was proposed as a means
                 to counteract the emergence of highly conservative
                 code, and instead favor highly evolvable
                 code.

                 Copyright 1996 Lee Altenberg",
  notes =        "

                 EP-94 http://www.wspc.com.sg/books/compsci/2401.html
                 http://www.natural-selection.com/eps/EP94.html",
}

@InProceedings{Altenberg:1995STPT,
  author =       "Lee Altenberg",
  year =         "1995",
  title =        "The {Schema} {Theorem} and {Price}'s {Theorem}",
  booktitle =    "Foundations of Genetic Algorithms 3",
  editor =       "L. Darrell Whitley and Michael D. Vose",
  publisher =    "Morgan Kaufmann",
  publisher_address = "San Francisco, CA, USA",
  address =      "Estes Park, Colorado, USA",
  pages =        "23--49",
  month =        "31 " # jul # "--2 " # aug # " 1994",
  organisation = "International Society for Genetic Algorithms",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-356-5",
  URL =          "http://dynamics.org/~altenber/PAPERS/STPT/",
  abstract =     "Holland's Schema Theorem is widely taken to be the
                 foundation for explanations of the power of genetic
                 algorithms (GAs). Yet some dissent has been expressed
                 as to its implications. Here, dissenting arguments are
                 reviewed and elaborated upon, explaining why the Schema
                 Theorem has no implications for how well a GA is
                 performing. Interpretations of the Schema Theorem have
                 implicitly assumed that a correlation exists between
                 parent and offspring fitnesses, and this assumption is
                 made explicit in results based on Price's Covariance
                 and Selection Theorem. Schemata do not play a part in
                 the performance theorems derived for representations
                 and operators in general. However, schemata re-emerge
                 when recombination operators are used. Using
                 Geiringer's recombination distribution representation
                 of recombination operators, a ``missing'' schema
                 theorem is derived which makes explicit the intuition
                 for when a GA should perform well. Finally, the method
                 of ``adaptive landscape'' analysis is examined and
                 counterexamples offered to the commonly used
                 correlation statistic. Instead, an alternative
                 statistic---the transmission function in the fitness
                 domain--- is proposed as the optimal statistic for
                 estimating GA performance from limited
                 samples.

                 Copyright 1996 Lee Altenberg",
  notes =        "FOGA-3

                 Deals with GAs as a whole, not specifically GP.",
}

@InCollection{Altenberg:1995GGEGPM,
  author =       "Lee Altenberg",
  year =         "1995",
  title =        "Genome growth and the evolution of the
                 genotype-phenotype map",
  booktitle =    "Evolution as a Computational Process",
  editor =       "Wolfgang Banzhaf and Frank H. Eeckman",
  publisher =    "Springer-Verlag",
  address =      "Berlin, Germany",
  pages =        "205--259",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dynamics.org/~altenber/PAPERS/GGEGPM/",
  size =         "55 pages",
  abstract =     "The evolution of new genes is distinct from evolution
                 through allelic substitution in that new genes bring
                 with them new degrees of freedom for genetic
                 variability. Selection in the evolution of new genes
                 can therefore act to sculpt the dimensions of
                 variability in the genome. This ``constructional''
                 selection effect is an evolutionary mechanism, in
                 addition to genetic modification, that can affect the
                 variational properties of the genome and its
                 evolvability.

                 One consequence is a form of genic selection: genes
                 with large potential for generating new useful genes
                 when duplicated ought to proliferate in the genome,
                 rendering it ever more capable of generating adaptive
                 variants. A second consequence is that alleles of new
                 genes whose creation produced a selective advantage may
                 be more likely to also produce a selective advantage,
                 provided that gene creation and allelic variation have
                 correlated phenotypic effects. A fitness distribution
                 model is analyzed which demonstrates these two effects
                 quantitatively.

                 These are effects that select on the nature of the
                 genotype-phenotype map. New genes that perturb numerous
                 functions under stabilizing selection, i.e. with high
                 pleiotropy, are unlikely to be advantageous. Therefore,
                 genes coming into the genome ought to exhibit low
                 pleiotropy during their creation. If subsequent
                 offspring genes also have low pleiotropy, then genic
                 selection can occur. If subsequent allelic variation
                 also has low pleiotropy, then that too should have a
                 higher chance of not being deleterious.

                 The effects on pleiotropy are illustrated with two
                 model genotype-phenotype maps: Wagner's linear
                 quantitative-genetic model with Gaussian selection, and
                 Kauffman's ``NK'' adaptive landscape model.
                 Constructional selection is compared with other
                 processes and ideas about the evolution of constraints,
                 evolvability, and the genotype-phenotype map. Empirical
                 phenomena such as dissociability in development,
                 morphological integration, and exon shuffling are
                 discussed in the context of this evolutionary
                 process.

                 Copyright 1996 Lee Altenberg",
  notes =        "

                 ",
}

@Unpublished{Altenberg:and:Feldman:1995SGTEMG2,
  author =       "Lee Altenberg and Marcus W. Feldman",
  year =         "1995",
  title =        "Selection, generalized transmission, and the evolution
                 of modifier genes. {II}. {M}odifier polymorphisms",
  note =         "In preparation",
  URL =          "ftp://ftp.mhpcc.edu/pub/incoming/altenberg/LeeSGTEMG2MP.ps.Z",
  notes =        "

                 ",
}

@InProceedings{alvarez:1998:,
  author =       "Luis F. Alvarez and Vassili V. Toropov",
  title =        "Application of Genetic Programming to the Choice of a
                 Structure of Global Approximations",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{amos:1998:DNAsbc,
  author =       "Martyn Amos and Paul E. Dunne and Alan Gibbons",
  title =        "{DNA} Simulation of Boolean Circuits",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "679--683",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@TechReport{anderson:1994:profile,
  author =       "Kenneth R. Anderson",
  title =        "Courage in Profiling",
  institution =  "BBN",
  year =         "1994",
  month =        "28 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://openmap.bbn.com/pub/kanderson/faster94/faster94/postscript/profile.ps",
  notes =        "Compares speed of GP systems written in C (Tackett's
                 SGPC which uses a tree representation) and Lisp (John
                 Koza) on a symbolic regression problem, Optimised lisp
                 performs better than expected. Koza lisp GP code
                 performance improved 30 fold by use of
                 profiling.

                 Software:
                 ftp://openmap.bbn.com/pub/kanderson/faster94/faster94/courage/koza3.lisp

                 2. You can easily convert your eval into a closure
                 compiler: Paper:
                 http://www.iro.umontreal.ca/~feeley/papers/complang87.ps.gz",
}

@InProceedings{anderson:ppsn2002:pp689,
  author =       "Eike Falk Anderson",
  title =        "Off-Line Evolution of Behaviour for Autonomous Agents
                 in Real-Time Computer Games",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "689 ff.",
  year =         "2002",
  editor =       "H.-P. Schwefel {J.-J. Merelo Guerv\'os, P. Adamidis,
                 H.-G. Beyer, J.-L. Fern\'andez-Villaca\~nas}",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Keywords: Activities::Games, Related::Machine
                 Learning, Technique::Fitness - Evaluation,
                 Technique::Genetic programming - general",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900689.pdf",
}

@InProceedings{andersson:1999:rmbGPrc,
  author =       "Bjorn Andersson and Per Svensson and Peter Nordin and
                 Mats Nordahl",
  title =        "Reactive and Memory­Based Genetic Programming for
                 Robot Control",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "161--172",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 AIMGP machine code GP, memory, simulated robot",
}

@InProceedings{andersson:2000:4lrGP,
  author =       "Bjorn Andersson and Per Svensson and Peter Nordin and
                 Mats Nordahl",
  title =        "On-line Evolution of Control for a Four-Legged Robot
                 Using Genetic Programming",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and George D. Smith
                 and David Corne and Martin Oates and Emma Hart and Pier
                 Luca Lanzi and Egbert Jan Willem and Yun Li and Ben
                 Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "319--326",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, linear GP",
  ISBN =         "3-540-67353-9",
  notes =        "{"}Galloping only appears after many hours of
                 training{"} p323.

                 EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM,
                 EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April
                 17, 2000
                 Proceedings

                 http://evonet.dcs.napier.ac.uk/evoworkshops/

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67353-9",
}

@InProceedings{Andersson:1998:ecmlc,
  author =       "Claes Andersson and Mats G. Nordahl",
  title =        "Evolving Coupled Map Lattices for Computation",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "151--162",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "Genetic Programming is used to evolve coupled map
                 lattices for density classification. The most
                 successful evolved rules depending only on nearest
                 neighbors (r=1) show better performance than existing
                 r=3 cellular automaton rules on this task.",
  notes =        "EuroGP'98",
}

@InProceedings{ando:2002:mgnbhg,
  author =       "Shin Ando and Hitoshi Iba and Erina Sakamoto",
  title =        "Modeling Genetic Network by Hybrid {GP}",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "291--296",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
}

@Misc{andre:UGthesis,
  author =       "David Andre",
  title =        "Artificial Evolution of Intelligence: Lessons from
                 natural evolution: An illustrative approach using
                 Genetic Programming",
  school =       "Stanford University, Symbolic Systems Program",
  year =         "1994",
  type =         "BS Honors Thesis",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{kinnear:andre,
  title =        "Automatically Defined Features: The Simultaneous
                 Evolution of 2-Dimensional Feature Detectors and an
                 Algorithm for Using Them",
  author =       "David Andre",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "477--494",
  keywords =     "genetic algorithms, genetic programming",
  chapter =      "23",
  size =         "18 pages",
  notes =        "

                 Mixture of GP and two dee GA",
}

@InProceedings{andre:maps,
  author =       "David Andre",
  title =        "Evolution of Mapmaking Ability: Strategies for the
                 evolution of learning, planning, and memory using
                 genetic programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "250--255",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{ieee94:andre,
  author =       "David Andre",
  title =        "Learning and Upgrading Rules for an {OCR} System Using
                 Genetic Programming",
  year =         "1994",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  notes =        "Uses GP both to recognise C in various fonts and to
                 maintain manually produced extremely high level code
                 when a new font is added",
}

@InProceedings{Andre:1995:ammsp,
  author =       "David Andre",
  title =        "The Evolution of Agents that Build Mental Models and
                 Create Simple Plans Using Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "248--255",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms, memory",
  ISBN =         "1-55860-370-0",
  size =         "8 pages",
  notes =        "Worlds 2x2, 4x4 and 8x8. Separate program trees for
                 mapmaker and mapuser. ADFs used by mapusers only. Uses
                 repeat, repeati (Repeat_Index), IncMem. Torrodial
                 memory, isomophic to world. Steady state, Tournament
                 selection (8) but with smaller (2) tournament group
                 size for deletion. Evolved programs subjected to
                 analysis and explanation. Evolved general solutions
                 from limited test cases. Suggests simple strategies
                 dominate more complex ones. GP better than random.",
}

@InProceedings{andre:1995:parallel,
  author =       "David Andre and John R. Koza",
  title =        "Parallel Genetic Programming on a Network of
                 Transputers",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "111--120",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  size =         "10 pages",
  notes =        "like Koza:1995:pGPnt part of rosca:1995:ml",
}

@InProceedings{andre:1995:apalmm,
  author =       "David Andre",
  title =        "The Automatic Programming of Agents that Learn Mental
                 Models and Create Simple Plans of Action",
  booktitle =    "IJCAI-95 Proceedings of the Fourteenth International
                 Joint Conference on Artificial Intelligence",
  year =         "1995",
  volume =       "1",
  pages =        "741--747",
  address =      "Montreal, Quebec, Canada",
  publisher_address = "San Francisco, CA, USA",
  month =        "20-25 " # aug,
  organisation = "IJCAII,AAAI,CSCSI",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "1-55860-363-8",
  notes =        "MAPMAKER searches for gold",
}

@InProceedings{andre:1996:GKL,
  author =       "David Andre and Forrest H {Bennett III} and John R.
                 Koza",
  title =        "Evolution of Intricate Long-Distance Communication
                 Signals in Cellular Automata using Genetic
                 Programming",
  booktitle =    "Artificial Life V: Proceedings of the Fifth
                 International Workshop on the Synthesis and Simulation
                 of Living Systems",
  year =         "1996",
  volume =       "1",
  address =      "Nara, Japan",
  publisher_address = "Cambridge, MA, USA",
  month =        "16--18 " # may,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/alife-gkl-96.ps",
  size =         "8 pages",
  abstract =     "It is exceedingly difficult to program cellular
                 automata. This is especially true when the desired
                 computation requires global communication and global
                 integration of information across great distances of
                 time and space in the cellular space. Various
                 human-written algorithms have appeared in the past two
                 decades for the vexatious majority classification task
                 for one-dimensional two-state cellular automata. This
                 paper describes how genetic programming with
                 automatically defined functions evolved a rule for this
                 task with an accuracy of 82.326%. This level of
                 accuracy exceeds that of the original 1978
                 Gacs-Kurdyumov-Levin (GKL) rule, all other known
                 human-written rules, and all other known rules produced
                 by automated methods. The rule evolved by genetic
                 programming is qualitatively different from all
                 previous rules in that it employs a larger and more
                 intricate repertoire of domains and particles to
                 represent and communicate information across the
                 cellular space.",
  notes =        "Alife-5 A longer version of this paper will be
                 presented at the GP-96 conference. GP gets best
                 solution to GKL problem

                 {"}The population size used to evolve the current
                 world's record for the GKL majority classification
                 1-dimensionall 2-sate 7-neighbor cellular authomata
                 problem was 51,200.

                 I believe Melanie Mitchell at the Santa Fe Institute
                 has been doing continuing additional work on using GAs
                 to evolve CA rules for various other problems.{"}",
}

@InCollection{andre:1996:aigp2,
  author =       "David Andre and John R. Koza",
  title =        "Parallel Genetic Programming: {A} Scalable
                 Implementation Using The Transputer Network
                 Architecture",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "317--338",
  chapter =      "16",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "This chapter describes the parallel implementation of
                 genetic programming in the C programming language using
                 a PC type computer (running Windows) acting as a host
                 and a network of processing nodes using the transputer
                 architecture. Using this approach, researchers of
                 genetic algorithms and genetic programming can acquire
                 computing power that is intermediate between the power
                 of currently available workstations and that of
                 supercomputers at a cost that is intermediate between
                 the two. This approach is illustrated by a comparison
                 of the computational effort required to solve the
                 problem of symbolic regression of the Boolean
                 even-5-parity function with different migration rates.
                 Genetic programming required the least computational
                 effort with an 5% migration rate. Moreover, this
                 computational effort was less than that required for
                 solving the problem with a serial computer and a
                 panmictic population of the same size. That is, apart
                 from the nearly linear speed-up in executing a fixed
                 amount of code inherent in the parallel implementation
                 of genetic programming, the use of distributed
                 sub-populations with only limited migration delivered
                 more than linear speed-up in solving the problem.",
}

@InProceedings{andre:1996:camc,
  author =       "David Andre and Forrest H {Bennett III} and John R.
                 Koza",
  title =        "Discovery by Genetic Programming of a Cellular
                 Automata Rule that is Better than any Known Rule for
                 the Majority Classification Problem",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "3--11",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/gp96.gkl.ps",
  size =         "9 pages",
  abstract =     "It is difficult to program cellular automata. This is
                 especially true when the desired computation requires
                 global communication and global integration of
                 information across great distances in the cellular
                 space. Various human- written algorithms have appeared
                 in the past two decades for the vexatious majority
                 classification task for one-dimensional two-state
                 cellular automata. This paper describes how genetic
                 programming with automatically defined functions
                 evolved a rule for this task with an accuracy of
                 82.326%. This level of accuracy exceeds that of the
                 original 1978 Gacs-Kurdyumov-Levin (GKL) rule, all
                 other known human-written rules, and all other known
                 rules produced by automated methods. The rule evolved
                 by genetic programming is qualitatively different from
                 all previous rules in that it employs a larger and more
                 intricate repertoire of domains and particles to
                 represent and communicate information across the
                 cellular space.",
  notes =        "GP-96",
}

@InProceedings{andre:1996:introns,
  author =       "David Andre and Astro Teller",
  title =        "A Study in Program Response and the Negative Effects
                 of Introns in Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "12--20",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AndreTeller.ps",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/TellerGP96/TellerGP96.html",
  size =         "9 pages",
  abstract =     "The standard method of obtaining a response in
                 tree-based genetic programming is to take the value
                 returned by the root node. In non-tree representations,
                 alternate methods have been explored. One alternative
                 is to treat a specific location in indexed memory as
                 the response value when the program terminates. The
                 purpose of this paper is to explore the applicability
                 of this technique to tree-structured programs and to
                 explore the intron effects that these studies bring to
                 light. This paper's experimental results support the
                 finding that this memory-based program response
                 technique is an improvement for some, but not all,
                 problems. In addition, this paper's experimental
                 results support the finding that, contrary to past
                 research and speculation, the addition or even
                 facilitation of introns can seriously degrade the
                 search performance of genetic programming.",
  notes =        "GP-96 html version available from
                 http://www.cs.cmu.edu/~astro/",
}

@InProceedings{andre:1996:parGP,
  author =       "David Andre and John R. Koza",
  title =        "A parallel implementation of genetic programming that
                 achieves super-linear performance",
  booktitle =    "Proceedings of the International Conference on
                 Parallel and Distributed Processing Techniques and
                 Applications",
  year =         "1996",
  editor =       "Hamid R. Arabnia",
  volume =       "III",
  pages =        "1163--1174",
  address =      "Sunnyvale",
  month =        "9-11 " # aug,
  publisher =    "CSREA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/pdptap96.ps",
  abstract =     "This paper describes the successful parallel
                 implementation of genetic programming on a network of
                 processing nodes using the transputer architecture.
                 With this approach, researchers of genetic algorithms
                 and genetic programming can acquire computing power
                 that is intermediate between the power of currently
                 available workstations and that of supercomputers at
                 intermediate cost. This approach is illustrated by a
                 comparison of the computational effort required to
                 solve a benchmark problem. Because of the decoupled
                 character of genetic programming, our approach achieved
                 a nearly linear speed up from parallelization. In
                 addition, for the best choice of parameters tested, the
                 use of subpopulations delivered a super linear speed-up
                 in terms of the ability of the algorithm to solve the
                 problem. Several examples are also presented where the
                 parallel genetic programming system evolved solutions
                 that are competitive with human performance on the same
                 problem.",
  notes =        "Awarded Best Paper Award PDPTA'96",
}

@InCollection{andre:1997:HEC,
  author =       "David Andre",
  title =        "Learning and Upgrading Rules for an Optical Character
                 Recognition System Using Genetic Programming",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and D. B. Fogel and Z. Michalewicz",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  notes =        "invited chapter,
                 http://bookmark.iop.org/bookpge.htm?&book=386h#top",
  size =         "pages",
}

@Misc{andre:cs267,
  author =       "David Andre",
  title =        "Multi-level parallelism in automatically synthesizing
                 soccer-playing programs for Robocup using genetic
                 programming",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.cs.berkeley.edu/~dandre/cs267/final/cs267_final.ps",
  URL =          "http://www.cs.berkeley.edu/~dandre/cs267/final/project_final.htm",
  size =         "18 pages",
  abstract =     "Many of the various proposals for tomorrow's
                 supercomputers have included clusters of
                 multiprocessors as an essential component. However,
                 when designing the systems of the future, it is
                 important to insure that the nature of the parallelism
                 provided matches up with some relevant and important
                 set of algorithms. This project presents empirical
                 program synthesis as an algorithm that can successfully
                 exploit the multiple levels of interconnect present in
                 an multi-SMP cluster system. When applying program
                 synthesis techniques to difficult problems, it is often
                 the case that two distinct levels of parallelism will
                 emerge. First, many example programs must be tested --
                 and can often be tested in parallel. This matches up
                 with the {"}slow{"} interconnect on a clump-based
                 system. Second, the execution of a particular program
                 can often be parallelized, especially if the program is
                 complicated or requires interactions with a complex
                 simulation. This level of parallelism, in contrast to
                 the first, often requires fine-grained communication.
                 Thus, this matches up with the {"}fast{"} level of the
                 clump-based system.

                 In particular, this project presents a multi-level
                 parallel system for the automatic program synthesis of
                 soccer-playing agents for the Robocup simulator
                 competition using genetic programming. The system
                 utilizes both the fast shared-memory communication of
                 the SMP system as well as a much slower mechanism for
                 the inter-SMP communication. The system is benchmarked
                 on a variety of configurations, and speedup curves are
                 presented. Additionally, a simple LogP analysis
                 comparing the performance of the designed system with a
                 single-processor based NOW system is presented.
                 Finally, the Robocup project is reviewed and the future
                 work outlined.",
  notes =        "my ghostview (Jan 2002) barfs at cs267_final.ps but it
                 prints",
}

@Article{AK97,
  author =       "David Andre and John R. Koza",
  title =        "A parallel implementation of genetic programming that
                 achieves super-linear performance",
  journal =      "Information Sciences",
  year =         "1998",
  volume =       "106",
  number =       "3-4",
  pages =        "201--218",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0020-0255",
  URL =          "http://www.cs.berkeley.edu/~dandre/papers/isj97.ps",
  size =         "18 pages",
  abstract =     "This paper describes the successful parallel
                 implementation of genetic programming on a network of
                 processing nodes using the transputer architecture.
                 With this approach, researchers of genetic algorithms
                 and genetic programming can acquire computing power
                 that is intermediate between the power of currently
                 available workstations and that of supercomputers at
                 intermediate cost. This approach is illustrated by a
                 comparison of the computational effort required to
                 solve a benchmark problem. Because of the decoupled
                 character of genetic programming, our approach achieved
                 a nearly linear speed up from parallelization. In
                 addition, for the best choice of parameters tested, the
                 use of subpopulations delivered a <i>super-linear</i>
                 speed-up in terms of the ability of the algorithm to
                 solve the problem. Several examples are also presented
                 where the parallel genetic programming system evolved
                 solutions that are competitive with human
                 performance.",
  notes =        "Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}

@InProceedings{andre:1998:tdcGPmdk,
  author =       "David Andre and Forrest H {Bennett III} and John Koza
                 and Martin A. Keane",
  title =        "On the Theory of Designing Circuits using Genetic
                 Programming and a Minimum of Domain Knowledge",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "130--135",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c023.pdf",
  size =         "6 pages",
  abstract =     "The problem of analog circuit design is a difficult
                 problem that is generally viewed as requiring human
                 intelligence to solve. Considerable progress has been
                 made in automating the design of certain categories of
                 purely digital circuits; however, the design of analog
                 electrical circuits and mixed analog-digital circuits
                 has not proved to be as amenable to automation. When
                 critical analog circuits are required for a project,
                 skilled and highly trained experts are necessary.
                 Previous work on applying genetic programming to the
                 design of analog circuits has proved to be successful
                 at evolving a wide variety of circuits, including
                 filters, amplifiers, and computational circuits;
                 however, previous approaches have required the
                 specification of an appropriate embryonic circuit. This
                 paper explores a method to eliminate even this small
                 amount of problem specific knowledge, and, in addition,
                 proves that the representation used is capable of
                 producing all circuits.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@InProceedings{Andre:1999:ETD,
  author =       "D. Andre and A. Teller",
  title =        "Evolving {Team Darwin United}",
  booktitle =    "RoboCup-98: Robot Soccer World Cup II",
  year =         "1999",
  editor =       "M. Asada and H. Kitano",
  volume =       "1604",
  series =       "LNCS",
  pages =        "346--351",
  address =      "Paris, France",
  month =        jul # " 1998",
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-66320-7",
  ISSN =         "0302-9743",
  bibdate =      "Mon Sep 13 16:57:02 MDT 1999",
  acknowledgement = ack-nhfb,
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Teller_Astro.ps",
  size =         "7 pages",
  abstract =     "The RoboCup simulator competition is one of the most
                 challenging international proving grounds for
                 contemporary AI research. Exactly because of the high
                 level of complexity and a lack of reliable strategic
                 guidelines, the pervasive attitude has been that the
                 problem can most successfully be attacked by human
                 expertise, possibly assisted by some level of machine
                 learning. This led, in RoboCup'97, to a field of
                 simulator teams all of whose level and style of play
                 were heavily influenced by the human designers of those
                 teams. It is the thesis of our work that machine
                 learning, if given the opportunity to design (learn)
                 ``everything'' about how the simulator team operates,
                 can develop a competitive simulator team that solves
                 the problem utilizing highly successful, if largely
                 non- human, styles of play. To this end, Darwin United
                 is a team of eleven players that have been evolved as a
                 team of coordinated agents in the RoboCup simulator.
                 Each agent is given a subset of the lowest level
                 perceptual inputs and must learn to execute series of
                 the most basic actions (turn, kick, dash) in order to
                 participate as a member of the team. This paper
                 presents our motivation, our approach, and the specific
                 construction of our team that created itself from
                 scratch.",
  notes =        "LNCS 1604
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66320-7

                 READ and WRITE functions, ie memory, 8 programs control
                 the 11 players however these 8 can use 8 shared ADFs",
}

@InCollection{kinnear:andrews,
  author =       "Martin Andrews and Richard Prager",
  title =        "Genetic Programming for the Acquisition of Double
                 Auction Market Strategies",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "16",
  size =         "14 pages",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "355--368",
  notes =        "{"} a GP approach was very successful in learning
                 strategies for playing a simple game with complex
                 dynamics{"} Ref Knobeln Contest:
                 Sanfrancisco.ira.uka.de [129.13.13.110]
                 /pub/knobeln

                 Generational GP pop=300, touranment selection? size=2?
                 Comparison with Simulated Annealing:SA also good but GP
                 better Best GP exceeded performance of handcode
                 routines (on average?) 65% of time. Check details of
                 what exctly this means.

                 Set number of games played so could distinquish meadian
                 from top quartile with 95% confidence. Claims it helps,
                 but doesnt seem to have either speeded things at lot or
                 made much better result.

                 ",
}

@PhdThesis{angeline:dissertation,
  author =       "Peter John Angeline",
  title =        "Evolutionary Algorithms and Emergent Intelligence",
  school =       "Ohio State University",
  year =         "1993",
  size =         "180 pages",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://nervous.cis.ohio-state.edu/pub/papers/DISS/pja",
  notes =        "prints mainly ok but three pages lost from chapter 8",
}

@InCollection{kinnear:angeline,
  title =        "Genetic Programming and Emergent Intelligence",
  author =       "Peter John Angeline",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "75--98",
  chapter =      "4",
  size =         "23 pages",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.natural-selection.com/people/pja/docs/aigp.ps.Z",
  notes =        "{"}Contrasts GP with other Weak/strong AI methods,
                 credit assignment, USEFUL, diplodity=redundancy=good,
                 hierarchical code/decode of subroutines better than
                 Koza ADF Loads of references{"}

                 I realized that inherent dynamics of genetic
                 programming encouraged certain emergent properties. The
                 most important of these is that introns emerge
                 naturally from the process to protect the developing
                 program from crossover. Others in the field think this
                 extra stuff in the genetic program is a bad thing,
                 reflected by their choice of the term {"}bloating{"}
                 for the effect. This chapter is the first to take a
                 positive view on GP introns and other emergent
                 phenomena. I think this is the first paper to associate
                 the {"}extra{"} code in genetic programs with the
                 intron concept.",
}

@InProceedings{icga93:angeline,
  author =       "Peter J. Angeline and Jordan B. Pollack",
  title =        "Competitive Environments Evolve Better Solutions for
                 Complex Tasks",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  pages =        "264--270",
  month =        "17-21 " # jul,
  address =      "University of Illinois at Urbana-Champaign",
  publisher_address = "2929 Campus Drive, Suite 260, San Mateo, CA
                 94403, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ttp://www.natural-selection.com/people/pja/icga93.ps.Z",
  size =         "7 pages",
  ISBN =         "1-55860-299-2",
  notes =        "very like thesis

                 One method I investigated was called competitive
                 fitness functions which is a fitness function that
                 compares performance between members of the population
                 to determine a ranking of individuals for reproduction.
                 THis obviates the need for a quantitative model of the
                 quality of solutions and replaces it with a more
                 simplistic measure of {"}x is better than y{"}. The
                 paper explores this concept using GLiB and appeared in
                 ICGA93.",
}

@InProceedings{Angeline:1994:GPCS,
  author =       "P. J. Angeline",
  title =        "Genetic programming: {A} current snapshot",
  booktitle =    "Proceedings of the Third Annual Conference on
                 Evolutionary Programming",
  year =         "1994",
  editor =       "D. B. Fogel and W. Atmar",
  publisher =    "Evolutionary Programming Society",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.natural-selection.com/people/pja/docs/ep94-gp.ps.Z",
}

@InProceedings{Angeline:1992:EIS,
  author =       "P. J. Angeline and J. B. Pollack",
  title =        "The evolutionary induction of subroutines",
  booktitle =    "Proceedings of the Fourteenth Annual Conference of the
                 Cognitive Science Society",
  year =         "1992",
  address =      "Bloomington, Indiana, USA",
  publisher =    "Lawrence Erlbaum",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.natural-selection.com/people/pja/docs/cogsci92.ps.Z",
  notes =        "GLiB is an emergent method for discovering
                 task-specific modular decompositions in genetic
                 programs. At least this is how I used to talk about it.
                 I know consider this an individual-level self-adaptive
                 method for forming decompositions in genetic
                 programs.",
}

@TechReport{Angeline:1993:CHLR,
  author =       "P. J. Angeline and J. B. Pollack",
  title =        "Coevolving High-Level Representations.",
  institution =  "Laboratory for Artificial Intelligence. The Ohio State
                 University",
  year =         "1993",
  type =         "July",
  number =       "Technical report 92-PA-COEVOLVE",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{angeline:1993:ema,
  author =       "P. J. Angeline and J. B. Pollack",
  title =        "Evolutionary Module Acquisition",
  booktitle =    "Proceedings of the Second Annual Conference on
                 Evolutionary Programming",
  year =         "1993",
  editor =       "D. Fogel and W. Atmar",
  pages =        "154--163",
  address =      "La Jolla, CA, USA",
  month =        "25-26 " # feb,
  organisation = "The Evolutionary Programming Society",
  keywords =     "genetic algorithms, genetic programming, FSM, GLiB",
  URL =          "http://www.natural-selection.com/people/pja/docs/ep93.ps.Z",
  size =         "9 pages",
  notes =        "Artificial Ant (John Muir). Finite State Machines.
                 Genetic Library Builder

                 ",
}

@InProceedings{Angeline:1991:CHLR,
  author =       "P. J. Angeline and J. B. Pollack",
  title =        "Coevolving high-level representations",
  booktitle =    "Artificial Life III",
  year =         "1994",
  editor =       "Christopher G. Langton",
  volume =       "XVII",
  series =       "SFI Studies in the Sciences of Complexity",
  pages =        "55--71",
  address =      "Santa Fe, New Mexico",
  month =        "15-19 " # jun # " 1992",
  publisher =    "Addison-Wesley",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.natural-selection.com/people/pja/docs/alife3.ps.Z",
  notes =        "ALife3 Held June 1992 in Santa Fe, New Mexico, USA
                 GLiB, Tower of Hanoi, Tic Tac Toe. Also in thesis.",
}

@Article{angeline:1995:er,
  author =       "Peter J. Angeline",
  title =        "Evolution Revolution: An Introduction to the Special
                 Track on Genetic and Evolutionary Programming",
  journal =      "IEEE Expert",
  year =         "1995",
  volume =       "10",
  number =       "3",
  pages =        "6--10",
  month =        jun,
  note =         "Guest editor's introduction",
  keywords =     "genetic algorithms, genetic programming",
  size =         "4 pages",
  notes =        "fab colour picture by Karl Sims 6 articles in special
                 track; 2 use evolutionary programming, 2 use genetic
                 programming (Tackett:1995:mGP and wong:1995:glp) and 2
                 use hybrids ((GA and GP howard:1995:GA-P) and (Riziki
                 and Zmuda, August 1995 GA and EP morphological pattern
                 recognition))",
}

@InProceedings{angeline:1995:mcc,
  author =       "P. J. Angeline",
  title =        "Morphogenic Evolutionary Computations: Introduction,
                 Issues and Examples",
  booktitle =    "Evolutionary Programming IV: The Fourth Annual
                 Conference on Evolutionary Programming",
  year =         "1995",
  editor =       "John Robert McDonnell and Robert G. Reynolds and David
                 B. Fogel",
  pages =        "387--401",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-13317-2",
  URL =          "http://www.natural-selection.com/people/pja/docs/ep95-morph.ps.Z",
  size =         "16 pages",
  notes =        "EP-95",
}

@InCollection{angeline:1995:asa,
  author =       "Peter J. Angeline",
  title =        "Adaptive and Self-Adaptive Evolutionary Computations",
  booktitle =    "Computational Intelligence: A Dynamic Systems
                 Perspective",
  publisher =    "IEEE Press",
  year =         "1995",
  editor =       "Marimuthu Palaniswami and Yianni Attikiouzel",
  pages =        "152--163",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.natural-selection.com/people/pja/docs/icec95.ps.Z",
  size =         "13 pages",
}

@Book{book:1996:aigp2,
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  title =        "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "http://www.cs.bham.ac.uk/~wbl/aigp2.html",
  URL =          "http://mitpress.mit.edu/book-home.tcl?isbn=0262011581",
  size =         "538 pages",
}

@InCollection{intro:1996:aigp2,
  author =       "Peter J. Angeline",
  title =        "Genetic Programming's Continued Evolution",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "1--20",
  chapter =      "1",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
}

@InCollection{angeline:1996:aigp2,
  author =       "Peter J. Angeline",
  title =        "Two Self-Adaptive Crossover Operators for Genetic
                 Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "89--110",
  chapter =      "5",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "http://www.natural-selection.com/people/pja/docs/aigp2.ps.Z",
  notes =        "THese were called Selective Self-Adaptive Crossover
                 and Self-adaptive Multi-Crossover.",
}

@InProceedings{angeline:1996:leaf,
  author =       "Peter J. Angeline",
  title =        "An Investigation into the Sensitivity of Genetic
                 Programming to the Frequency of Leaf Selection During
                 Subtree Crossover",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "21--29",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.natural-selection.com/people/pja/docs/gp96.zip",
  size =         "9 pages",
  notes =        "GP-96 multiple types of mutation

                 Sunspot Numbers data from
                 http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html",
}

@InProceedings{angeline:1996:efm,
  author =       "Peter J. Angeline",
  title =        "Evolving Fractal Movies",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Evolutionary Programming",
  pages =        "503--511",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 EP paper",
}

@InProceedings{angeline:1997:tcbbe,
  author =       "Peter J. Angeline",
  title =        "Subtree Crossover: Building Block Engine or
                 Macromutation?",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "9--17",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www.natural-selection.com/people/pja/docs/gp97a.zip",
  size =         "pages",
  notes =        "GP-97",
}

@InProceedings{Angeline:1997:aIMepesr,
  author =       "Peter J. Angeline",
  title =        "An Alternative to Indexed Memory for Evolving Programs
                 with Explicit State Representations",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "evolutionary programming and evolution strategies",
  pages =        "423--430",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{angeline:1997:txde,
  author =       "Peter J. Angeline",
  title =        "Tracking Extrema in Dynamic Environments",
  booktitle =    "Proceedings of the 6th International Conference on
                 Evolutionary Programming",
  year =         "1997",
  editor =       "P. J. Angeline and R. G. Reynolds and J. R. McDonnell
                 and R. Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  address =      "Indianapolis, Indiana, USA",
  month =        apr # " 13-16",
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-62788-X",
  URL =          "http://www.natural-selection.com/people/pja/docs/ep97b.pdf",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-62788-X",
  size =         "11 pages",
  abstract =     "Typical applications of evolutionary optimization
                 involve the off-line approximation of extrema of static
                 multi-modal functions. Methods which use a variety of
                 techniques to self-adapt mutation parameters have been
                 shown to be more successful than methods which do not
                 use self-adaptation. For dynamic functions, the
                 interest is not to obtain the extrema but to follow it
                 as closely as possible. This paper compares the on-line
                 extrema tracking performance of an evolutionary program
                 without self-adaptation against an evolutionary program
                 using a self-adaptive Gaussian update rule over a
                 number of dynamics applied to a simple static function.
                 The experiments demonstrate that for some dynamic
                 functions, self-adaptation is effective while for
                 others it is detrimental.",
  notes =        "EP-97",
}

@InProceedings{angeline:1997:spie,
  author =       "Peter J. Angeline a and David B. Fogel",
  title =        "An evolutionary program for the identification of
                 dynamical systems",
  booktitle =    "Application and Science of Artificial Neural Networks
                 III",
  year =         "1997",
  editor =       "S. Rogers",
  volume =       "3077",
  pages =        "409--417",
  publisher_address = "Bellingham, WA, USA",
  organisation = "SPIE-The International Society for Optical
                 Engineering",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, evolutionary programming, system
                 identification, dynamical systems, optimization",
  URL =          "http://www.natural-selection.com/people/pja/docs/spie97.pdf",
  size =         "9 pages",
  abstract =     "Various forms of neural networks have been applied to
                 the identification of non-linear dynamical systems. In
                 most of these methods, the network architecture is set
                 prior to training. In this paper, a method that evolves
                 a symbolic solution for plant models is described. This
                 method uses an evolutionary program to manipulate
                 collections of parse trees expressed in a task specific
                 language. Experiments performed on two unknown plants
                 show this method is competitive with those that train
                 neural networks for similar problems",
}

@InProceedings{angeline:1998:sccb,
  author =       "Peter J. Angeline",
  title =        "Subtree Crossover Causes Bloat",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "745--752",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 programming",
  ISBN =         "1-55860-548-7",
  size =         "9 pages",
  notes =        "GP-98, Even-5 parity, intertwined spirals, sunspot
                 prediction.

                 ",
}

@Article{angeline:1998:hpees,
  author =       "Peter J. Angeline",
  title =        "A Historical Perspective on the Evolution of
                 Executable Structures",
  journal =      "Fundamenta Informaticae",
  year =         "1998",
  volume =       "35",
  number =       "1--4",
  pages =        "179--195",
  month =        aug,
  email =        "angeline@natural-selection.com",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0169-2968",
  URL =          "http://www.natural-selection.com/people/pja/gphist.pdf",
  size =         "16 pages",
  abstract =     "Genetic programming (Koza 1992) is a method of
                 inducing behaviors represented as executable programs.
                 The generality of the approach has spawned a
                 proliferation of work in the evolution of executable
                 structures that is unmatched in the history of the
                 subject. This paper describes the standard approach to
                 genetic programming, as defined in Koza (1992), and
                 then presents the significant studies that preceded its
                 inception as well as the diversification of techniques
                 evolving executable structures that is currently
                 underway in the field.",
  notes =        "Special volume: Evolutionary Computation

                 Also published in book form, see angeline:1999:hpees",
}

@Article{angeline:1998:mips3,
  author =       "Peter J. Angeline",
  title =        "Multiple Interacting Programs: {A} Representation for
                 Evolving Complex Behaviors",
  journal =      "Cybernetics and Systems",
  year =         "1998",
  volume =       "29",
  number =       "8",
  pages =        "779--806",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, mips",
  ISSN =         "0196-9722",
  URL =          "http://www.natural-selection.com/people/pja/docs/mips3.pdf",
  URL =          "http://www.tandf.co.uk/journals/frameloader.html?http://www.tandf.co.uk/journals/tf/01969722.html",
  size =         "31 pages",
  abstract =     "This paper defines a representation for expressing
                 complex behaviors, called multiple interacting programs
                 (MIPs), and describes an evolutionary method for
                 evolving solutions to difficult problems expressed as
                 MIPs structures. The MIPs representation is a
                 generalization of neural network architectures that can
                 model any type of dynamic system. The evolutionary
                 training method described is based on an evolutionary
                 program originally used to evolve the architecture and
                 weights of recurrent neural networks. Example
                 experiments demonstrate the training method s ability
                 to evolve appropriate MIPs solutions for difficult
                 problems. An analysis of the evolved solutions shows
                 their dynamics to be interesting and non-trivial.",
}

@InProceedings{angeline:1998:spie,
  author =       "Peter J. Angeline",
  title =        "Evolving Predictors for Chaotic Time Series",
  booktitle =    "Proceedings of SPIE: Application and Science of
                 Computational Intelligence",
  year =         "1998",
  editor =       "S. Rogers and D. Fogel and J. Bezdek and B. Bosacchi",
  volume =       "3390",
  pages =        "170--80",
  publisher_address = "Bellingham, WA, USA",
  organisation = "SPIE",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, evolutionary programming, neural networks,
                 chaotic time series prediction",
  URL =          "http://www.natural-selection.com/people/pja/docs/spie98.pdf",
  size =         "11 pages",
  abstract =     "Neural networks are a popular representation for
                 inducing single-step predictors for chaotic times
                 series. For complex time series it is often the case
                 that a large number of hidden units must be used to
                 reliably acquire appropriate predictors. This paper
                 describes an evolutionary method that evolves a class
                 of dynamic systems with a form similar to neural
                 networks but requiring fewer computational units.
                 Results for experiments on two popular chaotic times
                 series are described and the current methods
                 performance is shown to compare favorably with using
                 larger neural networks.",
}

@InCollection{angeline:1999:hpees,
  author =       "Peter J. Angeline",
  title =        "A Historical Perspective on the Evolution of
                 Executable Structures",
  booktitle =    "Evolutionary Computation",
  publisher =    "Ohmsha",
  year =         "1999",
  editor =       "A. E. Eiben and A. Michalewicz",
  address =      "Tokyo",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "4-274-90269-2",
  URL =          "http://www.ohmsha.co.jp/data/books/e_contents/4-274-90269-2.htm",
  notes =        "This is the book edition of the journal, Fundamenta
                 Informaticae, Volume 35, Nos. 1-4, 1998. See also
                 angeline:1998:hpees

                 ",
  size =         "pages",
}

@InProceedings{aparicio:1999:PM,
  author =       "Joaquim N. Aparicio and Luis Correia and Fernando
                 Moura-Pires",
  title =        "Populations are Multisets-{PLATO}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1845--1850",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "methodology, pedagogy and philosophy",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{ardell:1994:TOPE,
  author =       "David H. Ardell",
  title =        "{TOPE} and Magic Squares: {A} Simple {GA} Approach to
                 Combinatorial Optimization",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "1--6",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-187263-3",
  notes =        "Uses Genesis

                 This volume contains 20 papers written and submitted by
                 students describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{Arita:1997:hamilton,
  author =       "Masanori Arita and Akira Suyama and Masami Hagiya",
  title =        "A Heuristic Approach for Hamiltonian Path Problem with
                 Molecules",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "DNA Computing",
  pages =        "457--462",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@Article{Arkov:2000:ARC,
  author =       "V. Arkov and C. Evans and P. J. Fleming and D. C. Hill
                 and J. P. Norton and I. Pratt and D. Rees and K.
                 Rodriguez-Vazquez",
  title =        "System Identification Strategies Applied to Aircraft
                 Gas Turbine Engines",
  journal =      "Annual Reviews in Control",
  volume =       "24",
  pages =        "67--81",
  year =         "2000",
  number =       "1",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "A variety of system identification techniques are
                 applied to the derivation of models of aircraft gas
                 turbine dynamics. The motivation behind the study is to
                 improve the efficiency and cost-effectiveness of system
                 identification techniques currently used in the
                 industry. Four system identification approaches are
                 outlined in this paper. They are based upon:
                 identification using ambient noise only data, multisine
                 testing and frequency-domain identification,
                 time-varying models estimated using extended least
                 squares with optimal smoothing, and multiobjective
                 genetic programming to select model structure.",
}

@InProceedings{ashlock:1997:GPdd,
  author =       "Dan Ashlock",
  title =        "{GP}-Automata for Dividing the Dollar",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "18--26",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{ashlock:1997:spbs,
  author =       "Dan Ashlock and Charles Richter",
  title =        "The Effect of Splitting Populations on Bidding
                 Strategies",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "27--34",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{ashlock:1998:fctsGP,
  author =       "Dan Ashlock and James I. Lathrop",
  title =        "A Fully Characterized Test Suite for Genetic
                 Programming",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "537--546",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 Iowa State University.",
}

@InProceedings{ashlock:1998:ISAc,
  author =       "Dan Ashlock and Mark Joenks",
  title =        "{ISA}c Lists, {A} Different Representation for Program
                 Induction",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "3--10",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{Atkin:1993:GPLAMS,
  author =       "M. Atkin and P. R. Cohen",
  title =        "Genetic programming to learn an agent's monitoring
                 strategy",
  booktitle =    "Proceedings of the AAAI-93 Workshop on Learning Action
                 Models",
  year =         "1993",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Also available as Atkin:1993:GPLAMSa?",
}

@TechReport{Atkin:1993:GPLAMSa,
  author =       "M. Atkin and P. R. Cohen",
  title =        "Genetic programming to learn an agent's monitoring
                 strategy",
  institution =  "Computer Science Department, University of
                 Massachusetts",
  year =         "1993",
  type =         "Technical report",
  number =       "TR-93-26",
  address =      "Amherst, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Also available as Atkin:1993:GPLAMS?",
}

@InProceedings{Atkin:1994:LMSDGP,
  author =       "Marc S. Atkin and Paul R. Cohen",
  title =        "Learning monitoring strategies: {A} difficult genetic
                 programming application",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "328--332a",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, cupcake
                 problem",
  URL =          "http://eksl-www.cs.umass.edu/papers/IEEE.ps",
  notes =        "Novel? chrome/program structure linear, close to
                 assembly lanuage, used GOTOs and interrupt handlers.
                 Did _not_ get performance improvement on changing to
                 parse trees. Did evolve progs to control agents which
                 moved to the goal without colliding with an obstacle.
                 Finally cautions about problems with GP scaling
                 up.

                 {"}Also tried local mating (also known as fine grain
                 parallelism){"}

                 Also available as Technical Report 94-52, Dept. of
                 Computer Science, University of Massachusetts/Amherst,
                 USA?",
}

@TechReport{atkin:1995:mea,
  author =       "Marc S. Atkin and Paul R. Cohen",
  title =        "Monitoring in Embedded Agents",
  institution =  "Experimental Knowledge Systems Laboratory, Computer
                 Science Department, University of Massachusetts",
  year =         "1995",
  type =         "Computer Science Technical Report",
  number =       "95-66",
  address =      "Box 34610, Lederle Graduate Research Center, Amherst.
                 MA 01003-4610, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://eksl-www.cs.umass.edu/ijcai95-msa_95-66.ps",
  notes =        "refs to Atkin's Masters Thesis. Simulated robot in 2
                 dee world, sensors, conditionals, loop. LTB, explains
                 what the cupcake problem is. interrupt handlers.
                 Theoretical justification for cupcake result.",
  size =         "11 pages",
}

@InProceedings{atkinson-abutridy:1999:A,
  author =       "John A. Atkinson-Abutridy and Julio R. Carrasco-Leon",
  title =        "An evolutionary model for dynamically controlling a
                 behavior-based autonomous agent",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "16--24",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{atlan:1994:gpjss,
  author =       "Laurent Atlan and Jerome Bonnet and Martine Naillon",
  title =        "Learning Distributed Reactive Strategies by Genetic
                 Programming for the General Job Shop Problem",
  booktitle =    "Proceedings of the 7th annual Florida Artificial
                 Intelligence Research Symposium",
  year =         "1994",
  address =      "Pensacola, Florida, USA",
  month =        may,
  organisation = "Dassault-Aviation, Artificial Intelligence
                 Department",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.ens.fr/pub/reports/bioligie/disgajsp.ps.Z",
  size =         "11 pages",
  abstract =     "proposed is a general system to infer symbolic policy
                 functions for distributed reactive scheduling in
                 non-stationary environments. The job shop problem is
                 only used as a validating case study. Our system is
                 based both on an original distributed scheduling model
                 and on genetic programming for the inference of
                 symbolic policy functions. The purpose is to determine
                 heuristic policies that are local in time, long term
                 near-optimal, and robust with respect to perturbations.
                 Furthermore, the policies are local in state space: the
                 global decision problem is split into as many decision
                 problems as there are agents, i.e. machines in the job
                 shop problem. If desired, the genetic algorithm can use
                 expert knowledge as a priori knowledge, via
                 implementation of the symbolic representation of the
                 policy functions.",
  notes =        "{"}To be published in the proceedings of the Seventh
                 Annual Florida Artificial Intelligence Research
                 Symposium{"} DGT/DEA/IA2 December 1993

                 Combination of GP and Giffler and Thompson algorithm",
}

@InCollection{aytekin:1995:4-OPmap,
  author =       "Tevfik Aytekin and Emin Erkan Korkmaz and Halil Altay
                 G{\"{u}}vennir",
  title =        "An application of genetic programming to the 4-{OP}
                 problem using map-trees",
  booktitle =    "Progress in Evolutionary Computation",
  publisher =    "Springer-Verlag",
  year =         "1995",
  editor =       "X. Yao",
  volume =       "956",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "28--40",
  address =      "Heidelberg, Germany",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.lcsl.metu.edu.tr/~korkmaz/publication/BU-CEIS-9441.ps",
  notes =        "

                 ",
}

@InProceedings{azad:2002:gecco,
  author =       "R. Muhammad Atif Azad and Conor Ryan and Mark E. Burke
                 and Ali R. Ansari",
  title =        "A Re-examination Of The Cart Centering Problem Using
                 The Chorus System",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "707--715",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)

                 Nominated for best at GECCO award",
}

@InProceedings{azad:2002:gecco:workshop,
  title =        "A Position Independent Evolutionary Automatic
                 Programming Algorithm - The {Chorus} System",
  author =       "R. Muhammad Atif Azad",
  pages =        "260--263",
  booktitle =    "Graduate Student Workshop",
  editor =       "Sean Luke and Conor Ryan and Una-May O'Reilly",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{azam:1998:dsi:cs,
  author =       "Farooq Azam and H. F. VanLandingham",
  title =        "Dynamic Systems Identification: {A} Comparitive
                 Study",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{azam:1998:dsiGP,
  author =       "Farooq Azam and H. F. VanLandingham",
  title =        "Dynamic Systems Identification using Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{baber:2002:EuroGP,
  title =        "Evolutionary Algorithm Approach to Bilateral
                 Negotiations",
  author =       "Vinaysheel Baber and Rema Ananthanarayanan and Krishna
                 Kummamuru",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "202--211",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "The Internet is quickly changing the way
                 business-to-consumer and business-to-business commerce
                 is conducted. The technology has created an opportunity
                 to get beyond single-issue negotiation by determining
                 sellers' and buyers' preferences across multiple
                 issues, thereby creating possible joint gains for all
                 parties. We develop simple multiple issue algorithms
                 and heuristics that could be used in electronic
                 auctions and electronic markets. In this study, we show
                 how a genetic algorithm based technique, coupled with a
                 simple heuristic can achieve good results in business
                 negotiations. The negotiations' outcomes are evaluated
                 on two dimensions: joint utility and number of
                 ex-changes of offers to reach a deal. The results are
                 promising and indicate possible use of such approaches
                 in actual electronic commerce systems.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{babovic:1994:camh,
  author =       "Vladan Babovic and A. W. Minns",
  title =        "Use of computational adaptive methodologies in
                 hydroinformatics",
  booktitle =    "Proceedings of the first international conference on
                 hydroinformatics, Delft, Netherlands",
  year =         "1994",
  editor =       "A. Verwey and A. W. Minns and V. Babovic and C.
                 Maksimovic",
  pages =        "201--210",
  publisher_address = "P. O. Box 1675, Rotterdam, Netherlands",
  month =        "19--23 " # sep,
  publisher =    "A. A. Balkema",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "90-5410-512-7",
  abstract =     "Summaries a study of the performance of artificial
                 neural networks and GP compared to an empirically-based
                 method using a problem of salt intrusion as an
                 example.",
  notes =        "Does not present clear winner (ANN, GP or traditional)
                 upto reader to choose approriate to their
                 problem.

                 IHE-Delft, The Netherlands

                 ",
}

@InProceedings{babovic:1995:gmibed,
  author =       "Vladan Babovic",
  title =        "Genetic Model Induction Based on Experimental Data",
  booktitle =    "Proceedings of the XXVIth Congress of International
                 Association for Hydraulics Research",
  year =         "1995",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "GP used to perform an analysis of sediment transport
                 data and to induce relationshop between bed
                 concentration of suspended sediment and the hydraulic
                 conditions. GP results similar accuracy to traditional
                 techniques. IHE-Delft, The Netherlands",
  notes =        "To be held in London, 11--15 September 1995

                 ",
}

@PhdThesis{babovic:thesis,
  author =       "Vladan Babovic",
  title =        "Emergence, Evolution, Intelligence: Hydroinformatics",
  school =       "International Institute for Infrastructural, Hydraulic
                 and Environmental Engineering and Technical University
                 Delft",
  year =         "1996",
  address =      "The Netherlands",
  note =         "Published by A. A. Balkema Publishers",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "See also babovic:book",
}

@Book{babovic:book,
  author =       "Vladan Babovic",
  title =        "Emergence, evolution, intelligence; Hydroinformatics -
                 {A} study of distributed and decentralised computing
                 using intelligent agents",
  publisher =    "A. A. Balkema Publishers",
  year =         "1996",
  address =      "Rotterdam, Holland",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "90-5410-404-X",
  URL =          "http://www.jcn.nl/cgi-bin/www_new?a=1368;c",
  abstract =     "The computer controlled operating environments of such
                 facilities as automated factories, nuclear power
                 plants, telecommunication centres and space stations
                 are continually becoming more complex. The situation is
                 similar, if not even more apparent and urgent, in the
                 case of water. Water is not only mankind's most
                 valuable natural resource, but one which is in
                 increasingly limited supply. The fresh water is the
                 vital natural resource which supports all environmental
                 activities, that is, natural economy, and all human
                 socio-economic activities, that is, the artificial
                 economy. The pressure for a sustainable control and
                 exploration of water and thus for the peaceful
                 co-existence of human- & hydro-economies is not only a
                 human, socio-economic pressure, but it is the question
                 of life and death. Hydroinformatics - the nascent
                 technology concerned with the flow of information
                 related to the flow of fluids and all that they convey
                 - is probably the best possible answer yet proposed to
                 the problem of the control of the waters, the very
                 arteries and veins of the biosphere.",
  notes =        "publication of babovic:thesis",
  size =         "344 pages",
}

@InCollection{babovic:1996:wmbAI,
  author =       "V. Babovic",
  title =        "Can water resources management benefit from artificial
                 intelligence?",
  booktitle =    "Computation Fluid Dynamics: Bunte Bilder in der
                 Praxis",
  publisher =    "Meinz Verlag",
  year =         "1996",
  editor =       "J. Köngeter",
  pages =        "337--358",
  address =      "Aachen, Germany",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "26. IWASA International Wasserbau-Symposium Aachen
                 1995/96

                 ",
  size =         "pages",
}

@Article{babovic:1997:eehd1,
  author =       "Vladan Babovic and Michael B. Abbott",
  title =        "The evolution of equation from hydraulic data, Part
                 {I}: Theory",
  journal =      "Journal of Hydraulic Research",
  year =         "1997",
  volume =       "35",
  number =       "3",
  pages =        "397--410",
  keywords =     "genetic algorithms, genetic programming",
}

@Article{babovic:1997:eehd2,
  author =       "Vladan Babovic and Michael B. Abbott",
  title =        "The evolution of equation from hydraulic data, Part
                 {II}: Applications",
  journal =      "Journal of Hydraulic Research",
  year =         "1997",
  volume =       "35",
  number =       "3",
  pages =        "411--430",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{babovic:1997:mfnls,
  author =       "Vladan Babovic",
  title =        "On the Modelling and Forecasting of Non-linear
                 Systems",
  booktitle =    "Operational Water Management: Proceedings of the
                 European Water Resources Association Conference,
                 Copenhagen, Denmark, 3-6 September 1997",
  publisher =    "Balkema",
  year =         "1997",
  editor =       "J. C. Refsgaard and E. A. Karalis",
  pages =        "195--202",
  address =      "Rotterdam",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "90-5410-897-5",
  size =         "8 pages",
}

@InProceedings{babovic:1998:stdlkm,
  author =       "V. Babovic",
  title =        "Sediment transport data - Large knowledge mine",
  booktitle =    "Proceedings of the Third International Conference on
                 Hydroscience and Engineering",
  year =         "1998",
  address =      "Cottbus, Germany",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{babovic:1998:dmtsmf,
  author =       "V. Babovic",
  title =        "A data mining approach to time series modelling and
                 forecasting",
  booktitle =    "Proceeding of the Third International Conference on
                 Hydroinformatics",
  year =         "1998",
  editor =       "Babovic and Larsen",
  pages =        "847--856",
  address =      "Copenhagen, Denmark",
  publisher_address = "Rotterdam",
  publisher =    "Balkema",
  keywords =     "genetic algorithms, genetic programming, Vltava River
                 system, flood control and protection of Prague,
                 artificial neural networks",
  ISBN =         "90-5410-983-1",
  notes =        "Hydroinformatics'98",
}

@InProceedings{babovic:1998:mstGP,
  author =       "Vladan Babovic",
  title =        "Mining sediment transport data with genetic
                 programming",
  booktitle =    "Proceedings of the First International Conference on
                 New Information Technologies for Decision Making in
                 Civil Engineering",
  year =         "1998",
  pages =        "875--886",
  address =      "Montreal, Canada",
  month =        "11-13 " # oct,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{babovic:1999:cskd-veg,
  author =       "Vladan Babovic and Maarten Keijzer",
  title =        "Computer supported knowledge discovery - {A} case
                 study in flow resistance induced by vegetation",
  booktitle =    "Proceedings of the XXVIII Congress of International
                 Association for Hydraulic Research",
  year =         "1999",
  address =      "Graz, Austria",
  month =        "22-27 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  size =         "7 pages",
}

@InProceedings{babovic:1999:d2k,
  author =       "V. Babovic and M. Keijzer",
  title =        "Data to knowledge - The new scientific paradigm",
  booktitle =    "Water Industry Systems",
  year =         "1999",
  editor =       "D. Savic and G. Walters",
  pages =        "3--14",
  address =      "Exeter, United Kingdom",
  keywords =     "genetic algorithms, genetic programming",
}

@Article{babovic:1999:td2ksed,
  author =       "Vladan Babovic",
  title =        "Data Mining and Knowledge Discovery in Sediment
                 Transport",
  journal =      "Computer-Aided Civil and Infrastructure Engineering",
  year =         "2000",
  volume =       "15",
  number =       "5",
  pages =        "383--389",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1093-9687",
  URL =          "http://www.blackwellpublishers.co.uk/asp/journal.asp?ref=1093-9687&src=arc&vid=15&iid=5",
  abstract =     "The means for data collection have never been as
                 advanced as they are today. Moreover, the numerical
                 models we use today have never been so advanced.
                 Feeding and calibrating models against collected
                 measurements, however, represents only a one-way flow:
                 from measurements to the model. The observations of the
                 system can be analyzed further in the search for the
                 information they encode. Such automated search for
                 models accurately describing data constitutes a new
                 direction that can be identified as that of data
                 mining. It can be expected that in the years to come we
                 shall concentrate our efforts more and more on the
                 analysis of the data we acquire from natural or
                 artificial sources and that we shall mine for knowledge
                 from the data so acquired.

                 Data mining and knowledge discovery aim at providing
                 tools to facilitate the conversion of data into a
                 number of forms, such as equations, that provide a
                 better understanding of the process generating or
                 producing these data. These new models combined with
                 the already available understanding of the physical
                 processes&mdash;the theory&mdash;result in an improved
                 understanding and novel formulations of physical laws
                 and improved predictive capability.

                 This article describes the data mining process in
                 general, as well as an application of a data mining
                 technique in the domain of sediment transport. Data
                 related to the concentration of suspended sediment near
                 a bed are analyzed by the means of genetic programming.
                 Machine-induced relationships are compared against
                 formulations proposed by human experts and are
                 discussed in terms of accuracy and physical
                 interpretability.",
  size =         "pages",
}

@Article{babovic:1999:GPmie,
  author =       "Vladan Babovic and Maarten Keijzer",
  title =        "Genetic programming as a model induction engine",
  journal =      "Journal of Hydroinformatics",
  year =         "2000",
  volume =       "1",
  number =       "2",
  pages =        "35--60",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 knowledge discovery",
  notes =        "dimensionally aware GP. Additional river water flow
                 resistance caused by flexible vegetation",
}

@InCollection{bachman:2000:UGAVLGA,
  author =       "Brandon M. Bachman",
  title =        "Using the Genetic Algorithm with a Variable Length
                 Genome for Architectural",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "33--39",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{backer:1996:WSC,
  author =       "Gerriet Backer",
  title =        "Learning with missing data using Genetic Programming",
  booktitle =    "The 1st Online Workshop on Soft Computing (WSC1)",
  year =         "1996",
  address =      "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/",
  month =        "19--30 " # aug,
  organisation = "Research Group on ECOmp of the Society of Fuzzy Theory
                 and Systems (SOFT)",
  publisher =    "Nagoya University, Japan",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Missing data, Strongly Typed Genetic
                 Programming STGP",
  URL =          "http://www.psych.nat.tu-bs.de/psych/gb/wsc1/contents.htm",
  url_2 =        "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/files/backer.ps",
  URL =          "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/files/backer.ps.gz",
  abstract =     "Learning with imprecise or missing data has been a
                 major challenge for machine learning. A new approach
                 using Strongly Typed Genetic Programming is proposed
                 here, which uses extra computations based on other
                 input data to approximate the missing values. It
                 eliminates the need for pre-processing and makes use of
                 correlations between the input data. The decision
                 process itself and the handling of unknown data can be
                 extracted from the resulting program for an analysis
                 afterwards. Comparing it to an alternative approach on
                 a simple example shows the usefulness of this
                 approach.",
  size =         "5 pages",
  notes =        "Adds {"}unknown{"} data type to STGP. demo on iris
                 classification problem (see discussion on WSC1 pages)
                 email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp",
}

@InProceedings{bael:1999:TJSPSSBESE,
  author =       "Patrick Van Bael and Dirk Devogelaere and M.
                 Rijckaert",
  title =        "The Job Shop Problem Solved with Simple, Basic
                 Evolutionary Search Elements",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "665--669",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{baglioni:2000:eampaa,
  author =       "Stefania Baglioni and Celia da Costa Pereira and Dario
                 Sorbello and Andrea G. B. Tettamanzi",
  title =        "An Evolutionary Approach to Multiperiod Asset
                 Allocation",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "225--236",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "Portfolio construction can become a very complicated
                 problem, as regulatory constraints, individual
                 investor's requirements, non-trivial indices of risk
                 and subjective quality measures are taken into account,
                 together with multiple investment horizons and
                 cash-flow planning. This problem is approached using a
                 tree of possible scenarios for the future, and an
                 evolutionary algorithm is used to optimize an
                 investment plan against the desired criteria and the
                 possible scenarios. An application to a real defined
                 benefit pension fund case is discussed.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{bagnall:1999:UAABSMUME,
  author =       "A. J. Bagnall and G. D. Smith",
  title =        "Using an Adaptive Agent to Bid in a Simplified Model
                 of the {UK} Market in Electricity",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "774",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{balakrishnan:1996:ser,
  author =       "Karthik Balakrishnan and Vasant Honavar",
  title =        "On Sensor Evolution in Robotics",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "455--460",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 GA paper",
}

@InProceedings{Balakrishnan:1997:slrl,
  author =       "Karthik Balakrishnan and Vasant Honavar",
  title =        "Spatial Learning for Robot Localization",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Artifical life and evolutionary robotics",
  pages =        "389--397",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{balazs:1999:AE,
  author =       "Marton E. Balazs and Daniel L. Richter",
  title =        "A genetic algorithm with dynamic population:
                 Experimental results",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "25--30",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@Article{Baldwin:1999:IJAR,
  author =       "James F. Baldwin and Trevor P. Martin and James G.
                 Shanahan",
  title =        "Controlling with words using automatically identified
                 fuzzy Cartesian granule feature models",
  journal =      "International Journal of Approximate Reasoning",
  volume =       "22",
  pages =        "109--148",
  year =         "1999",
  number =       "1-2",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V07-3XWJVTP-K/1/fca9fc7ee54707e1f2ed9847e29c1b7e",
  abstract =     "We present a new approach to representing and
                 acquiring controllers based upon Cartesian granule
                 features - multidimensional features formed over the
                 cross product of words drawn from the linguistic
                 partitions of the constituent input features -
                 incorporated into additive models. Controllers
                 expressed in terms of Cartesian granule features enable
                 the paradigm {"}controlling with words{"} by
                 translating process data into words that are
                 subsequently used to interrogate a rule base, which
                 ultimately results in a control action. The system
                 identification of good, parsimonious additive Cartesian
                 granule feature models is an exponential search
                 problem. In this paper we present the G_DACG
                 constructive induction algorithm as a means of
                 automatically identifying additive Cartesian granule
                 feature models from example data. G_DACG combines the
                 powerful optimisation capabilities of genetic
                 programming with a novel and cheap fitness function,
                 which relies on the semantic separation of concepts
                 expressed in terms of Cartesian granule fuzzy sets, in
                 identifying these additive models. We illustrate the
                 approach on a variety of problems including the
                 modelling of a dynamical process and a chemical plant
                 controller.",
}

@Article{baluja:1994:taaecgi,
  author =       "Shumeet Baluja and Dean Pomerleau and Todd Jochem",
  title =        "Towards Automated Artificial Evolution for
                 Computer-generated Images",
  journal =      "Connection Science",
  year =         "1994",
  volume =       "6",
  number =       "2 and 3",
  pages =        "325--354",
  keywords =     "genetic algorithms, genetic programming, artificial
                 neural networks (ANN), simulated evolution, computer
                 graphics",
  abstract =     "

                 In 1991, Karl Sims presented work on artificial
                 evolution in which he used genetic algorithms to evolve
                 complex structures for use in computer generated images
                 and animations. The evolution of the computer generated
                 images progressed from simple, randomly generated
                 shapes to interesting images which the users
                 interactively created. The evolution advanced under the
                 constant guidance and supervision of the user. This
                 paper describes attempts to automate the process of
                 image evolution through the use of artificial neural
                 networks. The central objective of this study is to
                 learn the user's preferences, and to apply this
                 knowledge to evolve aesthetically pleasing images which
                 are similar to those evolved through interactive
                 sessions with the user. This paper presents a detailed
                 analysis of both the shortcomings and successes
                 encountered in the use of five artificial neural
                 network architectures. Further possibilities for
                 improving the performance of a fully automated system
                 are also discussed.",
  notes =        "also CMU techical report CMU//CS-93-198

                 ",
}

@TechReport{banzhaf:mrl:tech,
  author =       "Wolfgang Banzhaf",
  title =        "Genetic Programming for Pedestrians",
  institution =  "Mitsubishi Electric Research Labs",
  year =         "1993",
  type =         "MERL Technical Report",
  number =       "93-03",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/pedes93.ps.gz",
  abstract =     "We propose an extension to the Genetic Programming
                 paradigm which allows users of traditional Genetic
                 Algorithms to evolve computer programs. To this end, we
                 have to introduce mechanisms like transscription,
                 editing and repairing into Genetic Programming. We
                 demonstrate the feasibility of the approach by using it
                 to develop programs for the prediction of sequences of
                 integer numbers.",
  notes =        "As banzhaf:mrl",
}

@InProceedings{banzhaf:mrl,
  author =       "Wolfgang Banzhaf",
  title =        "Genetic Programming for Pedestrians",
  institution =  "Mitsubishi Electrical Research Laboratories, Cambridge
                 Research Center",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  pages =        "628",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/GenProg_forPed.ps.Z",
  abstract =     "We propose an extension to the Genetic Programming
                 paradigm which allows users of traditional Genetic
                 Algorithms to evolve computer programs. To this end, we
                 have to introduce mechanisms like transcription,
                 editing and repairing into Genetic Programming. We
                 demonstrate the feasibility of the approach by using it
                 to develop programs for the prediction of sequences of
                 integer numbers.",
  notes =        "Also available as MRL Technical Report 93-03 11 pages.
                 (banzhaf:mrl:tech)

                 225 bit GA, 5 bit grouping encode terminal or two arg
                 function, clean up by {"}editing{"} and {"}repair{"} to
                 produce variable length tree shaped prog. No looping,
                 recursion or memory. Demonstrated on learning sequences
                 of small integers, fails on primes.

                 ",
}

@InProceedings{banzhaf:1994:ppsn3,
  author =       "Wolfgang Banzhaf",
  title =        "Genotype-Phenotype-Mapping and Neutral Variation --
                 {A} case study in Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and Reinhard
                 M{\"a}nner",
  series =       "LNCS",
  volume =       "866",
  pages =        "322--332",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58484-6",
  URL =          "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/ppsn94.ps.gz",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
  abstract =     "We propose the application of a genotype-phenotype
                 mapping to the solution of constrained optimization
                 problems. The method consists of strictly separating
                 the search space of genotypes from the solution space
                 of phenotypes. A mapping from genotypes into phenotypes
                 provides for the appropriate expression of information
                 represented by the genotypes. The mapping is
                 constructed as to guarantee feasibility of phenotypic
                 solutions for the problem under study. This enforcing
                 of constraints causes multiple genotypes to result in
                 one and the same phenotype. Neutral variants are
                 therefore frequent and play an important role in
                 maintaining genetic diversity. As a specific example,
                 we discuss Binary Genetic Programming (BGP), a variant
                 of Genetic Programming that uses binary strings as
                 genotypes and program trees as phenotypes.",
  notes =        "PPSN3

                 Tested on symbolic regression of 0.5x**2 and
                 exp(-3.0*x**2) Produces high level code (FORTRAN, C?)
                 which is compiled, claims this gives huge speedup.

                 ",
}

@InProceedings{banzhaf:1997:gabrrfr,
  author =       "Wolfgang Banzhaf and Peter Nordin and Markus Olmer",
  title =        "Generating Adaptive Behavior for a Real Robot using
                 Function Regression within Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "35--43",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  size =         "pages",
  notes =        "GP-97",
}

@Book{banzhaf:1997:book,
  author =       "Wolfgang Banzhaf and Peter Nordin and Robert E. Keller
                 and Frank D. Francone",
  title =        "Genetic Programming -- An Introduction; On the
                 Automatic Evolution of Computer Programs and its
                 Applications",
  publisher =    "Morgan Kaufmann, dpunkt.verlag",
  year =         "1998",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-510-X",
  ISBN =         "3-920993-58-6",
  URL =          "http://www.mkp.com/books_catalog/1-55860-510-X.asp",
  notes =        "details from banzhaf Tue, 23 Sep 1997 12:58:06
                 PDT

                 FROM THE FOREWORD BY J.R. KOZA

                 Genetic programming addresses the problem of automatic
                 programming, namely the problem of how to enable a
                 computer to do useful things without instructing it,
                 step by step, on how to do it. The rapid growth of the
                 field of genetic programming reflects the growing
                 recognition that, after half a century of research in
                 the fields of artificial intelligence, machine
                 learning, adaptive systems, automated logic, expert
                 systems, and neural networks, we may finally have a way
                 to achieve automatic programming. Genetic programming
                 is fundamentally different from other approaches in
                 terms of (i) its representation (namely, programs),
                 (ii) the role of knowledge (none), (iii) the role of
                 logic (none), and (iv) its mechanism (gleaned from
                 nature) for getting to a solution within the space of
                 possible solutions.

                 FROM THE FIRST SECTION OF THE BOOK

                 Automated programming will be one of the most important
                 areas of computer science research over the next twenty
                 years. Hardware speed and capability has leapt forward
                 exponentially. Yet software consistently lags years
                 behind the capabilities of the hardware. The gap
                 appears to be ever increasing. Demand for computer code
                 keeps growing but the process of writing code is still
                 mired in the modern day equivalent of the medieval
                 ``guild'' days. Like swords in the 15th century,
                 muskets before the early 19th century and books before
                 the printing press, each piece of computer code is,
                 today, handmade by a craftsman for a particular
                 purpose. The history of computer programming is a
                 history of attempts to move away from the ``craftsman''
                 approach -- structured programming, object oriented
                 programming, object libraries, rapid prototyping. But
                 each of these advances leaves the code that does the
                 real work firmly in the hands of a craftsman, the
                 programmer. The ability to enable computers to learn to
                 program themselves is of the utmost importance in
                 freeing the computer industry and the computer user
                 from code that is obsolete before it is released.

                 ",
  size =         "480 pages",
}

@Proceedings{banzhaf:1998:GP,
  title =        "Genetic Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64360-5",
  size =         "232 pages",
  notes =        "EuroGP'98",
}

@Article{lemonde:1998:23apr,
  key =          "lemonde",
  title =        "Les Robots inventeent la vie",
  journal =      "Le Monde",
  year =         "1998",
  month =        "23 Avril",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "in french, Description of EvoRobot'98 in particular:
                 Stefanio Nolfi and Dario Floreano, Jean Arcady-Meyer,
                 Henrik Lund, dittrich:1998:lmrrm, Nick Jakobi",
}

@Proceedings{banzhaf:1999:gecco99,
  title =        "{GECCO}-99: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-611-4",
  URL =          "http://www.amazon.com/exec/obidos/ASIN/1558606114/qid%3D977054373/105-7666192-3217523",
  size =         "2 volumes",
  notes =        "GECCO-99",
}

@Article{banzhaf:2000:genpletter,
  author =       "W. Banzhaf and W. B. Langdon",
  title =        "Some considerations on the reason for bloat",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "1",
  pages =        "81--91",
  month =        mar,
  email =        "banzhaf@tarantoga.cs.uni-dortmund.de",
  keywords =     "genetic algorithms, genetic programming, linear
                 genomes, effective fitness, neutral variations",
  ISSN =         "1389-2576",
  abstract =     "A representation-less model for genetic programming is
                 presented. The model is intended to examine the
                 mechanisms that lead to bloat in genetic programming
                 (GP). We discuss two hypotheses (fitness causes bloat
                 and neutral code is protective) and perform simulations
                 to examine the predictions deduced from these
                 hypotheses. Our observation is that predictions from
                 both hypotheses are realized in the simulated model.",
  notes =        "Article ID: 395990",
}

@Article{banzhaf:2000:ei,
  author =       "Wolfgang Banzhaf",
  title =        "Editorial Introduction",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "1/2",
  pages =        "5--6",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISSN =         "1389-2576",
}

@Article{banzhaf:2000:IS,
  author =       "Wolfgang Banzhaf",
  title =        "The artificial evolution of computer code",
  journal =      "IEEE Intelligent Systems",
  year =         "2000",
  volume =       "15",
  number =       "3",
  pages =        "74--76",
  month =        may # "-" # jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1094-7167",
  URL =          "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf",
  size =         "3 pages",
  notes =        "part of hirsh:2000:GP",
}

@Article{banzhaf:2000:ack,
  author =       "W. Banzhaf",
  title =        "Acknowledgement",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "4",
  pages =        "307",
  month =        oct,
  ISSN =         "1389-2576",
}

@Article{banzhaf:2001:intro,
  author =       "W. Banzhaf",
  title =        "Editorial Introduction",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "1",
  pages =        "5",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISSN =         "1389-2576",
  size =         "1 page",
}

@InProceedings{barash:1998:mGAofsalf,
  author =       "Danny Barash and Ann Orel and V. Rao Vemuri",
  title =        "Micro Genetic Algorithms in Finding the Optimal
                 Frequency for Stabilizing Atoms by High-intensity Laser
                 Fields",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Article{barnum:2000:qc,
  author =       "Howard Barnum and Herbert J Bernstein and Lee
                 Spector",
  title =        "Quantum circuits for {OR} and {AND} of {ORs}",
  journal =      "Journal of Physics A: Mathematical and General",
  year =         "2000",
  volume =       "33",
  number =       "45",
  pages =        "8047--8057",
  month =        "17 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://hampshire.edu/lspector/pubs/jpa.pdf",
  URL =          "http://hampshire.edu/lspector/pubs/jpa.ps",
  abstract =     "We give the first quantum circuit for computing f(0)
                 or f(1) more reliably than is classically possible with
                 a single evaluation function. Or therefor joins XOR (ie
                 parity) to give the full set of logical connectives (up
                 to relabelling of inputs and outputs) for which there
                 is a quantum speedup",
  notes =        "reports new quantum algorithms discovered by GP, with
                 some details on the GP processes",
}

@InProceedings{baron:1999:S,
  author =       "Christophe Baron and Guy Gouarderes",
  title =        "Systemions to model alternative issues in problem
                 solving",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "31--37",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{baronti:2002:gecco:lbp,
  title =        "Enhancing Tournament Selection to Prevent Code Bloat
                 in Genetic Programming",
  author =       "Flavio Baronti and Antonina Starita",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "17--22",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

@InProceedings{barry:1999:AXCSPE,
  author =       "Alwyn Barry",
  title =        "Aliasing in {XCS} and the Consecutive State Problem: 1
                 - Effects",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "19--26",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{barry:1999:AXCSPS,
  author =       "Alwyn Barry",
  title =        "Aliasing in {XCS} and the Consecutive State Problem: 2
                 - Solutions",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "27--34",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Proceedings{barry:2002:gecco:workshop,
  title =        "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "optimization, fuzzy model, genetic algorithm, design
                 optimization, case-based reasoning, evolutionary
                 algorithm, evolution strategies, simulated annealing,
                 agents, evolutionary computation, co-evolution, genetic
                 programming, parallel implementation, learning
                 classifier system, time series prediction, grammatical
                 evolution, multi-objective optimization, planning,
                 scheduling, industrial applications, machine learning,
                 niching, linkage learning",
  size =         "330 pages",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002)",
}

@Article{Bastian:2000:FSS,
  author =       "Andreas Bastian",
  title =        "Identifying fuzzy models utilizing genetic
                 programming",
  journal =      "Fuzzy Sets and Systems",
  volume =       "113",
  pages =        "333--350",
  year =         "2000",
  number =       "3",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V05-4234BFC-1/1/261a04fa056f3f0dfe0fb79a773a971a",
  abstract =     "Fuzzy models offer a convenient way to describe
                 complex nonlinear systems. Moreover, they permit the
                 user to deal with uncertainty and vagueness. Due to
                 these advantages fuzzy models are employed in various
                 fields of applications, e.g. control, forecasting, and
                 pattern recognition. Nevertheless, it has to be
                 emphasized that the identification of a fuzzy model is
                 a complex optimization task with many local minima.
                 Genetic programming provides a way to solve such
                 complex optimization problems. In this work, the use of
                 genetic programming to identify the input variables,
                 the rule base and the involved membership functions of
                 a fuzzy model is proposed. For this purpose, several
                 new reproduction operators are introduced.",
}

@InProceedings{battle:1999:GPFKBFLC,
  author =       "Daryl Battle and Abdollah Homaifar and Edward Tunstel
                 and Gerry Dozier",
  title =        "Genetic Programming of Full Knowledge Bases for Fuzzy
                 Logic Controllers",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1463--1468",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{bauer:1995:EEAGPACSS,
  author =       "Eric T. Bauer",
  title =        "Evolving Efficient Algorithms by Genetic Programming:
                 {A} Case Study in Sorting",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "1--10",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{baum:1998:tceae,
  author =       "Eric B. Baum and Igor Durdanovic",
  title =        "Toward Code Evolution By Artificial Economies",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB. See also baum:1998:tceaeTR",
}

@TechReport{baum:1998:tceaeTR,
  author =       "Eric B. Baum and Igor Durdanovic",
  title =        "Toward Code Evolution By Artificial Economies",
  institution =  "NEC Research Institute",
  year =         "1998",
  address =      "4 Independence Way, Princeto, NJ 08540, USA",
  month =        "10 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Hayek2 blocks world {"}crossover is much better than
                 headless chicken mutation{"} meta-agents, inherited
                 wealth, rent, intellectual property, strong typing
                 STGP. See also (baum:1998:tceae",
  size =         "53 pages",
}

@InProceedings{Baydar:2000:GECCO,
  author =       "Cem M. Baydar and Kazuhiro Saitou",
  title =        "A Genetic Programming Framework for Error Recovery in
                 Robotic Assembly Systems",
  pages =        "756",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@Article{bayne:1997:ve,
  author =       "Michael D. Bayne",
  title =        "Vive l'evolution",
  journal =      "Deep Magic",
  year =         "1997",
  month =        "12 " # feb,
  note =         "www page",
  keywords =     "genetic algorithms, genetic programming, Java, www",
  URL =          "http://www.go2net.com/internet/deep/1997/02/12/",
  abstract =     "Quick overview of GP, ants GP java demo, http links to
                 interesting places",
  notes =        "Deep magic at http://www.go2net.com/internet/deep/",
}

@InProceedings{beale:2002:RTIC,
  author =       "Stuart Beale",
  title =        "Traffic Data: Less is More",
  booktitle =    "Road Transport Information and Control",
  year =         "2002",
  address =      "Savoy Place, London, UK",
  month =        "19-21 " # mar,
  organisation = "IEE",
  email =        "rtic2002@iee.org.uk",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "RTIC 2002 http://conferences.iee.org.uk/RTIC/ For
                 {"}genetic algorithm{"} read {"}genetic
                 programming{"}",
}

@Article{ga:Beard93a,
  author =       "Nick Beard",
  title =        "The joy of genetic programming",
  journal =      "Personal Computer World",
  year =         "1993",
  pages =        "471--472",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  size =         "2 pages",
  notes =        "overview/introduction",
}

@InProceedings{beaulieu:2002:gecco,
  author =       "Julie Beaulieu and Christian Gagn{\'e} and Marc
                 Parizeau",
  title =        "Lens System Design And Re-engineering With
                 Evolutionary Algorithms",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "155--162",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "evolvable hardware, evolutionary reengineering,
                 evolvable optics, genetic algorithms, genetic
                 programming, lens system design",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)

                 Nominated for best at GECCO award",
}

@InProceedings{beck:1999:EAM,
  author =       "M. A. Beck and I. C. Parmee",
  title =        "Extending the bounds of the search space: {A}
                 Multi-Population approach",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1469--1476",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{Bedner:1997:elca,
  author =       "Ilja Bedner",
  title =        "Evolving Light Cycle Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "Evolution of autonomous agents that muts compete for
                 survival in the light-cycle game as seen in the movie
                 tron",
  notes =        "part of koza:1997:GAGPs",
}

@InCollection{beheler:1995:UGACFOSGPI,
  author =       "Joey Beheler",
  title =        "Using Genetic Algorithms and Convolution to Find
                 Optimal Strategies in Games without Perfect
                 Information",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "11--18",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{beligiannis:1999:EMPFNS,
  author =       "G. N. Beligiannis and E. N. Demiris and S. D.
                 Likothanassis",
  title =        "Evolutionary Multimodel Partitioning Filters for
                 Nonlinear Systems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1227",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{bell:1999:ESWRNNGA,
  author =       "Matt Bell",
  title =        "Evolving the Structure and Weights of Recurrent Neural
                 Network though Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "11--20",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{belpaeme:1999:evfd,
  author =       "Tony Belpaeme",
  title =        "Evolution of Visual Feature Detectors",
  booktitle =    "Late Breaking Papers at EvoISAP'99: the First European
                 Workshop on Evolutionary Computation in Image Analysis
                 and Signal Processing",
  year =         "1999",
  editor =       "Riccardo Poli and Stefano Cagnoni and Hans-Michael
                 Voigt and Terry Fogarty and Peter Nordin",
  pages =        "1--10",
  address =      "Goteborg, Sweden",
  month =        "28 " # may,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://arti.vub.ac.be/~tony/papers/EvoISAP99.ps.gz",
  abstract =     "This paper describes how sets of visual feature
                 detectors are evolved starting from simple primitives.
                 The primitives, of which some are inspired on visual
                 processing observed in mammalian visual pathways, are
                 combined using genetic programming to form a
                 feed-forward feature-extraction hierarchy. Input to the
                 feature detectors consists of a series of real-world
                 images, containing objects or faces. The results show
                 how each set of feature detectors self-organizes into a
                 set which is capable of returning feature vectors for
                 discriminating the input images. We discuss the
                 influence of different settings on the evolution of the
                 feature detectors and explain some phenomena.",
  notes =        "EvoIASP'99 Available as CSRP-99-10 from the School of
                 Computer Science, University of Birmingham, Edgbaston,
                 Birmingham B15 2TT, UK.

                 STGP. Information returned by each (of 5) feature
                 detector, entropy of the output vector p4 {"}if the
                 outputs are weel spread, meaning the feature detectors
                 return useful information, the fitness will be high.
                 Explicit parsimony preasure, but not needed p8?
                 LilGP.",
}

@InProceedings{Belur:1997:CORElb,
  author =       "Sheela V. Belur",
  title =        "{CORE}: Constrained Optimization by Random Evolution",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "280--286",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670

                 MATLAB",
}

@InProceedings{Bengio:1994:GPslrNN,
  author =       "S. Bengio and Y. Bengio and J. Cloutier",
  title =        "Use of genetic programming for the search of a new
                 learning rule for neutral networks",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "324--327",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "4 pages",
  notes =        "Uses GP to produce a learning rule for training a
                 neural network. Evolved rule like back-propergation but
                 better, differential is cubed. Says neural network is
                 fully connected,

                 ",
}

@InProceedings{benhahia:1997:GPvd,
  author =       "Ilham Benyahia and J. Yves Potwin",
  title =        "Genetic Programming for Vehicle Dispatch",
  booktitle =    "Proceedings of the 1997 {IEEE} International
                 Conference on Evolutionary Computation",
  year =         "1997",
  address =      "Indianapolis",
  publisher_address = "Piscataway, NJ, USA",
  month =        "13-16 " # apr,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ICEC-97",
}

@InCollection{benini:1995:GFESOADF,
  author =       "Luca Benini",
  title =        "Genetic Fitting: Evolutionary Search of Optimal
                 Approximations for Discrete Functions",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "19--28",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{bennett:1996:emaa,
  author =       "Forrest H {Bennett III}",
  title =        "Automatic Creation of an Efficient Multi-Agent
                 Architecture Using Genetic Programming with
                 Architecture-Altering Operations",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "30--38",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{bennett:1996:emaant,
  author =       "Forrest H {Bennett III}",
  title =        "Emergence of a Multi-Agent Architecture and New
                 Tactics For the Ant Colony Foraging Problem Using
                 Genetic Programming",
  booktitle =    "Proceedings of the Fourth International Conference on
                 Simulation of Adaptive Behavior: From animals to
                 animats 4",
  year =         "1996",
  editor =       "Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer
                 and Jordan Pollack and Stewart W. Wilson",
  pages =        "430--439",
  address =      "Cape Code, USA",
  publisher_address = "Cambridge, MA, USA",
  month =        "9-13 " # sep,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-63178-4",
  notes =        "SAB-96 Each tree within individual treated as an
                 {"}agent{"}. Uses koza add/delete adf genetic
                 operations to evolve the number of agents as well as
                 their code.",
}

@InProceedings{bennet:1996:ices60db,
  author =       "Forrest H {Bennett III} and John R. Koza and David
                 Andre and Martin A. Keane",
  title =        "Evolution of a 60 Decibel op amp using genetic
                 programming",
  booktitle =    "Proceedings of International Conference on Evolvable
                 Systems: From Biology to Hardware (ICES-96)",
  year =         "1996",
  editor =       "Tetsuya Higuchi and Iwata Masaya and Weixin Liu",
  volume =       "1259",
  series =       "Lecture Notes in Computer Science",
  address =      "Tsukuba, Japan",
  publisher_address = "Berlin, Germany",
  month =        "7-8 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-63173-9",
  ISSN =         "0302-9743",
  LCCN =         "QA76.618 .I57 1996",
  bibdate =      "Mon Nov 24 10:31:37 1997",
  acknowledgement = ack-nhfb,
  URL =          "http://www-cs-faculty.stanford.edu/~koza/ICES60dB.ps",
  abstract =     "Genetic programming was used to evolve both the
                 topology and sizing (numerical values) for each
                 component of a low-distortion, low-bias 60 decibel
                 (1000-to-1) amplifier with good frequency
                 generalization.",
  notes =        "URL=version 1 as presented at the conference
                 http://www.etl.go.jp:8080/etl/kikou/ICES96/",
}

@InProceedings{bennet:1997:msrrrdpe,
  author =       "Forrest H {Bennett III}",
  title =        "A Multi-Skilled Robot that Recognizes and Responds to
                 Different Problem Environments",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "44--51",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  size =         "9 pages",
  notes =        "GP-97 two memory cells SET-D0 and SET-D1. Max rpb size
                 600, up to 2 ADFs (up to 200 each). Architecture
                 altering operations. OBJECT-DIST OBJECT-KIND and
                 ROOM-COLOR. Fitness includes time penalty. 4 rooms in
                 continous (ie floating point) world. Program is
                 repeatedly evaluated until 1000 timesteps or hits mine.
                 Claims [page 49] code cant remember locations",
}

@InProceedings{bennett:1999:SCASE,
  author =       "Forrest H {Bennett III} and John R. Koza and Martin A.
                 Keane and David Andre",
  title =        "Darwinian Programming and Engineering Design using
                 Genetic Programming",
  booktitle =    "Proceedings of the 1st International Workshop on Soft
                 Computing Applied to Software Engineering",
  year =         "1999",
  editor =       "Conor Ryan and Jim Buckley",
  pages =        "31--40",
  address =      "University of Limerick, Ireland",
  month =        "12-14 " # apr,
  organisation = "SCARE",
  publisher =    "Limerick University Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-874653-52-6",
  URL =          "http://www.genetic-programming.com/SCASE99.ps",
  abstract =     "One of the central challenges of computer science is
                 to build a system that can automatically create
                 computer programs that are competitive with those
                 produced by humans. This paper presents a candidate set
                 of criteria that identify when a machine-created
                 solution is competitive with a human-produced result.
                 We argue that the field of design is a useful testbed
                 for determining whether an automated technique can
                 produce results that are competitive with
                 human-produced results. We present several results that
                 are competitive with the products of human creativity
                 and inventiveness. This claim is supported by the fact
                 that each of the results infringe on previously issued
                 patents.",
  notes =        "http://scare.csis.ul.ie/scase99/ SCASE'99

                 Automatic analog electrical circuit synthesis:

                 Campbell 1917 Ladder Filter patent,

                 Zobel 1925 {"}M-Derived Half Section{"} patent,

                 Cauer 1934 - 1936 Elliptic patents,

                 Darlington 1952 Emitter-Follower patent",
}

@InProceedings{bennet:1999:astsaecGP,
  author =       "Forrest H {Bennett III} and Martin A. Keane and David
                 Andre and John R. Koza",
  title =        "Automatic Synthesis of the Topology and Sizing for
                 Analog Electrical Circuits Using Genetic Programming",
  booktitle =    "Evolutionary Algorithms in Engineering and Computer
                 Science",
  year =         "1999",
  editor =       "Kaisa Miettinen and Marko M. Mäkelä and Pekka
                 Neittaanmäki and Jacques Periaux",
  pages =        "199--229",
  address =      "Jyväskylä, Finland",
  publisher_address = "Chichester, UK",
  month =        "30 " # may # " - 3 " # jun,
  publisher =    "John Wiley \& Sons",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-471-99902-4",
  URL =          "http://www.genetic-programming.com/EUROGEN99CIRCUITS.ps",
  abstract =     "The design (synthesis) of an analog electrical circuit
                 entails the creation of both the topology and sizing
                 (numerical values) of all of the circuit's components.
                 There has previously been no general automated
                 technique for automatically creating the design for an
                 analog electrical circuit from a high-level statement
                 of the circuit's desired behavior. We have demonstrated
                 how genetic programming can be used to automate the
                 design of seven prototypical analog circuits, including
                 a lowpass filter, a highpass filter, a passband filter,
                 a bandpass filter, a frequency-measuring circuit, a 60
                 dB amplifier, a differential amplifier, a computational
                 circuit for the square root function, and a
                 time-optimal robot controller circuit. All seven of
                 these genetically evolved circuits constitute instances
                 of an evolutionary computation technique solving a
                 problem that is usually thought to require human
                 intelligence. The approach described herein can be
                 directly applied to many other problems of analog
                 circuit synthesis.",
  notes =        "EUROGEN'99 ghostview barfs at EUROGEN99CIRCUITS.ps
                 27/11/99
                 http://www.wiley.com/Corporate/Website/Objects/Products/0,9049,91449,00.html",
}

@InProceedings{bennett:1999:AISB,
  author =       "Forrest H {Bennett III} and John R. Koza and Martin A.
                 Keane and David Andre",
  title =        "Genetic programming: Biologically inspired computation
                 that exhibits creativity in solving non-trivial
                 problems",
  booktitle =    "Proceedings of the AISB'99 Symposium on Scientific
                 Creativity",
  year =         "1999",
  pages =        "29--38",
  address =      "Edingburgh",
  month =        "8-9 " # apr,
  organisation = "The Society for the Study of Artificial Intelligence
                 and Simulation of Behaviour",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/AISB99.ps",
  abstract =     "This paper describes a biologically inspired
                 domain-independent technique, called genetic
                 programming, that automatically creates computer
                 programs to solve problems. We argue that the field of
                 design is a useful testbed for determining whether an
                 automated technique can produce results that are
                 competitive with human-produced results. We present
                 several results that are competitive with the products
                 of human creativity and inventiveness. This claim is
                 supported by the fact that each of the results infringe
                 on previously issued patents. This paper presents a
                 candidate set of criteria that identify when a
                 machine-created solution to a problem is competitive
                 with a human-produced result.",
  notes =        "AISB-99",
}

@InProceedings{bennett:1999:BPCSPHPD,
  author =       "Forrest H Bennett III and John R. Koza and James
                 Shipman and Oscar Stiffelman",
  title =        "Building a Parallel Computer System for \$18,000 that
                 Performs a Half Peta-Flop per Day",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1484--1490",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications",
  ISBN =         "1-55860-611-4",
  URL =          "http://www.genetic-programming.com/GECCO99BEOWULF.ps",
  abstract =     "Techniques of evolutionary computation generally
                 require significant computational resources to solve
                 non-trivial problems of interest. Increases in
                 computing power can be realized either by using a
                 faster computer or by parallelizing the application.
                 Techniques of evolutionary computation are especially
                 amenable to parallelization. This paper describes how
                 to build a 10-node Beowulf-style parallel computer
                 system for $18,000 that delivers about a half peta-flop
                 (1015 floating-point operations) per day on runs of
                 genetic programming. Each of the 10 nodes of the system
                 contains a 533 MHz Alpha processor and runs with the
                 Linux operating system. This amount of computational
                 power is sufficient to yield solutions (within a couple
                 of days per problem) to 14 published problems where
                 genetic programming has produced results that are
                 competitive with human-produced results.",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{bennett:1999:EMGPACPDF,
  author =       "Forrest H Bennett III and John R. Koza and Martin A.
                 Keane and Jessen Yu and William Mydlowec and Oscar
                 Stiffelman",
  title =        "Evolution by Means of Genetic Programming of Analog
                 Circuits that Perform Digital Functions",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1477--1483",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications",
  ISBN =         "1-55860-611-4",
  URL =          "http://www.genetic-programming.com/GECCO99NAND.ps",
  abstract =     "This paper demonstrates the ability of genetic
                 programming to evolve analog circuits that perform
                 digital functions and mixed analog-digital circuits.
                 The evolved circuits include two purely digital
                 circuits (a 100 nano-second NAND circuit and a
                 two-instruction arithmetic logic unit circuit) and one
                 mixed-signal circuit, namely a three-input
                 digital-to-analog converter.",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 ghostview barfs at GECCO99NAND.ps 26/11/99",
}

@InProceedings{bennett:2000:ICES,
  author =       "Forrest H {Bennett III} and John R. Koza and Jessen
                 and Yu and William Mydlowec",
  title =        "Automatic synthesis, placement, and routing of an
                 amplifier circuit by means of genetic programming",
  booktitle =    "Evolvable Systems: From Biology to Hardware Third
                 International Conference, ICES 2000",
  year =         "2000",
  editor =       "Julian Miller and Adrian Thompson and Peter Thomson
                 and Terrence C. Fogarty",
  volume =       "1801",
  series =       "LNCS",
  pages =        "1--10",
  address =      "Edinburgh, Scotland, UK",
  month =        "17-19 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67338-5",
  URL =          "http://www.genetic-programming.com/ices2000.ps",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67338-5",
  abstract =     "The complete design of a circuit typically includes
                 the tasks of creating the circuit's placement and
                 routing as well as creating its topology and component
                 sizing. Design engineers perform these four tasks
                 sequentially. Each of these four tasks is, by itself,
                 either vexatious or computationally intractable. This
                 paper describes an automatic approach in which genetic
                 programming starts with a high-level statement of the
                 requirements for the desired circuit and simultaneously
                 creates the circuit's topology, component sizing,
                 placement, and routing as part of a single integrated
                 design process. The approach is illustrated using the
                 problem of designing a 60 decibel amplifier. The
                 fitness measure considers the gain, bias, and
                 distortion of the candidate circuit as well as the area
                 occupied by the circuit after the automatic placement
                 and routing.",
  notes =        "ICES-2000",
}

@InProceedings{Bennett:2000:GECCOlb,
  author =       "Forrest H {Bennett III} and Eleanor G. Rieffel",
  title =        "Using Genetic Programming to Design Decentralized
                 Controllers for Self-Reconfigurable Modular Robots",
  pages =        "35--42",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Part of whitley:2000:GECCOlb",
}

@InProceedings{bennett:2000:EH,
  author =       "F. H {Bennett III} and E. G. Rieffel",
  title =        "Design of Decentralized Controllers for
                 Self-Reconfigurable Modular Robots Using Genetic
                 Programming",
  booktitle =    "Proceedings of the Second NASA / DoD Workshop on
                 Evolvable Hardware",
  year =         "2000",
  pages =        "43--52",
  address =      "Palo Alto, California",
  month =        jul # " 13-15",
  organization = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-0762-X",
  abstract =     "Advantages of self-reconfigurable modular robots over
                 conventional robots include physical adaptability,
                 robustness in the presence of failures, and economies
                 of scale. Creating control software for modular robots
                 is one of the central challenges to realizing their
                 potential advantages. Modular robots differ enough from
                 traditional robots that new techniques must be found to
                 create software to control them. The novel difficulties
                 are due to the fact that modular robots are ideally
                 controlled in a decentralized manner, dynamically
                 change their connectivity topology, may contain
                 hundreds or thousands of modules, and are expected to
                 perform tasks properly even when some modules fail. We
                 demonstrate the use of genetic programming to
                 automatically create distributed controllers for
                 self-reconfigurable modular robots. .",
  notes =        "EH-2000
                 http://ic-www.arc.nasa.gov/ic/eh2000/index.html",
}

@InProceedings{bennett:2001:EuroGP,
  author =       "Forrest H {Bennett III} and Brad Dolin and Eleanor G.
                 Rieffel",
  title =        "Programmable Smart Membranes: Using Genetic
                 Programming to Evolve Scalable Distributed Controllers
                 for a Novel Self-Reconfigurable Modular Robotic
                 Application",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "234--245",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, modular
                 robot, distributed control, smart membrane,
                 self-reconfigurable, scalable, robust",
  ISBN =         "3-540-41899-7",
  size =         "12 pages",
  abstract =     "Self-reconfigurable modular robotics represents a new
                 approach to robotic hardware, in which the {"}robot{"}
                 is composed of many simple, identical interacting
                 modules. We propose a novel application of modular
                 robotics: the programmable smart membrane, a device
                 capable of actively filtering objects based on numerous
                 measurable attributes. Creating control software for
                 modular robotic tasks like the smart membrane is one of
                 the central challenges to realizing their potential
                 advantages. We use genetic programming to evolve
                 distributed control software for a 2-dimensional smart
                 membrane capable of distinguishing objects based on
                 color. The evolved controllers exhibit scalability to a
                 large number of modules and robustness to the initial
                 configurations of the robotic filter and the
                 particles.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{benson:2000:E,
  author =       "Karl Benson",
  title =        "Evolving automatic target detection algorithms",
  booktitle =    "Graduate Student Workshop",
  year =         "2000",
  editor =       "Conor Ryan and Una-May O'Reilly and William B.
                 Langdon",
  pages =        "249--252",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{Benson:2000:GECCO,
  author =       "Karl A Benson and David Booth and James Cubillo and
                 Colin Reeves",
  title =        "Automatic Detection of Ships in Spaceborne {SAR}
                 Imagery",
  pages =        "767",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO

                 ",
}

@InProceedings{benson:2000:efsmegpatdsi,
  author =       "Karl A Benson",
  title =        "Evolving Finite State Machines with Embedded Genetic
                 Programming for Automatic Target Detection within {SAR}
                 Imagery",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1543--1549",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, image
                 processing applications",
  ISBN =         "0-7803-6375-2",
  abstract =     "This paper presents a model comprising Finite State
                 Machines (FSMs) with embedded Genetic Programs (GPs)
                 which co-evolve to perform the task of Automatic Target
                 Detection (ATD). The fusion of a FSM and GPs allows for
                 a control structure (main program), the FSM, and
                 sub-programs, the GPs, to co-evolve in a symbiotic
                 relationship. The GP outputs along with the FSM state
                 transition levels are used to construct confidence
                 intervals that enable each pixel within the image to be
                 classified as either target or non-target, or to cause
                 a state transition to take place and further analysis
                 of the pixel to be performed. The algorithms produced
                 using this method consist of nominally four GPs, with a
                 typical node cardinality of less than ten, that are
                 executed in an order dictated by the FSM. The results
                 of the experimentation performed are compared to those
                 obtained in two independent studies of the same problem
                 using Kohonen Neural Networks and a two stage Genetic
                 Programming strategy.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644

                 ",
}

@InProceedings{benson:2000:PCEMMA,
  author =       "Karl Benson",
  title =        "Performing Classification with an Environment
                 Manipulating Mutable Automata",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "264--271",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, system
                 modeling and control",
  ISBN =         "0-7803-6375-2",
  abstract =     "In this paper a novel approach to performing
                 classification is presented. Hypersurface Discriminant
                 functions are evolved using Genetic Programming. These
                 discriminant functions reside in the states of a Finite
                 State Automata, which has the ability to reason 1 and
                 logically combine the hypersurfaces to generate a
                 complex decision space. An object may be classified by
                 one or many of the discriminant functions, this is
                 decided by the automata. During the evolution of this
                 symbiotic architecture, feature selection for each of
                 the discriminant functions is achieved implicitly, a
                 task which is normally performed before a
                 classification algorithm is trained. Since each
                 dis-criminant function has different features, and
                 objects may be classified with one or more discriminant
                 functions, no two objects from the same class need be
                 classified using the same features. Instead, the most
                 appropriate features for a given object are used.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644

                 ",
}

@InProceedings{benson4,
  author =       "Karl A Benson and David Booth and James Cubillo and
                 Colin Reeves",
  title =        "On the use of evolution to construct finite state
                 machines and mathematical functions to perform
                 automatic target detection",
  booktitle =    "Proceedings of the 3rd {IMA} conference on image
                 processing: mathematical methods, algorithms and
                 applications",
  year =         "2000",
  address =      "Leicester, UK",
  month =        "13-15 " # sep,
  publisher =    "IEE",
  organisation = "The Institute of Mathematics and its Applications, The
                 Institute of Physics, The Institute of Electrical
                 Engineers",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{benson5,
  author =       "Karl A Benson",
  title =        "Evolving Automatic Target Detection Algorithms that
                 logically Combine Decision Spaces",
  booktitle =    "Proceedings of the 11th British Machine Vision
                 Conference",
  year =         "2000",
  pages =        "685--694",
  address =      "Bristol, UK",
  month =        "11-14 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{bentley:1999:TWGDACEEDP,
  author =       "Peter Bentley and Sanjeev Kumar",
  title =        "Three Ways to Grow Designs: {A} Comparison of
                 Embryogenies for an Evolutionary Design Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "35--43",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{bentley:1999:EA,
  author =       "Peter J. Bentley",
  title =        "Evolving fuzzy detectives: An investigation into the
                 evolution of fuzzy rules",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "38--47",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-99LB, fraud detection, pre-GP 3-way clustering
                 of each attribute multi-objective fitness function.
                 variable size tree genotypes, bitstring in tree
                 specifies input field, start small. Newer version
                 available bentley:2000:EA

                 Iris and Wisconsin Breast Cancer. Perfomance falls
                 lineraly or quadratically with noise.",
}

@InProceedings{Bentley:2000:EA,
  author =       "Peter J. Bentley",
  title =        "{"}Evolutionary, my dear Watson{"} Investigating
                 Committee-based Evolution of Fuzzy Rules for the
                 Detection of Suspicious Insurance Claims",
  pages =        "702--709",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  URL =          "http://www.cs.ucl.ac.uk/staff/P.Bentley/rw074.zip",
  size =         "8 pages",
  notes =        "See also bentley:1999:EA

                 A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InCollection{beretz:2002:EAMEABGP,
  author =       "John P. Beretz",
  title =        "Evolution of Algorithms for Multi-Species Emergent
                 Assembly Behavior using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "21--30",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{berger:1999:AHGAVRPTWIC,
  author =       "Jean Berger and Mourad Sassi and Martin Salois",
  title =        "A Hybrid Genetic Algorithm for the Vehicle Routing
                 Problem with Time Windows and Itinerary Constraints",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "44--51",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{berger:2002:DMILFAGP,
  author =       "Eric Berger",
  title =        "Development of a Minimal Information Line Following
                 Algorithm using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "31--35",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{bergstrom:2000:eiraatrGP,
  author =       "Agneta Bergstrom and Patricija Jaksetic and Peter
                 Nordin",
  title =        "Enhancing Information Retrieval by Automatic
                 Acquisition of Textual Relations using Genetic
                 Programming",
  booktitle =    "IUI 2000",
  year =         "2000",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming, machine
                 learning, natural language processing, semantic
                 networks, information retrieval",
  URL =          "http://www.viktoria.informatik.gu.se/groups/play/publications/bergstrom.pdf",
  size =         "4 pages",
  abstract =     "We have explored a novel method to find textual
                 relations in electronic documents using genetic
                 programming and semantic networks. This can be used for
                 enhancing information retrieval and simplifying user
                 interfaces. The automatic extraction of relations from
                 text enables easier updating of electronic dictionaries
                 and may reduce interface area both for search input and
                 hit output on small screens such as cell phones and
                 PDAs (Personal Digital Assistants).",
  notes =        "www",
}

@InProceedings{bergstrom:2000:atrawGP,
  author =       "Agneta Bergstrom and Patricija Jaksetic and Peter
                 Nordin",
  title =        "Acquiring Textual Relations Automatically on the Web
                 using Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "237--246",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{bersano-begey:1996:pici,
  author =       "Tommaso F. Bersano-Begey and Jason M. Daida and John
                 F. Vesecky and Frank L. Ludwig",
  title =        "A Platform-Independent Collaborative Interface for
                 Genetic Programming Applications: Image Analysis for
                 Scientific Inquiry",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "1--8",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB java The email address for the bookstore for
                 mail orders is mailorder@bookstore.stanford.edu Phone
                 no 415-329-1217 or 800-533-2670",
}

@InProceedings{bersano-begey:1997:jcifGPa,
  author =       "Tommaso F. Bersano-Begey and Jason M. Daida and John
                 F. Vesecky and Frank L. Ludwig",
  title =        "A {Java} Collaborative Interface for Genetic
                 Programming Applications: Image Analysis and Scientific
                 Inquiry",
  booktitle =    "Proceedings of the 1997 {IEEE} International
                 Conference on Evolutionary Computation",
  year =         "1997",
  address =      "Indianapolis",
  publisher_address = "Piscataway, NJ, USA",
  month =        "13-16 " # apr,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/ICEC97image.pdf",
  notes =        "ICEC-97

                 Collaborative Interface Demonstration
                 http://www.sprl.umich.edu/acers/gaia/collab.html

                 GAIA (Genetic programming Assistant for Image
                 Analysis)

                 slides http://www-personal.umich.edu/~tombb/gaia74/",
}

@InProceedings{Bersano-Begey:1997:cedslo,
  author =       "Tommaso F. Bersano-Begey",
  title =        "Controlling Exploration, Diversity and Escaping Local
                 Optima in {GP}: Adapting Weights of Training Sets to
                 Model Resource Consumption",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "7--10",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{Bersano-Begey:1997:grffc,
  author =       "Tommaso F. Bersano-Begey and Jason M. Daida",
  title =        "A Discussion on Generality and Robustness and a
                 Framework for Fitness Set Construction in Genetic
                 Programming to Promote Robustness",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "11--18",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670

                 Fri, 05 Sep 1997 06:14:54 EDT I did some follow-up work
                 in trying to improve generality of code in the
                 wall-following problem, and started to look at how to
                 gain more information about generality by recording the
                 distribution of hits (rather than just their total), an
                 iterative algorithm to check for and correct ambiguous
                 training sets (one which can be solved by other
                 solutions besides the correct one), and an account of
                 the relationship between size and generality of
                 solutions. The following was a very preliminary work,
                 but I am now working on expanding each topic and
                 writing them in a more formal way.

                 slides http://www-personal.umich.edu/~tombb/gp973/",
}

@InProceedings{bersano-begey:1997:,
  author =       "T. F. Bersano-Begey and P. G. Kenny and E. H. Durfee",
  title =        "Multi-Agent Teamwork, Adaptive Learning and
                 Adversarial Planning in Robocup Using a {PRS}
                 Architecture",
  booktitle =    "IJCAI97",
  year =         "1997",
  note =         "accepted",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "um-prs.pdf broken 5-sep-97",
}

@InProceedings{Bersini:2000:GECCO,
  author =       "Hugues Bersini",
  title =        "Chemical Crossover",
  pages =        "825--832",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{Bertram:1997:ris,
  author =       "Robert R. Bertram and Jason M. Daida and John F.
                 Vesecky and Guy A. Meadows and Christian Wolf",
  title =        "Reconstructing Incomplete Signals Using Nonlinear
                 Interpolation and Genetic Algorithms",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "19--27",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{bertram:1998:risiGA,
  author =       "Robert R. Bertram and Jason M. Daida and John F.
                 Vesecky and  Guy A. Meadows and Christian Wolf",
  title =        "Reconstructing Incomplete Signals Using Nonlinear
                 Interpolation and Genetic Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "447--454",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{best:1999:CMGSE,
  author =       "Michael L. Best",
  title =        "Coevolving Mutualists Guide Simulated Evolution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "941",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming,
                 poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{bettenhausen:1995:biox,
  author =       "K. D. Bettenhausen and S. Gehlen and P. Marenbach and
                 H. Tolle",
  title =        "Bio{X}++ -- {N}ew results and conceptions concerning
                 the intelligent control of biotechnological processes",
  booktitle =    "6th International Conference on Computer Applications
                 in Biotechnology",
  year =         "1995",
  editor =       "A. Munack and K. Sch{\"u}gerl",
  pages =        "324--327",
  organisation = "IFAC Publications",
  publisher =    "Elsevier Science",
  email =        "mali@rt.e-technik.tu-darmstadt.de",
  keywords =     "genetic algorithms, genetic programming Expert
                 systems, neural networks, fuzzy systems, learning
                 control, fermentation, biotechnology",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/LIT/rst_95_03.ps.gz",
  size =         "4 pages",
  abstract =     "BioX++ facilities the transparent generation of
                 process control stratgies and sequences based on
                 automatically self-organized structured process models.
                 Experimental results showing the increased product
                 yeild and the discussion of approach-specific problems
                 are part of this paper as well as the new approaches
                 actually examined.",
  notes =        "14--17 May, Garmisch-Partenkirchen, Germany",
}

@InProceedings{bettenhausen:1995:sombbff,
  author =       "K. D. Bettenhausen and P. Marenbach",
  title =        "Self-organizing modeling of biotechnological batch and
                 fed-batch fermentations",
  booktitle =    "EUROSIM'95",
  year =         "1995",
  editor =       "F. Breitenecker and I. Husinsky",
  publisher =    "Elsevier",
  email =        "kurt.dirk.bettenhausen@rt.e-technik.tu-darmstadt.de
                 (Kurt Dirk Bettenhausen),
                 mali@rt.e-technik.tu-darmstadt.de",
  keywords =     "genetic algorithms, genetic programming, fermentation,
                 biotechnology",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/rst_95_23.ps.gz",
  size =         "5 pages",
  abstract =     "An approach for the automatic generation of dynamic
                 nonlinear process models obtained from experimantal
                 process data and theoretical biological and chemical
                 reflections using genetic programming for the
                 supervision and coordination of the symbolic model
                 structure during automatic development

                 BioX++ includes (amongs fuzzy rule learning, expert
                 system, NN also refered to) GP to produce process
                 models, constants adapted using standard algorithmic
                 techniques.",
  notes =        "11--15 September, Vienna, Austria",
}

@InProceedings{bettenhausen:1995:sombbffGP,
  author =       "K. D. Bettenhausen and P. Marenbach and Stephan Freyer
                 and Hans Rettenmaier and Ullrich Nieken",
  title =        "Self-organizing Structured modeling of a
                 Biotechnological Fed-batch fermentation by Means of
                 Genetic Programming",
  booktitle =    "First International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1995",
  editor =       "A. M. S. Zalzala",
  volume =       "414",
  pages =        "481--486",
  address =      "Sheffield, UK",
  publisher_address = "London, UK",
  month =        "12-14 " # sep,
  publisher =    "IEE",
  email =        "mali@rt.e-technik.tu-darmstadt.de",
  keywords =     "genetic programming, symbolic modelling, system
                 identification, biotechnology, predictive control",
  ISBN =         "0-85296-650-4",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/rst_95_24.ps.gz",
  size =         "6 pages",
  abstract =     "12--14 September 1995, Halifax Hall, University of
                 Sheffield, UK see also
                 http://www.iee.org.uk/LSboard/Conf/program/galprog.htm

                 The article describes an approach for the
                 self-organizing generation of models of complex and
                 unknown processes by means of GP and its application on
                 a biotechnological fed-batch production. First
                 experiments of the symbolic generation of structured
                 models within an industrial cooperation with BASF are
                 presented.",
  notes =        "Deals much more than bettenhausen:1995:ssmbff and
                 bettenhausen:1995:biox with the idea of Genetic
                 Programming.

                 First results from an application of our approach for
                 finding model of an industrial fed-batch fermentation
                 process are presented which. This work was part of an
                 cooperation of our Institute and the BASF AG,
                 Ludwigshafen, Germany. This paper includes a more
                 detailed description of how our GP system works.

                 ",
}

@InProceedings{beyer:1999:FNLEOGQFM,
  author =       "Hans-Georg Beyer and Dirk V. Arnold",
  title =        "Fitness Noise and Localization Errors of the Optimum
                 in General Quadratic Fitness Models",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "817--824",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{bezdek:1999:EADC,
  author =       "Trevor Bezdek",
  title =        "Evolution and Analysis of {DNA} Classifiers",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "21--30",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{bhanu:2002:gecco,
  author =       "Bir Bhanu and Yingqiang Lin",
  title =        "Learning Composite Operators For Object Detection",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1003--1010",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "real world applications, composite operators, genetic
                 programming, image segmentation, object detection",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{bhattacharya:2001:HIS,
  title =        "A Linear Genetic Programming Approach for Modeling
                 Electricity Demand Prediction in Victoria",
  author =       "Maumita Bhattacharya and Ajith Abraham and Baikunth
                 Nath",
  editor =       "Ajith Abraham and Mario Koppen",
  booktitle =    "2001 International Workshop on Hybrid Intelligent
                 Systems",
  series =       "LNCS",
  pages =        "379--394",
  publisher =    "Springer-Verlag",
  address =      "Adelaide, Australia",
  publisher_address = "Berlin",
  month =        "11-12 " # dec,
  year =         "2001",
  email =        "maumita.bhattacharya@infotech.monash.edu.au,
                 ajith.abraham@infotech.monash.edu.au,
                 b.nath@infotech.monash.edu.au",
  keywords =     "genetic algorithms, genetic programming, Linear
                 genetic programming, neuro-fuzzy, neural networks,
                 forecasting, electricity demand",
  URL =          "http://www-mugc.cc.monash.edu.au/~abrahamp/172.pdf",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6",
  ISBN =         "3-7908-1480-6",
  abstract =     "Genetic programming (GP), a relatively young and
                 growing branch of evolutionary computation is gradually
                 proving to be a promising method of modelling complex
                 prediction and classification problems. This paper
                 evaluates the suitability of a linear genetic
                 programming (LGP) technique to predict electricity
                 demand in the State of Victoria, Australia, while
                 comparing its performance with two other popular soft
                 computing techniques. The forecast accuracy is compared
                 with the actual energy demand. To evaluate, we
                 considered load demand patterns for ten consecutive
                 months taken every 30 minutes for training the
                 different prediction models. Test results show that
                 while the linear genetic programming method delivered
                 satisfactory results, the neuro fuzzy system performed
                 best for this particular application problem, in terms
                 of accuracy and computation time, as compared to LGP
                 and neural networks.",
  notes =        "HIS01",
}

@InProceedings{bhattacharyya:1998:rsGPlhf,
  author =       "Siddhartha Bhattacharyya and Olivier Pictet and Gilles
                 Zumbach",
  title =        "Representational Semantics for Genetic Programming
                 Based Learning in High-Frequency Financial Data",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "11--16",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{Bickel:1989:tsrGA,
  author =       "Authur S. Bickel and Riva Wenig Bickel",
  title =        "Tree Structured Rules in Genetic Algorithms",
  booktitle =    "Genetic Algorithms and their Applications: Proceedings
                 of the second International Conference on Genetic
                 Algorithms",
  year =         "1987",
  editor =       "John J. Grefenstette",
  pages =        "77--81",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "Hillsdale, NJ, USA",
  month =        "28-31 " # jul,
  publisher =    "Lawrence Erlbaum Associates",
  keywords =     "genetic algorithms, genetic programming",
  size =         "5 pages",
  abstract =     "GA applied to variable length lists of tree structured
                 production rules. Mutation applied within trees, eg >
                 replaced by >=. Inversion applied by re-ordering rules,
                 nb does change semantics of rules set because they are
                 applied in order, not applied within trees. Crossover
                 applied to lists NOT to contents of trees",
}

@InProceedings{bisat:1998:ussbctn,
  author =       "Mona T. Bisat and Charles W. Richter and Gerald B.
                 Sheble",
  title =        "Using Adaptive Agents to Study Bilateral Contracts and
                 Trade Networks",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Article{Bishop96,
  author =       "P. Bishop and R. Bloomfield",
  title =        "Conservative theory for long-term reliability-growth
                 prediction [of software]",
  journal =      "IEEE Transactions on Reliability",
  volume =       "45",
  number =       "4",
  month =        dec,
  pages =        "550--560",
  notes =        "Theoretical or Mathematical",
  address =      "Adelard, London, UK",
  year =         "1996",
  ISSN =         "0018-9529",
  URL =          "http://ieeexplore.ieee.org/iel1/24/12134/00556578.pdf?isNumber=12134&prod=JNL&arnumber=556578&arSt=550&ared=560&arAuthor=Bishop%2C+P.%3B+Bloomfield%2C+R.",
  URL =          "http://www.adelard.co.uk/resources/papers/pdf/issre96m.pdf",
  abstract =     "This paper describes a different approach to software
                 reliability growth modeling which enables long-term
                 predictions. Using relatively common assumptions, it is
                 shown that the average value of the failure rate of the
                 program, after a particular use-time, t, is bounded by
                 N/(e/spl middot/t), where N is the initial number of
                 faults. This is conservative since it places a
                 worst-case bound on the reliability rather than making
                 a best estimate. The predictions might be relatively
                 insensitive to assumption violations over the longer
                 term. The theory offers the potential for making
                 long-term software reliability growth predictions based
                 solely on prior estimates of the number of residual
                 faults. The predicted bound appears to agree with a
                 wide range of industrial and experimental reliability
                 data. Less pessimistic results can be obtained if
                 additional assumptions are made about the failure rate
                 distribution of faults.",
  keywords =     "software reliability, reliability theory, failure
                 analysis, long-term reliability-growth prediction,
                 software reliability growth modeling, program failure
                 rate, use-time, initial fault number, worst-case bound,
                 residual fault number, failure rate distribution",
  notes =        "cf. brady:murphy",
}

@InProceedings{bleuler:2001:mgprbus,
  author =       "Stefan Bleuler and Martin Brack and Lothar Thiele and
                 Eckart Zitzler",
  title =        "Multiobjective Genetic Programming: Reducing Bloat
                 Using {SPEA2}",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "536--543",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, SPEA, SPEA2,
                 Pareto, external set",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =

                 Solutions to even-9 parity.",
}

@InProceedings{BT94,
  author =       "Tobias Blickle and Lothar Thiele",
  title =        "Genetic Programming and Redundancy",
  booktitle =    "Genetic Algorithms within the Framework of
                 Evolutionary Computation (Workshop at KI-94,
                 Saarbr{\"u}cken)",
  editor =       "J. Hopf",
  publisher =    "Max-Planck-Institut f{\"u}r Informatik
                 (MPI-I-94-241)",
  address =      "

                 Im Stadtwald, Building 44, D-66123 Saarbr{\"u}cken,
                 Germany

                 ",
  pages =        "33--38",
  year =         "1994",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.tik.ee.ethz.ch/~blickle/GPandRedundancy.ps.gz",
  size =         "6 pages",
  notes =        "From GP list Wed, 22 Mar 95 we did some work on the
                 convergence problem and the redundancy in the trees in
                 GP. It turned out that {"}bloating{"} is a property of
                 GP that arises from the fact that more redundant trees
                 have a higher probability to survive crossover. As a
                 result, the redundant part of the trees grow bigger and
                 bigger because the increased proportion of redundant
                 {"}cut-sites{"} in the tree again lead to a higher
                 probability to survive crossover.

                 Gives a formula for tournament size related to
                 proportion of crossover in a generational GP. Ie
                 recommending T=10 for pc=0.9. This does not apply to
                 steady state GA.

                 ",
}

@TechReport{blickle:1995:css,
  author =       "Tobias Blickle and Lothar Thiele",
  title =        "A Comparison of Selection Schemes Used in Genetic
                 Algorithms",
  institution =  "TIK Institut fur Technische Informatik und
                 Kommunikationsnetze, Computer Engineering and Networks
                 Laboratory, ETH, Swiss Federal Institute of
                 Technology",
  year =         "1995",
  type =         "TIK-Report",
  number =       "11",
  edition =      "2",
  address =      "Gloriastrasse 35, 8092 Zurich, Switzerland",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.tik.ee.ethz.ch/~blickle/TIK-Report.html",
  url_2 =        "ftp://ftp.tik.ee.ethz.ch/pub/people/blickle/tikreport_v2.ps.gz",
  abstract =     "

                 Genetic Algorithms are a common probabilistic
                 optimization method based on the model of natural
                 evolution. One important operator in these algorithms
                 is the selection scheme for which a new description
                 model is introduced in this paper. With this a
                 mathematical analysis of tournament selection,
                 truncation selection, linear and exponential ranking
                 selection and proportional selection is carried out
                 that allows an exact prediction of the fitness values
                 after selection. The further analysis derives the
                 selection intensity, selection variance, and the loss
                 of diversity for all selection schemes. For completion
                 a pseudo- code formulation of each method is included.
                 The selection schemes are compared and evaluated
                 according to their properties leading to an unified
                 view of these different selection schemes. Furthermore
                 the correspondence of binary tournament selection and
                 ranking selection in the expected fitness distribution
                 is proven.",
  notes =        "

                 Of special interest for the GP community may be the
                 fact that in this report three analytic approximation
                 formulas are obtained using GP for symbolic regression.
                 The method is described in appendix A of the
                 report.

                 Second (extended and corrected) edition available via
                 www and ftp Dec 1995

                 ",
  size =         "65 pages",
}

@Article{blickle:1995:ea,
  author =       "Tobias Blickle",
  title =        "Optimieren nach dem Vorbild der Natur, Evolutionare
                 Algorithmen",
  journal =      "Bulletin SEV/VSE",
  year =         "1995",
  volume =       "86",
  number =       "25",
  pages =        "21--26",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.tik.ee.ethz.ch/~blickle/EA.ps.gz",
  notes =        "Introduction to GA and GP in German",
}

@TechReport{blickle:1995:YAGPLIC,
  author =       "Tobias Blickle",
  title =        "{YAGPLIC} User Manual",
  institution =  "Computer Engineering and Communication Network Lab
                 (TIK), Swiss Federal Institute of Technology (ETH)",
  year =         "1995",
  address =      "Gloriastrasse 35, CH-8092, Zurich",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.tik.ee.ethz.ch/~blickle/YAGPLIC.html",
  notes =        "Yet Another Genetic Programming Library In C Written
                 in C for maximum performance. Object-oriented
                 user-interface. Up to 32 data types possible in a tree
                 and type-consistent crossover. Several selection
                 schemes implemented: proportionate selection, ranking
                 selection, tournament selection, truncation selection.
                 Extensive output of statistical data for post
                 processing with MATHEMATICA.",
}

@TechReport{blickle:1996:ecs,
  author =       "Tobias Blickle",
  title =        "Evolving Compact Solutions in Genetic Programming: {A}
                 Case Study",
  institution =  "TIK Institut fur Technische Informatik und
                 Kommunikationsnetze, Computer Engineering and Networks
                 Laboratory, ETH, Swiss Federal Institute of
                 Technology",
  year =         "1996",
  type =         "TIK-Report",
  address =      "Gloriastrasse 35, 8092 Zurich, Switzerland",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.tik.ee.ethz.ch/~blickle/ppsn1.ps.gz",
  abstract =     "Genetic programming (GP) is a variant of genetic
                 algorithms where the data structures handled are trees.
                 This makes GP especially useful for evolving functional
                 relationships or computer programs, as both can be
                 represented as trees. Symbolic regression is the
                 determination of a function dependence $y=g({\bf x})$
                 that approximates a set of data points (${\bf
                 x_i},y_i$). In this paper the feasibility of symbolic
                 regression with GP is demonstrated on two examples
                 taken from different domains. Furthermore several
                 suggested methods from literature are compared that are
                 intended to improve GP performance and the readability
                 of solutions by taking into account introns or
                 redundancy that occurs in the trees and keeping the
                 size of the trees small. The experiments show that GP
                 is an elegant and useful tool to derive complex
                 functional dependencies on numerical data.",
  notes =        "Presented at PPSN 4

                 ",
  size =         "10 pages",
}

@InProceedings{blickle96,
  author =       "Tobias Blickle",
  title =        "Evolving Compact Solutions in Genetic Programming: {A}
                 Case Study",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  booktitle =    "Parallel Problem Solving From Nature IV. Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  publisher =    "Springer-Verlag",
  volume =       "1141",
  series =       "LNCS",
  pages =        "564--573",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  keywords =     "genetic algorithms, genetic programming, bloat,
                 deleting crossover",
  ISBN =         "3-540-61723-X",
  URL =          "http://www.tik.ee.ethz.ch/~blickle/ppsn1.ps.gz",
  size =         "10 pages",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html
                 PPSN4

                 same as blickle:1996:ecs Test of effectiveness of GP,
                 EDI, deleting and adaptive anti-bloat techniques.
                 Results differ continuous (symbolic regression) v.
                 discrete 6-mux deleting crossover similar to code
                 editing based on code interpretation during fitness
                 evaluation.",
}

@PhdThesis{blickle:thesis,
  author =       "Tobias Blickle",
  title =        "Theory of Evolutionary Algorithms and Application to
                 System Synthesis",
  school =       "Swiss Federal Institute of Technology",
  year =         "1996",
  address =      "Zurich",
  publisher =    "vdf Verlag",
  publisher_address = "CH-8092 Zurich",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-7281-2433-8",
  URL =          "http://www.vdf.ethz.ch/info/2433.html",
  URL =          "http://www.handshake.de/user/blickle/publications/diss.pdf",
  size =         "272 pages",
  abstract =     "The subject of this thesis are Evolutionary Algorithms
                 and their application to the automated synthesis and
                 optimization of complex digital systems composed of
                 hardware and software elements. In Part I the
                 probabilistic optimization method of Evolutionary
                 Algorithms (EAs) is presented. EAs apply the principles
                 of natural evolution (selection and random variation)
                 to a random set of points (population of individuals)
                 in the search space. Evolutionary Algorithms are first
                 embedded in the context of global optimization and the
                 most important and widely used methods for constraint-
                 handling are introduced, including a new method called
                 IOS (individual objective switching). This is followed
                 by a new formal description of selection schemes based
                 on fitness distributions. This description enables an
                 extensive and uniform examination of various selection
                 schemes leading to new insights about the impact of the
                 selection method parameters on the optimization
                 process. Subsequently the variation (recombination)
                 process of Evolutionary Algorithms is examined. As
                 different analysis techniques are necessary depending
                 on the representation of the problem (e.g. bit string,
                 vector, tree, graph) only the recombination process for
                 tree-representation (Genetic Programming) is
                 considered. A major part of the explanation treats the
                 problem of ``bloating'', i.e., the tree-size increase
                 during optimization. Furthermore, a new redundancy
                 based explanation of bloating is given and several
                 methods to avoid bloating are compared. Part II is
                 dedicated to the application of Evolutionary Algorithms
                 to the optimization of complex digital systems. These
                 systems are composed of hardware and software
                 components and characterized by a high complexity
                 caused by their heterogeneity (hardware/ software,
                 electrical/mechanical, analog/digital). Computer-aided
                 synthesis at the abstract system level is advantageous
                 for application specific systems or embedded systems as
                 it enables time-to-market to be reduced with a decrease
                 in design errors and costs. The main task of
                 system-synthesis is the transformation of a behavioral
                 specification (for example given by an algorithm) into
                 a structural specification, such as a composition of
                 processors, general or dedicated hardware modules,
                 memories and busses, while regarding various
                 restrictions, e.g. maximum costs, data throughput rate,
                 reaction time. Problems related to system synthesis are
                 for example performance estimation, architecture
                 optimization and design-space exploration. This thesis
                 introduces a formal description of system-synthesis
                 based on a new graph model where the specification is
                 translated into a specification graph. The main tasks
                 of system-synthesis (allocation, binding and
                 scheduling) are defined for this graph and the process
                 of system synthesis is formulated as a constrained
                 global optimization problem. This optimization problem
                 is solved by Evolutionary Algorithms using the results
                 of Part I of the thesis. Finally, an example of
                 synthesizing implementations of a video codec chip
                 H.261 is described demonstrating the effectiveness of
                 the proposed methodology and the capability of the EA
                 to obtain the Pareto points of the design space in a
                 single optimization run.",
  notes =        "Of special interest for this community might be
                 chapter 5 that deals with recombination and bloating in
                 GP YAGPLIC",
}

@InProceedings{blume:2000:ocfromsgesGLEAM,
  author =       "Christian Blume",
  title =        "Optimized Collision Free Robot Move Statement
                 Generation by the Evolutionary Software {GLEAM}",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and George D. Smith
                 and David Corne and Martin Oates and Emma Hart and Pier
                 Luca Lanzi and Egbert Jan Willem and Yun Li and Ben
                 Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "327--328",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Industrial
                 Machining Robots",
  ISBN =         "3-540-67353-9",
  notes =        "Robot command program is a vriable number of very high
                 level command actions.

                 EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM,
                 EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April
                 17, 2000
                 Proceedings

                 http://evonet.dcs.napier.ac.uk/evoworkshops/

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67353-9",
}

@InCollection{bobrovnikoff:2000:SEISP,
  author =       "Dmitri Bobrovnikoff",
  title =        "SoccerBots: Evolving Intelligent Soccer Players",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "40--45",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{boden:1996:tsobGA,
  author =       "Edward B. Boden and Gilford F. Martino",
  title =        "Testing Software using Order-Based Genetic
                 Algorithms",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "461--466",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 GA paper",
}

@InProceedings{boettcher:1999:EOMC,
  author =       "Stefan Boettcher and Allon G. Percus",
  title =        "Extremal Optimization: Methods derived from
                 Co-Evolution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "825--832",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{bohm:1996:eui,
  author =       "Walter Bohm and Andreas Geyer-Schulz",
  title =        "Exact Uniform Initialization for Genetic Programming",
  booktitle =    "Foundations of Genetic Algorithms IV",
  year =         "1996",
  editor =       "Richard K. Belew and Michael Vose",
  pages =        "379--407",
  address =      "University of San Diego, CA, USA",
  publisher_address = "San Francisco, California, USA",
  month =        "3--5 " # aug,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-460-X",
  notes =        "FOGA-4 k-bounded context-free languages May also use
                 key Boehm96 Demonstrated on XOR problem",
}

@InProceedings{bojarczuk:1999:DGP,
  author =       "Celia C. Bojarczuk and Heitor S. Lopes and Alex A.
                 Freitas",
  title =        "Discovering comprehensible classification rules by
                 using Genetic Programming: a case study in a medical
                 domain",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "953--958",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99).
                 See also bojarczuk:2000:kdcp",
}

@Article{bojarczuk:2000:kdcp,
  author =       "Celia C. Bojarczuk and Heitor S. Lopes and Alex A.
                 Freitas",
  title =        "Genetic programming for knowledge discovery in
                 chest-pain diagnosis",
  journal =      "IEEE Engineering in Medicine and Biology Magazine",
  year =         "2000",
  volume =       "19",
  number =       "4",
  pages =        "38--44",
  month =        jul # "-" # aug,
  keywords =     "genetic algorithms, genetic programming, data mining,
                 knowledge discovery, chest-pain diagnosis, predictive
                 accuracy, rule set, comprehensible rules, background
                 knowledge, preprocessing step, data sets",
  ISSN =         "0739-5175",
  URL =          "http://ieeexplore.ieee.org/iel5/51/18543/00853480.pdf",
  abstract =     "Explores a promising data mining approach. Despite the
                 small number of examples available in the authors'
                 application domain (taking into account the large
                 number of attributes), the results of their experiments
                 can be considered very promising. The discovered rules
                 had good performance concerning predictive accuracy,
                 considering both the rule set as a whole and each
                 individual rule. Furthermore, what is more important
                 from a data mining viewpoint, the system discovered
                 some comprehensible rules. It is interesting to note
                 that the system achieved very consistent results by
                 working from {"}tabula rasa,{"} without any background
                 knowledge, and with a small number of examples. The
                 authors emphasize that their system is still in an
                 experiment in the research stage of development.
                 Therefore, the results presented here should not be
                 used alone for real-world diagnoses without consulting
                 a physician. Future research includes a careful
                 selection of attributes in a preprocessing step, so as
                 to reduce the number of attributes (and the
                 corresponding search space) given to the GP. Attribute
                 selection is a very active research area in data
                 mining. Given the results obtained so far, GP has been
                 demonstrated to be a really useful data mining tool,
                 but future work should also include the application of
                 the GP system proposed here to other data sets, to
                 further validate the results reported in this
                 article.",
  notes =        "lilgp",
}

@InProceedings{bojarczuk:2001:idamap,
  author =       "Celia C. Bojarczuk and Heitor S. Lopes and Alex A.
                 Freitas",
  title =        "Data mining with constrained-syntax genetic
                 programming: applications to medical data sets",
  booktitle =    "Proceedings Intelligent Data Analysis in Medicine and
                 Pharmacology (IDAMAP-2001)",
  year =         "2001",
  note =         "a workshop at MedInfo-2001",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 classification",
  URL =          "http://magix.fri.uni-lj.si/idamap2001/papers/bojarczuk.pdf",
  notes =        "IDAMAP workshop
                 http://magix.fri.uni-lj.si/idamap2001/

                 Evolves IFTHEN rules. GP syntax contrained similar to
                 STGP. Size of rules used as component of fitness
                 function (actually product of sensitivity, specificity
                 and size releated coefficient. Demonstrated on 3 small
                 medical datasets (2 UCI).

                 ",
}

@InProceedings{bolis:2001:EuroGP,
  author =       "Enzo Bolis and Christian Zerbi and Pierre Collet and
                 Jean Louchet and Evelyne Lutton",
  title =        "A {GP} Artificial Ant for image processing:
                 preliminary experiments with {EASEA}",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "246--255",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Image
                 processing, Contour detection, EASEA, Animat",
  ISBN =         "3-540-41899-7",
  URL =          "http://www-rocq.inria.fr/fractales/Publications/EuroGPFinal.ps.gz",
  size =         "10 pages",
  abstract =     "This paper describes how animat-based {"}food
                 foraging{"} techniques may be applied to the design of
                 low-level image processing algorithms. First, we show
                 how we implemented the food foraging application using
                 the EASEA software package. We then use this technique
                 to evolve an animat and learn how to move inside images
                 and detect high-gradient lines with a minimum
                 exploration time. The resulting animats do not use
                 standard {"}scanning + filtering{"} techniques but
                 develop other image exploration strategies close to
                 contour tracking. Experimental results on grey level
                 images are presented.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{bollini:1999:dpEAdp,
  author =       "Alessandro Bollini and Marco Piastra",
  title =        "Distributed and Persistent Evolutionary Algorithms: a
                 Design Pattern",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "173--183",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Java objectstore database",
}

@InProceedings{bollini:1999:A,
  author =       "Alessandro Bollini and Marco Piastra",
  title =        "A persistent blackboard for distributed evolutionary
                 computation",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "48--56",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Java",
  notes =        "GECCO-99LB",
}

@InProceedings{bonarini:1999:CRLAACFLCS,
  author =       "Andrea Bonarini",
  title =        "Comparing Reinforcement Learning Algorithms Applied to
                 Crisp and Fuzzy Learning Classifier Systems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "52--59",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{bongard:1999:ECAL,
  author =       "J. C. Bongard",
  title =        "Coevolutionary Dynamics of a Multi-population Genetic
                 Programming System",
  booktitle =    "Advances in Artificial Life",
  year =         "1999",
  editor =       "D. Floreano and J.-D. Nicoud and F. Mondada",
  volume =       "1674",
  series =       "LNAI",
  pages =        "154",
  address =      "Lausanne",
  month =        "13-17 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-66452-1",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66452-1",
  notes =        "ECAL-99",
}

@InProceedings{bongard:2000:legion,
  author =       "Josh C. Bongard",
  title =        "The Legion System: {A} Novel Approach to Evolving
                 Heterogeneity for Collective Problem Solving",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "16--28",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "We investigate the dynamics of agent groups evolved to
                 peform a collective task, and in which the behavioural
                 heterogeneity of the group is under evolutionary
                 control. Two task domains are studied: solutions are
                 evolved for the two tasks using an evolutionary
                 algorithm called the Legion system. A new metric of
                 heterogeneity is also introduced, which measures the
                 heterogeneity of evolved group behaviours. It was found
                 that the amount of heterogeneity evolved in an agent
                 group is dependent on the given problem domain: for the
                 first task, the Legion system evolved heterogeneous
                 groups; for the second task, primarily homogeneous
                 groups evolved. We conclude that the proposed system,
                 in conjunction with the introduced heterogeneity
                 measure, can be used as a tool for investigating
                 various issues concerning redundancy, robustness and
                 division of labour in the context of evolutionary
                 approaches to collective problem solving.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{bonham:1999:AIEEWCOGA,
  author =       "Christopher R. Bonham and Ian C. Parmee",
  title =        "An Investigation of Exploration and Exploitation
                 Within Cluster Oriented Genetic Algorithms ({COGA}s)",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1491--1497",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{boryczka:2002:gecco,
  author =       "Mariusz Boryczka and Zbigniew J. Czech",
  title =        "Solving Approximation Problems By Ant Colony
                 Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "133",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "artificial life, adaptive behavior, agents, ant colony
                 optimization, poster paper, ant colony programming,
                 approximation problems, automatic programming, genetic
                 programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{boryczka:2002:gecco:lbp,
  title =        "Solving Approximation Problems by Ant Colony
                 Programming",
  author =       "Mariusz Boryczka and Zbigniew J. Czech",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "39--46",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming, automatic
                 programming, ant colony programming, approximation
                 problems",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

@InProceedings{bosman:1999:LIPIDEA,
  author =       "Peter A. N. Bosman and Dirk Thierens",
  title =        "Linkage Information Processing In Distribution
                 Estimation Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "60--67",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@MastersThesis{bot:1999:masters,
  author =       "Martijn Bot",
  title =        "Application of Genetic Programming to the Induction of
                 Linear Programming Trees",
  school =       "Vrije Universiteit",
  year =         "1999",
  address =      "Amsterdam, The Netherlands",
  month =        "1 " # jul,
  keywords =     "genetic algorithms, genetic programming, data mining",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/martijn/verslag.ps.gz",
  URL =          "http://www.cs.vu.nl/~mbot/verslag.ps.gz",
  size =         "48 pages",
  notes =        "See also bot:1999:GPilct, bot:2000:GPilct",
}

@InProceedings{bot:1999:GPilct,
  author =       "Martijn Bot and William B. Langdon",
  title =        "Application of Genetic Programming to Induction of
                 Linear Classification Trees",
  booktitle =    "Proceedings of the Eleventh Belgium/Netherlands
                 Conference on Artificial Intelligence (BNAIC'99)",
  year =         "1999",
  editor =       "Eric Postma and Marc Gyssens",
  pages =        "107--114",
  address =      "Kasteel Vaeshartelt, Maastricht, Holland",
  month =        "3-4 " # nov,
  organisation = "BNVKI, Dutch and the Belgian AI Association",
  keywords =     "genetic algorithms, genetic programming, data mining",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/martijn/BNAIC99.bot.18aug99.ps.gz",
  size =         "8 pages",
  notes =        "http://www.cs.unimaas.nl/~bnvki/bnaic99/",
}

@InProceedings{bot:2000:GPilct,
  author =       "Martijn C. J. Bot and William B. Langdon",
  title =        "Application of Genetic Programming to Induction of
                 Linear Classification Trees",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "247--258",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/martijn/bot.eurogp2000.19jan.ps.gz",
  URL =          "http://www.cs.vu.nl/~mbot/mijnpapers/euroGP2000/paper.ps",
  abstract =     "A common problem in datamining is to find accurate
                 classifiers for a dataset. For this purpose, genetic
                 programming (GP) is applied to a set of benchmark
                 classification problems. Using GP we are able to induce
                 decision trees with a linear combination of variables
                 in each function node. A new representation of decision
                 trees using strong typing in GP is introduced. With
                 this representation it is possible to let the GP
                 classify into any number o f classes. Results indicate
                 that GP can be applied successfully to classification
                 problems. Comparisons with current state-of-the-art
                 algorithms in machine learning are presented and areas
                 of future research are identified.",
  notes =        "See also bot:1999:GPilct EuroGP'2000, part of
                 poli:2000:GP",
}

@InProceedings{Bot:2000:GECCO,
  author =       "Martijn C. J. Bot",
  title =        "Improving Induction of Linear Classification Trees
                 with Genetic Programming",
  pages =        "403--410",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/martijn/bot.gecco2000.19jan.ps.gz",
  URL =          "http://www.cs.vu.nl/~mbot/mijnpapers/gecco2000/GP185.ps",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{bot:2001:EuroGP,
  author =       "Martijn C. J. Bot",
  title =        "Feature Extraction for the k-Nearest Neighbour
                 Classifier with Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "256--267",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Feature
                 Extraction, Machine Learning",
  ISBN =         "3-540-41899-7",
  URL =          "http://www.cs.vu.nl/~mbot/mijnpapers/euroGP2001/paper.ps",
  size =         "12 pages",
  abstract =     "In pattern recognition the curse of dimensionality can
                 be handled either by reducing the number of features,
                 e.g. with decision trees or by extraction of new
                 features.

                 We propose a genetic programming (GP) framework for
                 automatic extraction of features with the express aim
                 of dimension reduction and the additional aim of
                 improving accuracy of the k-nearest neighbour (k-NN)
                 classifier. We will show that our system is capable of
                 reducing most datasets to one or two features while
                 k-NN accuracy improves or stays the same. Such a small
                 number of features has the great advantage of allowing
                 visual inspection of the dataset in a two-dimensional
                 plot.

                 Since k-NN is a non-linear classification algorithm, we
                 compare several linear fitness measures. We will show
                 the a very simple one, the accuracy of the minimal
                 distance to means (mdm) classifier outperforms all
                 other fitness measures.

                 We introduce a stopping criterion gleaned from numeric
                 mathematics. New features are only added if the
                 relative increase in training accuracy is more than a
                 constant d, for the mdm classifier estimated to be
                 3.3%.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{bot:2001:fencgp,
  author =       "Martijn C. J. Bot",
  title =        "Feature Extraction for the k-Nearest Neighbour
                 Classifier with Genetic Programming",
  booktitle =    "Graduate Student Workshop",
  year =         "2001",
  editor =       "Conor Ryan",
  pages =        "397--400",
  address =      "San Francisco, California, USA",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}

@InProceedings{Boumaza:2001:EvoWorks,
  author =       "Amine M. Boumaza and Jean Louchet",
  title =        "Dynamic Flies: Using Real-Time Parisian Evolution in
                 Robotics",
  booktitle =    "Applications of Evolutionary Computing",
  year =         "2001",
  editor =       "Egbert J. W. Boers and Stefano Cagnoni and Jens
                 Gottlieb and Emma Hart and Pier Luca Lanzi and Gunther
                 R. Raidl and Robert E. Smith and Harald Tijink",
  volume =       "2037",
  series =       "LNCS",
  pages =        "288--297",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, fly algorithm, robot",
  ISBN =         "3-540-41920-9",
  URL =          "http://www-rocq.inria.fr/fractales/Publications/evoiasp2001_Louchet_Boumaza.ps.gz",
  abstract =     "The Fly algorithm is a Parisian evolution strategy
                 devised for parameter space exploration in computer
                 vision applications, which has been applied to
                 stereovision. The resulting scene model is a set of 3-D
                 points which concentrate upon the surfaces of
                 obstacles. In this paper, we present how the
                 evolutionary scene analysis can be continuously updated
                 and integrated into a specific real-time mobile robot
                 navigation system. Simulation-based experimental
                 results are presented.",
  notes =        "EvoWorkshops2001",
}

@InCollection{bozarth:2000:PCVGP,
  author =       "Bradley J. Bozarth",
  title =        "Programmatic Compression of Video using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "46--53",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{brabazon:2001:AAANZ,
  author =       "Tony Brabazon and M. O'Neill and C. Ryan and J. J.
                 Collins",
  title =        "Uncovering Technical Trading Rules Using Evolutionary
                 Automatic Programming",
  booktitle =    "Proceedings of 2001 AAANZ Conference (Accounting
                 Association of Australia and NZ)",
  year =         "2001",
  address =      "Auckland, New Zealand",
  month =        "1-3 " # jul,
  keywords =     "grammatical evolution, financial prediction, genetic
                 algorithms, genetic programming",
}

@InProceedings{brabazon:2002:EuroGP,
  title =        "Evolving classifiers to model the relationship between
                 strategy and corporate performance using grammatical
                 evolution",
  author =       "Anthony Brabazon and Michael O'Neill and Conor Ryan
                 and Robin Matthews",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "103--112",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  publisher =    "Springer-Verlag",
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "This study examines the potential of grammatical
                 evolution to construct a linear classifier to predict
                 whether a firm's corporate strategy will increase or
                 decrease shareholder wealth. Shareholder wealth is
                 measured using a relative fitness criterion, the change
                 in a firm's market-value-added ranking in the
                 Stern-Stewart Performance 1000 list, over a four year
                 period, 1992-1996. Model inputs and structure are
                 selected by means of grammatical evolution. The best
                 classifier correctly categorised the direction of
                 performance ranking change in 66.38% of the firms in
                 the training set and 65% in the out-of-sample
                 validation set providing support for a hypothesis that
                 changes in corporate strategy are linked to changes in
                 corporate performance.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{brabazon:2002:gecco,
  author =       "Anthony Brabazon and Michael O'Neill and Robin
                 Matthews and Conor Ryan",
  title =        "Grammatical Evolution And Corporate Failure
                 Prediction",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1011--1018",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "real world applications, corporate failure prediction,
                 genetic programming, genotype to phenotype mapping,
                 grammars, grammatical evolution",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{brabazon:2002:gecco:workshop,
  title =        "Trading Foreign Exchange Markets Using Evolutionary
                 Automatic Programming",
  author =       "Tony Brabazon and Michael O'Neill",
  pages =        "133--136",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InCollection{braden:2002:AAPSPGA,
  author =       "Katie Braden",
  title =        "A simple Approach to Protein Structure Prediction
                 using Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "36--44",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@TechReport{brady:murphy,
  author =       "Robert M. Brady and Ross J. Anderson and Robin C.
                 Ball",
  title =        "Murphy's law, the fitness of evolving species, and the
                 limits of software reliability",
  institution =  "Computer Laboratory, Cambridge",
  year =         "1996?",
  email =        "rja14@cl.cam.ac.uk",
  URL =          "http://www.ftp.cl.cam.ac.uk/ftp/users/rja14/babtr.pdf",
  abstract =     "We tackle two problems of interest to the software
                 assurance community. Firstly, existing models of
                 software development (such as the waterfall and spiral
                 models) are oriented towards one-off software
                 development projects, while the growth of mass market
                 computing has led to a world in which most software
                 consists of packages which follow an evolutionary
                 development model. This leads us to ask whether
                 anything interesting and useful may be said about
                 evolutionary development. We answer in the affirmative.
                 Secondly, existing reliability growth models emphasise
                 the Poisson distribution of individual software bugs,
                 while the empirically observed reliability growth for
                 large systems is asymptotically slower than this. We
                 provide a rigorous explanation of this phenomenon. Our
                 reliability growth model is inspired by statistical
                 thermodynamics, but also applies to biological
                 evolution. It is in close agreement with experimental
                 measurements of the fitness of an evolving species and
                 the reliability of commercial software products.
                 However, it shows that there are significant
                 differences between the evolution of software and the
                 evolution of species. In particular, we establish
                 maximisation properties corresponding to Murphy?s law
                 which work to the advantage of a biological species,
                 but to the detriment of software reliability.",
  size =         "11 pages",
  notes =        "cf Bishop96

                 Takes huge liberties, dressing them in maths,

                 {"}the number of defects which survive a selection
                 process is maximised{"}

                 {"}debugging removes the minimum possible number of
                 bugs that must be removed in order to pass the test
                 sequence{"}.

                 {"}we have a dsitribution of deffects that e behaves
                 statisically as if they were in thermal equilibrium at
                 this{"} [1/t] {"}temperature{"}.",
}

@InProceedings{brameier:1999:PMCGP,
  author =       "Markus Brameier and Frank Hoffmann and Peter Nordin
                 and Wolfgang Banzhaf and Frank Francone",
  title =        "Parallel Machine Code Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1228",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{Brameier:2001:TEC,
  author =       "Markus Brameier and Wolfgang Banzhaf",
  title =        "A Comparison of Linear Genetic Programming and Neural
                 Networks in Medical Data Mining",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2001",
  volume =       "5",
  number =       "1",
  pages =        "17--26",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ls11-www.informatik.uni-dortmund.de/people/banzhaf/ieee_taec.pdf",
  abstract =     "We apply linear genetic programming to several
                 diagnosis problems in medicine. An efficient algorithm
                 is presented that eliminates intron code in linear
                 genetic programs. This results in a significant speedup
                 which is especially interesting when operating with
                 complex datasets as they are occuring in real-world
                 applications like medicine. We compare our results to
                 those obtained with neural networks and argue that
                 genetic programming is able to show similar performance
                 in classification and generalization even within a
                 relatively small number of generations.",
  notes =        "proben1/UCI LGP variable length string of C
                 instruction. Branching. steady state tournament
                 selection. two-point string crossover {"}high mutation
                 rates have been experienced to produced better
                 results{"} p19. Size<=256 {"}it is much easier for the
                 GP system to implement structural introns [than
                 semantic ones]{"} p20 {"}for all problems discussed,
                 the performance of GP in generalization comes close to
                 or even better then the results documented for NNs{"}
                 (MLP, RPROP) p21 Ten demes of 500 connected in one
                 direction circle. 5% mutation rate. {"}On average, the
                 number of effective generations is reduced by a factor
                 of three when using demes. Tests with and without
                 conditionals. Runtime comparison.",
}

@Article{brameier:2001:GPEM,
  author =       "Markus Brameier and Wolfgang Banzhaf",
  title =        "Evolving Teams of Predictors with Linear Genetic
                 Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "4",
  pages =        "381--407",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, evolution of
                 teams, combination of multiple predictors, linear
                 genetic programming",
  ISSN =         "1389-2576",
  abstract =     "This paper applies the evolution of GP teams to
                 different classification and regression problems and
                 compares different methods for combining the outputs of
                 the team programs. These include hybrid approaches
                 where (1) a neural network is used to optimize the
                 weights of programs in a team for a common decision and
                 (2) a real numbered vector (the representation of
                 evolution strategies) of weights is evolved with each
                 term in parallel. The cooperative team approach results
                 in an improved training and generalization performance
                 compared to the standard GP method. The higher
                 computational overhead of team evolution is
                 counteracted by using a fast variant of linear GP.",
}

@InProceedings{brameier:2002:EuroGP,
  title =        "Explicit Control of Diversity and Effective Variation
                 Distance in Linear Genetic Programming",
  author =       "Markus Brameier and Wolfgang Banzhaf",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  publisher =    "Springer-Verlag",
  volume =       "2278",
  series =       "LNCS",
  pages =        "37--49",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "We have investigated structural distance metrics for
                 linear genetic programs. Causal connections between
                 changes of the genotype and changes of the phenotype
                 form a necessary condition for analyzing structural
                 differences between genetic programs and for the two
                 objectives of this paper: (i) The distance information
                 between individuals is used to control structural
                 diversity of population individuals actively by a
                 two-level tournament selection. (ii) Variation distance
                 of effective code is controlled for different genetic
                 operators - including a mutation operator that works
                 closely with the applied distance measure. Numerous
                 experiments have been performed for three benchmark
                 problems.",
  notes =        "EuroGP'2002, part of lutton:2002:GP Best paper",
}

@InProceedings{branke:1999:RGDSSEA,
  author =       "Jurgen Branke and Massimo Cutaia and Heinrich Dold",
  title =        "Reducing Genetic Drift in Steady State Evolutionary
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "68--74",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{brave:1994:recursive,
  author =       "Scott Brave",
  title =        "Evolution of Planning: Using recursive techniques in
                 Genetic Planning",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "1--10",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{brave:1994:recursiveGW,
  author =       "Scott Brave",
  title =        "Using Genetic Programming to Evolve Recursive Programs
                 for Tree Search",
  booktitle =    "Fourth Golden West Conference on Intelligent Systems",
  year =         "1995",
  editor =       "S. Louis",
  pages =        "60--65",
  publisher_address = "San Francisco, California, USA",
  month =        "12-14 " # jun,
  publisher =    "International Society for Computers and their
                 Applications - ISCA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-880843-12-9",
  notes =        "ISCA-GW-95",
}

@InProceedings{brave:1994:mmGW,
  author =       "Scott Brave",
  title =        "Using Genetic Programming to Evolve Mental Models",
  booktitle =    "Fourth Golden West Conference on Intelligent Systems",
  year =         "1995",
  editor =       "S. Louis",
  pages =        "91--96",
  publisher_address = "San Francisco, California, USA",
  month =        "12-14 " # jun,
  publisher =    "International Society for Computers and their
                 Applications - ISCA",
  keywords =     "genetic algorithms, genetic programming, memory",
  ISBN =         "1-880843-12-9",
  notes =        "ISCA-GW-95",
}

@InCollection{brave:1996:aigp2,
  author =       "Scott Brave",
  title =        "Evolving Recursive Programs for Tree Search",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "203--220",
  chapter =      "10",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  notes =        "Recursive ADFs, non-recursive ADFs and non-ADF GP
                 compared on a tree searching problem. Tree depths 2-7
                 (ie up to 127 leaf nodes) containing one goal node.
                 Problem arranged so can only be solved (by luck?) or by
                 using memory. READ+WRITE update a single memory cell
                 per tree node, ie no index, just access current cell.
                 WRITE not as Teller but returns its argument. ADF1 and
                 ADF2 syntax set up so one can search tree and one can
                 move within it, cf. Andre.

                 Recursive ADFs much better than ADFs much better than
                 non-ADFs, difference increase as tree size increases.
                 {"}random{"}? program search can find recursive ADF
                 programs which solve problem.",
  size =         "17 pages",
}

@InProceedings{brave:1996:dface,
  author =       "Scott Brave",
  title =        "Evolving Deterministic Finite Automata Using Cellular
                 Encoding",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "39--44",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96 DGPC {"}inremental growth of finite automata
                 from an initial single-state zygote{"}, {"}Induced
                 automata to recognise several different (formal)
                 languages{"} eg Tomita {"}applies cellular encoding to
                 the evolution of determistic finite (state)
                 automata.{"}",
}

@InProceedings{brave:1996:emmmGP,
  author =       "Scott Brave",
  title =        "The Evolution of Memory and Mental Models Using
                 Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms, memory",
  pages =        "261--266",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96. cf. brave:1994:mmGW

                 ",
}

@Proceedings{brave:1999:gecco99lb,
  editor =       "Scott Brave and Annie S. Wu",
  title =        "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, Genetic Programming, Evolutionary
                 Programming, fuzzy rules",
  size =         "311 pages",
  notes =        "GECCO-99LB",
}

@InProceedings{breeden:1999:UJE,
  author =       "Joseph L. Breeden and Todd W. Allen",
  title =        "Using an optimization toolkit for Java to evolve
                 market strategies for European seeds",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "57--64",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@InCollection{breunig:1995:LIPRGP,
  author =       "Markus M. Breunig",
  title =        "Location Independent Pattern Recognition using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "29--38",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@Article{Brezocnik:2001:MPT,
  author =       "Miran Brezocnik and Joze Balic and Z. Kampus",
  title =        "Modeling of forming efficiency using genetic
                 programming",
  journal =      "Journal of Materials Processing Technology",
  volume =       "109",
  pages =        "20--29",
  year =         "2001",
  number =       "1-2",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6TGJ-423HM9M-5/1/bcc93a13fbb04521236d3a8e16f8850b",
  abstract =     "This paper proposes new approach for modeling of
                 various processes in metal-forming industry. As an
                 example, we demonstrate the use of genetic programming
                 (GP) for modeling of forming efficiency. The forming
                 efficiency is a basis for determination of yield stress
                 which is the fundamental characteristic of metallic
                 materials. Several different genetically evolved models
                 for forming efficiency on the basis of experimental
                 data for learning were discovered. The obtained models
                 (equations) differ in size, shape, complexity and
                 precision of solutions. In one run out of many runs of
                 our GP system the well-known equation of Siebel was
                 obtained. This fact leads us to opinion that GP is a
                 very powerful evolutionary optimization method
                 appropriate not only for modeling of forming efficiency
                 but also for modeling of many other processes in
                 metal-forming industry.",
}

@Article{Brezocnik:2001:RCIM,
  author =       "Miran Brezocnik and Joze Balic",
  title =        "genetic-based approach to simulation of
                 self-organizing assembly",
  journal =      "Robotics and Computer-Integrated Manufacturing",
  volume =       "17",
  pages =        "113--120",
  year =         "2001",
  number =       "1-2",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V4P-42DP1Y1-J/1/175033beb3ddb787b75c22253e5534c2",
  abstract =     "The paper proposes a new and innovative biologically
                 oriented idea in conceiving intelligent systems in
                 modern factories of the future. The intelligent system
                 is treated as an autonomous organization structure
                 efficiently adapting itself to the dynamic changes in
                 the production environment and the environment in a
                 wider sense. Simulation of self-organizing assembly of
                 mechanical parts (basic components) into the product is
                 presented as an example of the intelligent system. The
                 genetic programming method is used. The genetic-based
                 assembly takes place on the basis of the genetic
                 content in the basic components and the influence of
                 the environment. The evolution of solutions happens in
                 a distributed way, nondeterministically, bottom-up, and
                 in a self-organizing manner. The paper is also a
                 contribution to the international research and
                 development program intelligent manufacturing systems,
                 which is one of the biggest projects ever introduced.",
}

@InProceedings{brizuela:1999:ADSGAJSSP,
  author =       "Carlos A. Brizuela and Nobuo Sannomiya",
  title =        "A Diversity Study in Genetic Algorithms for Job Shop
                 Scheduling Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "75--82",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{brock:1994:ers,
  author =       "Oliver Brock",
  title =        "Evolving Reusable Subroutines for Genetic
                 Programming",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "11--19",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html

                 ADFS previously evolved may be used by subsequent GP
                 runs. Ie become part of fitness set for rpb and adf of
                 later runs.",
}

@TechReport{broughton:1998:e3DwlsGPwww,
  author =       "T. Broughton and P. Coates and H. Jackson",
  title =        "Exploring 3{D} design worlds using Lindenmayer systems
                 and Genetic Programming",
  institution =  "University of East London",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://homepages.uel.ac.uk/0483p/chapter12.html",
  notes =        "www info only",
}

@InCollection{broughton:1999:e3DwlsGPwww,
  author =       "T. Broughton and P. S. Coates and H. Jackson",
  title =        "Exploring Three-dimensional design worlds using
                 Lindenmeyer Systems and Genetic Programming",
  booktitle =    "Evolutionary Design Using Computers",
  publisher =    "Academic press",
  year =         "1999",
  editor =       "Peter Bentley",
  chapter =      "14",
  address =      "London, UK",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-12-089070-4",
  URL =          "http://www.cs.ucl.ac.uk/staff/P.Bentley/evdes.html",
  abstract =     "The raw Lindenmeyer-system (L-system) generates random
                 branching structures in the isospatial grid. Using a
                 three dimensional L-system, early experiments (reported
                 CAAD Futures 97, coates:1997:GPx3dw ) showed that
                 globally defined useful form (the flytrap) can evolve
                 quite quickly using one fitness function This paper
                 will describe further experiments undertaken using an
                 improved L-system and multigoal evolution to evolve
                 space/enclosure systems that satisfy both the
                 requirements of space use and those of enclosure. This
                 is implemented as symbiotic coevolution between: 1)
                 L-system branching tree system whose goal is to
                 surround the largest volume of empty space (defined as
                 space which is {"}invisible{"} to an outside observer).
                 2) Circulation system utilising walking three
                 dimensional turtles to measure the spatial property of
                 the enclosed space. The resulting enclosure phenotypes
                 can be realised using the occupied isospatial grid
                 points as nodes of a nurbs surface.

                 The chapter covers:

                 1.0 Introduction to Genetic Programming, L-Systems and
                 the Isospatial Grid

                 2.0 Three dimensional L-systems, production rules and
                 s- expressions

                 3.0 Evolutionary Experiments in Simple Environments

                 4.0 Symbiotic Coevolution",
  notes =        "

                 ",
}

@Article{brown:1997:GPsoccer,
  author =       "Janelle Brown",
  title =        "{AI}, Teamwork is Goal of Robot Soccer Tourney",
  journal =      "Wired News",
  year =         "1997",
  volume =       "5",
  number =       "10",
  month =        "3:04pm PDT 26 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.wired.com/news/news/culture/story/6388.html",
  size =         "1 page",
  notes =        "Report on RoboCup robot competition (held at IJCAI
                 1997 in Nagoya, Japan) http://www.robocup.org/RoboCup/
                 see also luke:1997:csstcGP and
                 http://www.cs.umd.edu/users/seanl/soccerbots/",
}

@InProceedings{browncribbs:1996:nand,
  author =       "H. {Brown Cribbs III} and Robert E. Smith",
  title =        "Classifier System Renaissance: New Analogies, New
                 Directions",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Classifier Systems, Genetic Algorithms",
  pages =        "547--552",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 Classifier paper",
}

@Misc{browne:1996:bsc,
  author =       "David Browne",
  title =        "Vision-Based Obstacle Avoidance: {A} Coevolutionary
                 Approach",
  school =       "Department of Software Development, Monash
                 University",
  year =         "1996",
  type =         "Bachelor of Computing with Honours",
  address =      "Australia",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.csse.monash.edu.au/hons/projects/1996/David.Browne/",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/browne/browne_thesis.ps.gz",
  size =         "147 pages",
  abstract =     "This thesis investigates the design of robust obstacle
                 avoidance strategies. Specifically, simulated
                 coevolution is used to breed steering agents and
                 obstacle courses in a `computational arms race'. Both
                 steering agent strategies and obstacle courses are
                 represented by computer programs, and are coevolved
                 according to the genetic programming paradigm.

                 Previous research has found it difficult to evolve
                 robust vision based obstacle avoidance agents. By
                 independently evolving obstacle avoidance agents
                 against a competing evolving species (ie the obstacle
                 courses), it is hypothesised that the robustness of the
                 agents will be increased.

                 The simon system, an existing genetic programming tool,
                 is modified and used to evolve both the obstacle
                 avoidance agents and the obstacle courses. A comparison
                 is made between the robustness of coevolved obstacle
                 avoidance agents and traditionally evolved
                 (non-coevolved) agents. Robustness is measured by
                 average performance in a series of randomly generated
                 obstacle courses.

                 Experimental results show that the average robustness
                 of the coevolved oa agents is greater than that of the
                 traditionally evolved, and statistically it is shown
                 that this data is representative of all cases.

                 It is therefore concluded that coevolution is
                 applicable to oa type problems, and can be used to
                 evolve more robust, general purpose Vision-Based
                 Obstacle Avoidance agents.",
}

@InProceedings{bruce:1996:agOOpGP,
  author =       "Wilker Shane Bruce",
  title =        "Automatic Generation of Object-Oriented Programs Using
                 Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms, memory",
  pages =        "267--272",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96 Early version available from
                 http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-96.ps

                 Uses GP to induce stack, queue and P queue. Represents
                 objects as array of trees, one per method. Mutation and
                 crossover. {"}Strongly typed GP generally out performed
                 untyped GP as was expected{"}. STGP. Says details in
                 bruce:thesis.",
}

@PhdThesis{bruce:thesis,
  author =       "Wilker Shane Bruce",
  title =        "The Application of Genetic Programming to the
                 Automatic Generation of Object-Oriented Programs",
  school =       "School of Computer and Information Sciences, Nova
                 Southeastern University",
  year =         "1995",
  address =      "3100 SW 9th Avenue, Fort Lauderdale, Florida 33315,
                 USA",
  month =        Dec,
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.scis.nova.edu/~brucews/PUBLICATIONS/thesis.ps",
  url_2 =        "ftp://cs.ucl.ac.uk/bruce.thesis.ps.gz",
  size =         "664 pages",
}

@InProceedings{bruce:1997:lprsbGPADF,
  author =       "Wilker Shane Bruce",
  title =        "The Lawnmower Problem Revisited: Stack-Based Genetic
                 Programming and Automatically Defined Functions",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "52--57",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-97.ps",
  size =         "6 pages",
  notes =        "GP-97 Zero fitness if attempts to pop empty stack.
                 LEFT primitive removed from population. ARG0 never in
                 best best of run. {"}SBGP required significantly more
                 search than tree-based GP{"} {"}comparisons ... may be
                 problem dependant{"}. {"}In both systems [GP and SBGP]
                 the use of ADFs appreciably improved the ability of the
                 GP system to quickly find a solution to the [lawn
                 mower] problem.{"} failure of SBGP without ADFs to
                 solve 8x12 {"}is most probably due to our limit of a
                 maximium of 256 elements in a solution{"}.",
}

@InProceedings{brucherseifer:2001:EuroGP,
  author =       "Eva Brucherseifer and Peter Bechtel and Stephan Freyer
                 and Peter Marenbach",
  title =        "An Indirect Block-Oriented Representation for Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "268--279",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming,
                 Block-oriented representation, Biotechnology, Process
                 modelling, Controller design, Causality",
  ISBN =         "3-540-41899-7",
  size =         "12 pages",
  abstract =     "When Genetic Programming (GP) is applied to system
                 identification or controller design different codings
                 can be used for internal representation of the
                 individuals. One common approach is a block-oriented
                 representation where nodes of the tree structure
                 directly correspond to blocks in a block diagram. In
                 this paper we present an indirect block-oriented
                 representation, which adopts some aspects of the way
                 humans perform the modelling in order to increase the
                 GP system's performance. A causality measure based on
                 an edit distance is examined to compare the direct an
                 the indirect representation. Finally, results from a
                 real world application of the indirect block-oriented
                 representation are presented.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@Article{bruhn:2002:ECJ,
  author =       "Peter Bruhn and Andreas Geyer-Schulz",
  title =        "Genetic Programming over Context-Free Languages with
                 Linear Constraints for the Knapsack Problem: First
                 Results",
  journal =      "Evolutionary Computation",
  year =         "2002",
  volume =       "10",
  number =       "1",
  pages =        "51--74",
  month =        "Spring",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, grammar-based genetic, programming,
                 combinatorial, optimization, context-free grammars,
                 with linear constraints, knapsack problems",
}

@InCollection{Bui:1997:s8p,
  author =       "Thai Bui",
  title =        "Solving the 8-Puzzle with Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "11--17",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{bull:1996:SandS,
  author =       "Lawrence Bull and Terence C Fogarty",
  title =        "Evolutionary Computing in Multi-Agent Environments:
                 Speciation and Symbiogenesis",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  booktitle =    "Parallel Problem Solving From Nature IV. Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  publisher =    "Springer-Verlag",
  volume =       "1141",
  series =       "LNCS",
  pages =        "12--21",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  keywords =     "genetic algorithms",
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
                 Wall climbing quadruped robot simulation",
}

@InProceedings{Bull:1997:ecmaee,
  author =       "Larry Bull and Owen Holland",
  title =        "Evolutionary Computing in Multi-Agent Environments:
                 Eusociality",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "347--352",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{bull:1999:OZSCDM,
  author =       "Larry Bull",
  title =        "On using {ZCS} in a Simulated Continuous
                 Double-Auction Market",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "83--90",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{Burgess:2001:IST,
  author =       "Colin J. Burgess and Martin Lefley",
  title =        "Can genetic programming improve software effort
                 estimation? comparative evaluation",
  year =         "2001",
  journal =      "Information and Software Technology",
  volume =       "43",
  number =       "14",
  pages =        "863--873",
  abstract =     "Accurate software effort estimation is an important
                 part of the software process. Originally, estimation
                 was performed using only human expertise, but more
                 recently, attention has turned to a variety of machine
                 learning (ML) methods. This paper attempts to evaluate
                 critically the potential of genetic programming (GP) in
                 software effort estimation when compared with
                 previously published approaches, in terms of accuracy
                 and ease of use. The comparison is based on the
                 well-known Desharnais data set of 81 software projects
                 derived from a Canadian software house in the late
                 1980s. The input variables are restricted to those
                 available from the specification stage and significant
                 effort is put into the GP and all of the other solution
                 strategies to offer a realistic and fair comparison.
                 There is evidence that GP can offer significant
                 improvements in accuracy but this depends on the
                 measure and interpretation of accuracy used. GP has the
                 potential to be a valid additional tool for software
                 effort estimation but set up and running effort is high
                 and interpretation difficult, as it is for any complex
                 meta-heuristic technique.",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V0B-44D4196-7/1/20f45986fc0a4827ad09169178379d73",
}

@TechReport{burgess:1999:faasdeGP,
  author =       "Glenn Burgess",
  title =        "Finding Approximate Analytic Solutions To Differential
                 Equations Using Genetic Programming",
  institution =  "Surveillance Systems Division, Defence Science and
                 Technology Organisation, Australia",
  month =        Feb,
  year =         "1999",
  type =         "Technical Report",
  number =       "DSTO-TR-0838",
  address =      "Salisbury, SA, 5108, Austrlia",
  notes =        "Based on author's 1997 Dept. Phys. Honours Thesis,
                 Flinders University of South Australia",
  keywords =     "genetic algorithms, genetic programming, differential
                 equations",
  URL =          "http://203.36.224.190/cgi-bin/dsto/extract.pl?DSTO-TR-0838",
  URL =          "http://www.dsto.defence.gov.au/corporate/reports/DSTO-TR-0838.pdf",
  abstract =     "The computational optimisation technique, genetic
                 programming, is applied to the analytic solution of
                 general differential equations. The approach generates
                 a mathematical expression that is an approximate or
                 exact solution to the particular equation under
                 consideration. The technique is applied to a number of
                 differential equations of increasing complexity in one
                 and two dimensions. Comparative results are given for
                 varying several parameters of the algorithm such as the
                 size of the calculation stack and the variety of
                 available mathematical operators. Several novel
                 approaches gave negative results. Angeline's module
                 acquisition (MA) and Koza's automatically defined
                 functions (ADF) are considered and the results of some
                 modifications are presented. One result of significant
                 theoretical interest is that the syntax-preserving
                 crossover used in Genetic Programming may be
                 generalised to allow the exchange of n-argument
                 functions without adverse effects.

                 The results show that Genetic Programming is an
                 effective technique that can give reasonable results,
                 given plenty of computing resources. The technique used
                 here can be applied to higher dimensions; although in
                 practice the algorithmic complexity may be too high.",
  size =         "73 pages",
}

@InCollection{burjorjee:1999:GAGGS,
  author =       "Keki M. Burjorjee",
  title =        "Genetic Algorithms Go to Grade School",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "31--40",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{burke:2002:gecco,
  author =       "Edmund Burke and Steven Gustafson and Graham Kendall",
  title =        "A Survey And Analysis Of Diversity Measures In Genetic
                 Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "716--723",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, diversity,
                 population diversity, population dynamics",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)

                 Nominated for best at GECCO award",
}

@Article{burk:1998:pmgGA,
  author =       "Donald S. Burke and Kenneth A. De Jong and John J.
                 Grefenstette and Connie Loggia Ramsey and Annie S. Wu",
  title =        "Putting More Genetics into Genetic Algorithms",
  journal =      "Evolutionary Computation",
  year =         "1998",
  volume =       "6",
  number =       "4",
  pages =        "387--410",
  month =        "Winter",
  keywords =     "genetic algorithms, Models of viral evolution,
                 variable-length representation, length penalty
                 functions, genome length adaptation, noncoding regions,
                 duplicative genes",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=6&issue=4",
  abstract =     "The majority of current genetic algorithms (GAs),
                 while inspired by natural evolutionary systems, are
                 seldom viewed as biologically plausible models. This is
                 not a criticism of GAs, but rather a reflection of
                 choices made regarding the level of abstraction at
                 which biological mechanisms are modeled, and a
                 reflection of the more engineering-oriented goals of
                 the evolutionary computation community. Understanding
                 better and reducing this gap between GAs and genetics
                 has been a central issue in an interdisciplinary
                 project whose goal is to build GA-based computational
                 models of viral evolution. The result is a system
                 called Virtual Virus (VIV). The VIV incorporates a
                 number of more biologically plausible mechanisms,
                 including a more flexible genotype-to-phenotype
                 mapping. In VIV the genes are independent of position,
                 and genomes can vary in length and may contain
                 noncoding regions, as well as duplicative or competing
                 genes.

                 Initial computational studies with VIV have already
                 revealed several emergent phenomena of both biological
                 and computational interest. In the absence of any
                 penalty based on genome length, VIV develops
                 individuals with long genomes and also performs more
                 poorly (from a problem-solving viewpoint) than when a
                 length penalty is used. With a fixed linear length
                 penalty, genome length tends to increase dramatically
                 in the early phases of evolution and then decrease to a
                 level based on the mutation rate. The plateau genome
                 length (i.e., the average length of individuals in the
                 final population) generally increases in response to an
                 increase in the base mutation rate. When VIV converges,
                 there tend to be many copies of good alternative genes
                 within the individuals. We observed many instances of
                 switching between active and inactive genes during the
                 entire evolutionary process. These observations support
                 the conclusion that noncoding regions serve a positive
                 step in understanding how GAs might exploit more of the
                 power and flexibility of biological evolution while
                 simultaneously providing better tools for understanding
                 evolving biological systems.",
  notes =        "Special Issue: Variable-Length Representation and
                 Noncoding Segments for Evolutionary Algorithms Edited
                 by Annie S. Wu and Wolfgang Banzhaf",
}

@Misc{burk:1998:pmgGAx,
  author =       "Donald S. Burke and Kenneth A. De Jong and John J.
                 Grefenstette and Connie Loggia Ramsey and Annie S. Wu",
  title =        "Putting More Genetics into Genetic Algorithms",
  howpublished = "preprint of burk:1998:pmgGA",
  year =         "1998",
  month =        "19 " # oct,
  keywords =     "genetic algorithms, Models of viral evolution,
                 variable-length representation, length penalty
                 functions, genome length adaptation, noncoding regions,
                 duplicative genes",
  URL =          "http://www.ib3.gmu.edu/gref/papers/burke-ec98.ps",
  size =         "pages",
}

@InProceedings{burke:ppsn2002:pp341,
  author =       "Edmund Burke and Steven Gustafson and Graham Kendall
                 and Natalio Krasnogor",
  title =        "Advanced Population Diversity Measures in Genetic
                 Programming",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "341 ff.",
  year =         "2002",
  editor =       "J.-J. Merelo Guerv\'os and P. Adamidis and H.-G. Beyer
                 and J.-L. Fern\'andez-Villaca\~nas and H.-P. Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Keywords: Technique::Advanced techniques -
                 miscellaneous, Technique::Genetic programming -
                 general, Theory of EC::Evolution dynamics",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900341.pdf",
}

@InProceedings{busch:2002:EuroGP,
  title =        "Automatic Generation of Control Programs for Walking
                 Robots Using Genetic Programming",
  author =       "Jens Busch and Jens Ziegler and Wolfgang Banzhaf and
                 Andree Ross and Daniel Sawitzki and Christian Aue",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "258--267",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "We present the system SIGEL that combines the
                 simulation and visualization of robots with a Genetic
                 Programming system for the automated evolution of
                 walking. It is designed to automatically generate
                 control programs for arbitrary robots without depending
                 on detailed analytical information of the robots'
                 kinematic structure. Different fitness functions as
                 well as a variety of parameters allow the easy and
                 interactive configuration and adaptation of the
                 evolution process and the simulations.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@TechReport{butler:1995:eddie,
  author =       "James M. Butler and Edward P. K. Tsang",
  title =        "{EDDIE} Beats the Bookies",
  institution =  "Computer Science, University of Essex",
  year =         "1995",
  type =         "Technical Report",
  number =       "CSM-259",
  address =      "Colchester CO4 3SQ, UK",
  month =        "15 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://cswww.essex.ac.uk/CSP/edward/finance/CSM-259.ps.Z",
  notes =        "

                 EDDIE, which stands for Evolutionary Dynamic Data
                 Investment Evaluator, is designed as a tool to help
                 channelling expert's knowledge into computer programs
                 for making rules, which can then be examined by experts
                 and used by other people. EDDIE is based on the concept
                 of Genetic Programming, which borrows its ideas from
                 evolution. EDDIE been applied to real horse races. We
                 used the first 150 handicap races results in 1993
                 together with the expert knowledge that we could find
                 from a text on horse racing to train EDDIE, which
                 generates rules about betting. These rules were used to
                 bet on the remaining 30 races in that season and
                 obtained 88% return on investment. As scientists, we
                 should always be cautious about experimental results.
                 The sample size is small and luck may have a part to
                 play in the success of EDDIE. However, the results
                 justifies the investment of more time and effort into
                 this research, which is what we are doing. See also
                 tsang:1998:eddie",
}

@Article{buxton:2001:MC,
  author =       "B. F. Buxton and W. B. Langdon and S. J. Barrett",
  title =        "Data Fusion by Intelligent Classifier Combination",
  journal =      "Measurement and Control",
  year =         "2001",
  editor =       "Qing-Ping Yang",
  volume =       "34",
  number =       "8",
  pages =        "229--234",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/mc/",
  abstract =     "The use of hybrid intelligent systems in industrial
                 and commercial applications is briefly reviewed. The
                 potential for application of such systems, in
                 particular those that combine results from several
                 constituent classifiers, to problems in drug design is
                 discussed. It is shown that, although there are no
                 general rules as to how a number of classifiers should
                 best be combined, effective combinations can
                 automatically be generated by genetic programming (GP).
                 A robust performance measure based on the area under
                 classifier receiver-operating-characteristic (ROC)
                 curves is used as a fitness measure in order to
                 facilitate evolution of multi-classifier systems that
                 outperform their constituent individual classifiers.
                 The approach is illustrated by application to publicly
                 available Landsat data and to pharmaceutical data of
                 the kind used in one stage of the drug design
                 process.",
  notes =        "http://www.instmc.org.uk/pubs/measandcontrol.htm
                 {"}Measurement + Control is neither a {"}learned{"}
                 journal nor a commercial trade publication{"} feature
                 issue of M&C on Signal Processing

                 Awarded best paper prize by Worshipful Company of
                 Instrument Makers.",
}

@Misc{buxton:2002:rocket,
  author =       "B. F. Buxton and S B Holden and P C Treleaven",
  title =        "Intelligent Data Analysis and Fusion Techniques in
                 Pharmaceuticals, Bioprocessing and Process Control",
  year =         "2002",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, boosting,
                 support vector machines",
  URL =          "http://www.cs.ucl.ac.uk/research/rocket/private/epsrc.htm",
  notes =        "End of project report. INTErSECT Faraday Partnership
                 Flagship Project, 4 January 1999- 3 July 2002 Grant
                 Reference GR/M43975",
}

@InProceedings{calderoni:1998:GPadsar,
  author =       "Stephane Calderoni and Pierre Marcenac",
  title =        "Genetic Programming For Automatic Design Of
                 Self-Adaptive Robots",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "163--177",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "The general framework tackled in this paper is the
                 automatic generation of intelligent collective
                 behaviors using genetic programming and reinforcement
                 learning. We define a behavior-based system relying on
                 automatic design process using artificial evolution to
                 synthesize high level behaviors for autonomous agents.
                 Behavioral strategies are described by tree-based
                 structures, and manipulated by genetic evolving
                 processes. Each strategy is dynamically evaluated
                 during simulation, and weighted by an adaptative value.
                 This value is a quality factor that reflects the
                 relevance of a strategy as a good solution for the
                 learning task. It is computed using heterogeneous
                 reinforcement techniques associating immediate and
                 delayed reinforcements as dynamic progress estimators.
                 This work has been tested upon a canonical
                 experimentation framework: the foraging robots problem.
                 Simulations have been conducted and have produced some
                 promising results.",
  notes =        "EuroGP'98",
}

@InProceedings{calderoni:1999:BCSMD,
  author =       "Stephane Calderoni",
  title =        "Behavior-Based Control System in MultiAgent Domain",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1439",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming,artificial
                 life, adaptive behavior and agents, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{campbell:2000:EGPDROR,
  author =       "Elliott Campbell",
  title =        "Evaluation of Genetic Programming for Determining
                 Reservoir Operating Rules",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "54--59",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{cangelosi:1999:HADNN,
  author =       "Angelo Cangelosi",
  title =        "Heterochrony and Adaptation in Developing Neural
                 Networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1241--1248",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Cantu-Paz:1997:mibcpGA,
  author =       "Erick Cantu-Paz and David E. Goldberg",
  title =        "Modeling Idealized Bounding Cases of Parallel Genetic
                 Algorithms",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "353--361",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{cantu:1998:demsPGA,
  author =       "Erick Cantu-Paz",
  title =        "Designing Efficient Master-Slave Parallel Genetic
                 Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "455",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{cantu:1998:mcabcPGA,
  author =       "Erick Cantu-Paz",
  title =        "Using Markov Chains to Analyze a Bounding Case of
                 Parallel Genetic Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "456--462",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{cantu-paz:1999:MPTTGA,
  author =       "Erick Cantu-Paz",
  title =        "Migration Policies and Takeover Times in Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "775",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cantu-paz:1999:TMRMPGA,
  author =       "Erick Cantu-Paz",
  title =        "Topologies, Migration Rates, and Multi-Population
                 Parallel Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "91--98",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cantu-paz:1999:M,
  author =       "Erick Cantu-Paz",
  title =        "Migration policies, selection pressure, and parallel
                 evolutionary algorithms",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "65--73",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@Proceedings{cantu-paz:2002:gecco:lbp,
  title =        "Late Breaking papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  publisher =    "AAAI",
  address =      "New York, NY",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "Genetic Algorithm, Genetic Programming, Evolvable
                 Network Architecture , Dynamic Neural Net, Pattern
                 Recognition , Evolutionary Computation, Automated
                 Sensor, Multiagent Systems, Optimisation, Evolvable
                 Hardware , Genetic Multi-Agent Planning, Evolutionary
                 Testing, Evolving Neural Network Architectures,
                 Evolving Software, Airline Fleet Assignment, Ant Colony
                 Algorithm, Artificial Immune System , Artificial Life,
                 Evolving Cellular Automata",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002})",
}

@InProceedings{cao:1998:2eaode,
  author =       "Hongqing Cao and Lishan Kang and Zbigniew Michalewicz
                 and Yuping Chen",
  title =        "A Two-level Evolutionary Algorithm for Modeling System
                 of Ordinary Differential Equations",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "17--22",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{cao:1999:CMSUNN,
  author =       "Lijuan Cao and Tay Eng Hock (Francis) and Ma Lawrence
                 and Wai Cheong Yeong",
  title =        "Classification of the Market States Using Neural
                 Network",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "776",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cao:1999:EMODEDS,
  author =       "Hongqing Cao and Lishan Kang and Yuping Chen",
  title =        "Evolutionary Modeling of Ordinary Differential
                 Equations for Dynamic Systems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "959--965",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cao:1999:NBMCANACS,
  author =       "Lijuan Cao and Tay Eng Hock (Francis)",
  title =        "Neuro-Genetic Based Method to the Classification of
                 Acupuncture Needle: {A} Case Study",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "99--105",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{cao:2000:odeGP,
  author =       "Hongqing Cao and Lishan Kang and Yuping Chen and
                 Jingxian Yu",
  title =        "Evolutionary Modeling of Systems of Ordinary
                 Differential Equations with Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "4",
  pages =        "309--337",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 modeling, system of ordinary differential equations,
                 higher-order ordinary differential equation",
  ISSN =         "1389-2576",
  abstract =     "This paper describes an approach to the evolutionary
                 modeling problem of ordinary differential equations
                 including systems of ordinary differential equations
                 and higher-order differential equations. Hybrid
                 evolutionary modeling algorithms are presented to
                 implement the automatic modeling of one- and
                 multi-dimensional dynamic systems respectively. The
                 main idea of the method is to embed a genetic algorithm
                 in genetic programming where the latter is employed to
                 discover and optimize the structure of a model, while
                 the former is employed to optimize its parameters. A
                 number of practical examples are used to demonstrate
                 the effectiveness of the approach. Experimental results
                 show that the algorithm has some advantages over most
                 available modeling methods.",
}

@Article{cao:2000:ode2GP,
  author =       "Hong-Qing Cao and Li-Shan Kang and Tao Guo and Yu-Ping
                 Chen and Hugo de Garis",
  title =        "A two-level hybrid evolutionary algorithm for modeling
                 one-dimensional dynamic systems by higher-order {ODE}
                 models",
  journal =      "IEEE Transactions on Systems, Man and Cybernetics --
                 Part B: Cybernetics",
  year =         "2000",
  volume =       "40",
  number =       "2",
  pages =        "351--357",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, evolutionary algorithm, ODE models,
                 one-dimensional dynamic systems, ordinary differential
                 equation, two-level hybrid evolutionary modeling
                 algorithm, THEMA, crossover operator",
  ISSN =         "1083-4419",
  URL =          "http://ieeexplore.ieee.org/iel5/3477/18067/00836383.pdf",
  size =         "7 pages",
  abstract =     "This paper presents a new algorithm for modeling
                 one-dimensional (1-D) dynamic systems by higher-order
                 ordinary differential equation (HODE) models instead of
                 the ARMA models as used in traditional time series
                 analysis. A two-level hybrid evolutionary modeling
                 algorithm (THEMA) is used to approach the modeling
                 problem of HODE's for dynamic systems. The main idea of
                 this modeling algorithm is to embed a genetic algorithm
                 (GA) into genetic programming (GP), where GP is
                 employed to optimize the structure of a model (the
                 upper level), while a GA is employed to optimize the
                 parameters of the model (the lower level). In the GA,
                 we use a novel crossover operator based on a nonconvex
                 linear combination of multiple parents which works
                 efficiently and quickly in parameter optimization
                 tasks. Two practical examples of time series are used
                 to demonstrate the THEMA's effectiveness and
                 advantages.",
}

@Article{carbajal:2001:GPEM,
  author =       "Santiago {Garcia Carbajal} and Fermin Gonzalez
                 Martinez",
  title =        "Evolutive Introns: {A} Non-Costly Method of Using
                 Introns in {GP}",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "2",
  pages =        "111--122",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, bloating,
                 introns, intertwined spirals",
  ISSN =         "1389-2576",
  URL =          "http://ipsapp009.lwwonline.com/content/getfile/4723/5/3/fulltext.pdf",
  abstract =     "We proposed a new strategy to explicitly define
                 introns that increases the probability of selecting
                 good crossover points as evolution goes on. Our
                 approach differs from existing methods in the procedure
                 followed to adapt the probabilities of groups of code
                 being protected. We also provide some experimental
                 results in symbolic regression and classification that
                 reinforced our belief in the usefulness of this
                 procedure. Collateral effects of Evolutive Introns
                 (EIs) are also studied to determine possible
                 modifications in the behavior of a classical Genetic
                 Programming (GP) system.",
}

@Misc{card:1999:GPWNTSP,
  author =       "Stu Card",
  title =        "Genetic Programming of Wavelet Networks for Time
                 Series Prediction",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, neural-nets,
                 wavelets, time, scale, frequency, prediction,
                 stochastic, nonlinear",
  URL =          "http://www.borg.com/~stu/GECCO99.html",
}

@InCollection{carobus:2000:EGPBUGPCPNH,
  author =       "Alexander P. Carobus",
  title =        "Evolution of Game Playing Behavior: Using Genetic
                 Programming to Create Players for Net Hack",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "60--69",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{castillo:2002:gecco,
  author =       "Flor A. Castillo and Ken A. Marshall and James L.
                 Green and Arthur K. Kordon",
  title =        "Symbolic Regression In Design Of Experiments: {A} Case
                 Study With Linearizing Transformations",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1043--1047",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "real world applications, design of experiment (DoE),
                 genetic programming, lack of fit, linearizing
                 transformations, symbolic regression",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{castillo:1999:GGOMPEA,
  author =       "P. A. Castillo and V. Rivas and J. J. Merelo and J.
                 Gonzalez and A. Prieto and G. Romero",
  title =        "{G}-Prop-{III}: Global Optimization of Multilayer
                 Perceptrons using an Evolutionary Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "942",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming,
                 poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cattral:1999:RAGA,
  author =       "Robert Cattral and Franz Oppacher and Dwight Deugo",
  title =        "Rule Acquisition with a Genetic Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "778",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems, poster papers",
  ISBN =         "1-55860-611-4",
  abstract =     "Data mining, applied to poisonous mushroom machine
                 learning benchmark",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cavaretta:1999:DMGPTIPGE,
  author =       "Michael J. Cavaretta and Kumar Chellapilla",
  title =        "Data Mining using Genetic Programming: The
                 Implications of Parsimony on Generalization Error",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1330--1337",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, data mining",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InCollection{caverlee:2000:AGAADOBS,
  author =       "James B. Caverlee",
  title =        "A Genetic Algorithm Approach to Discovering an Optimal
                 Blackjack Strategy",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "70--79",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InCollection{cederberg:2002:TCTGAATNG,
  author =       "Scott Cederberg",
  title =        "The evolution of Cooperation: The Genetic Algorithm
                 Applied to Three Normal-Form Games",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "45--51",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InCollection{chai:2000:DCCPDGP,
  author =       "Daniel Chai",
  title =        "Development of a Computer Controller Players for
                 Daleks using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "80--89",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{chakraborti:1998:GAplaNLP,
  author =       "C. Chakraborti and K. K. N. Sastry",
  title =        "The Genetic Algorithms Approach for Proving Logical
                 Arguments in Natural Language",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "463--470",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@Article{chambers:2001:GPEM,
  author =       "Lance D. Chambers",
  title =        "Book Review: Genetic Programming and Data Structures:
                 Genetic Programming+Data Structures=Automatic
                 Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "3",
  pages =        "301--303",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1389-2576",
  notes =        "Review of langdon:book",
}

@InProceedings{chan:1999:MAFEMSGA,
  author =       "Zeke S. H. Chan and H. W. Ngan and A. B. Rad",
  title =        "Minimum-Allele-Reserve-Keeper ({MARK}): {A} Fast and
                 Effective Mutation Scheme for Genetic Algorithm
                 ({GA})",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "106--113",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{chan:1999:AS,
  author =       "Zeke S. H. Chan and H. W. Ngan and A. B. Rad",
  title =        "A new method to resist premature convergence:
                 Synchonising gene-convergence with correlated
                 recombination",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "74--79",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@InCollection{chan:1995:VEWCUGP,
  author =       "King Choi Chan",
  title =        "Valid English Word Classifier Using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "39--48",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InCollection{chan:2002:AGPFAGP,
  author =       "David Michael Chan",
  title =        "Automatic Generation of Prime Factorization Algorithms
                 using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "52--57",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp {"}GP hard{"} p57",
}

@InProceedings{char:1997:caiGP,
  author =       "K. Govinda Char",
  title =        "Constructivist {AI} with {GP}",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "28--34",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{char:1997:elGPcAI,
  author =       "K. Govinda Char",
  title =        "Evolution of Learning with Genetic Programming -
                 Constructivist {AI} with Genetic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "289",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  URL =          "http://www.elec.gla.ac.uk/~kchar/gp97.ps",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@InProceedings{char:1998:clGP,
  author =       "K. Govinda Char",
  title =        "Constructive Learning with Genetic Programming",
  booktitle =    "Late Breaking Papers at EuroGP'98: the First European
                 Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and Marc Schoenauer
                 and Terry Fogarty and Wolfgang Banzhaf",
  pages =        "1--5",
  address =      "Paris, France",
  publisher_address = "School of Computer Science",
  month =        "14-15 " # apr,
  publisher =    "CSRP-98-10, The University of Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.elec.gla.ac.uk/~kchar/eurogp.ps",
  size =         "5 pages",
  notes =        "EuroGP'98LB part of Poli:1998:egplb",
}

@PhdThesis{char:thesis,
  author =       "K. Govinda Char",
  title =        "Constructivist {AI} with Genetic Programming",
  school =       "Department of Electronics and Electrical Engineering,
                 University of Glasgow",
  year =         "1998",
  address =      "Rankine Building, Oakfield Avenue, Glasgow G12 8LT,
                 Scotland, UK",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@Article{chattoe:1998:uEArsp,
  author =       "Edmund Chattoe",
  title =        "Just How (Un)realistic are Evolutionary Algorithms as
                 Representations of Social Processes?",
  journal =      "The Journal of Artificial Societies and Social
                 Simulation",
  year =         "1998",
  volume =       "1",
  number =       "3",
  month =        "30 " # jun,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithms, social evolution, selectionist paradigm",
  URL =          "http://www.soc.surrey.ac.uk/JASSS/1/3/2.html",
  size =         "158407 bytes",
  abstract =     "This paper attempts to illustrate the importance of a
                 coherent behavioural interpretation in applying
                 evolutionary algorithms like Genetic Algorithms and
                 Genetic Programming to the modelling of social
                 processes. It summarises and draws out the implications
                 of the Neo-Darwinian Synthesis for processes of social
                 evolution and then discusses the extent to which
                 evolutionary algorithms capture the aspects of
                 biological evolution which are relevant to social
                 processes. The paper uses several recent papers in the
                 field as case studies, discussing more and less
                 successful uses of evolutionary algorithms in social
                 science. The key aspects of evolution discussed in the
                 paper are that it is dependent on relative rather than
                 absolute fitness, it does not require global knowledge
                 or a system level teleology, it avoids the credit
                 assignment problem, it does not exclude Lamarckian
                 inheritance and it is both progressive and open
                 ended.",
  notes =        "JASSS",
}

@InProceedings{Chaudhri:2000:GECCO,
  author =       "Omer A. Chaudhri and Jason M. Daida and Jonathan C.
                 Khoo and Wendell S. Richardson and Rachel B. Harrison
                 and William J. Sloat",
  title =        "Characterizing a Tunably Difficult Problem in Genetic
                 Programming",
  pages =        "395--402",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{Chellapilla:1997:eptm,
  author =       "Kumar Chellapilla",
  title =        "Evolutionary Programming with Tree Mutations: Evolving
                 Computer Programs without Crossover",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "evolutionary programming and evolution strategies",
  pages =        "431--438",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97 non-standard initialisation of initial pop, 6
                 mutation operators, no crossover 6-bit multiplexor,
                 simple symbolic regression x+x**2+x**3+x**4, artificial
                 ant (Santa Fe Trail), cart centering",
}

@InProceedings{chellapilla:1998:enlbtatuEP,
  author =       "Kumar Chellapilla",
  title =        "Evolving Nonlinear Controllers for Backing up a
                 Truck-and-Trialer Using Evolutionary Programming",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "417--426",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "evolutionary programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7",
}

@InProceedings{chellapilla:1998:agnoclbbEP,
  author =       "Kumar Chellapilla",
  title =        "Automatic Generation of Nonlinear Optimal Control Laws
                 for Broom Balancing using Evolutionary Programming",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "195--200",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c034.pdf",
  size =         "6 pages",
  abstract =     "This paper explores the use of mutation operators with
                 evolutionary programming (EP) to automatically generate
                 time optimal {"}bang-bang{"} type control laws for the
                 three dimensional broom balancing (inverted pendulum)
                 problem. EP produces a time optimal nonlinear control
                 strategy that takes the state variables as input and
                 determines the direction of the {"}bang-bang{"} force
                 to be applied. Preliminary results indicate that the
                 control laws generated are capable of generalizing over
                 previously unseen input states and compare well with
                 nonlinear control laws that were generated using other
                 evolutionary computation methods.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence.
                 Comparison with koza:book results",
}

@InProceedings{chellapilla:1998:piempwsx,
  author =       "Kumar Chellapilla",
  title =        "A Preliminary Investigation into Evolving Modular
                 Programs without Subtree Crossover",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "23--31",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{chellapilla:1998:elsoEP,
  author =       "Kumar Chellapilla and Hemanth Birru and Rao
                 Sathyanarayan",
  title =        "Effectivenss of Local Search Operators in Evolutionary
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "753--761",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolutionary programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{Chellapilla:1998:eptm,
  author =       "Kumar Chellapilla",
  title =        "Evolving Computer Programs without Subtree Crossover",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "1997",
  volume =       "1",
  number =       "3",
  pages =        "209--216",
  month =        sep,
  keywords =     "Evolutionary Programming, genetic algorithms, genetic
                 programming symbolic expressions, variation operators",
  notes =        "negative results on building block hypothesis, C++
                 code available, Compares use of EP using 6 types of
                 tree mutation with GP on: 6-mux, 3, 4, 5, 6 parity,
                 symbolic regression, two box, two spirals, Santa Fe
                 trail artificial ant, cart centering, 4 variation on
                 broom balancing. In general EP wins in terms of Effort
                 to find the solution. Gives algorithm used to create
                 initial random trees",
}

@InProceedings{chen:1995:psmrGP,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Predicting Stock Returns with Genetic Programming: Do
                 the Short-Term Nonlinear Regularities Exist?",
  booktitle =    "Proceedings of the Fifth International Workshop on
                 Artificial Intelligence and Statistics",
  year =         "1995",
  editor =       "D. Fisher",
  pages =        "95--101",
  address =      "Ft. Lauderdale, Florida, U.S.A.",
  month =        jan # " 4-7",
  organisation = "Society for Artificial Intelligence and Statistics",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1995:cqtm,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "On the Competitiveness of the Quantity Theory of
                 Money: {A} Natural-Selection Test Based on Genetic
                 Programming",
  booktitle =    "11th International Conference on Advanced Science and
                 Technology",
  year =         "1995",
  address =      "Chicago, Illinois, U.S.A",
  month =        "25-27 " # mar,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1995:cale,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "On the Coordination and Adaptability of the Large
                 Economy: An Application of Genetic Programming to the
                 Cobweb Model",
  booktitle =    "Proceedings of the First International Conference on
                 Applications of Dynamic Models to Economics",
  year =         "1995",
  number =       "3",
  series =       "The School of Management National Central University's
                 International Conference Series",
  pages =        "121--159",
  address =      "ChungLi, Taiwan, R.O.C.",
  month =        jun # " 17-18",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1995:GPpsme,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming, Predictability and Stock Market
                 Efficiency",
  booktitle =    "Proceedings of 1995 IFAC/IFIP/IFORS/SEDC Symposium on
                 Modelling and Control of National and Regional
                 Economies",
  year =         "1995",
  volume =       "II",
  address =      "Gold Coast, Australia",
  month =        jul # " 3-5",
  organisation = "International Federation of Automatic Control",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1995:pcdsGP,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Predicting Chaotic Dynamic Systems with Genetic
                 Programming",
  booktitle =    "Proceedings of the 50th International Statistical
                 Institute Session",
  year =         "1995",
  address =      "Beijing",
  month =        aug # " 21-29",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1995:itmeeipt,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Information Transmission, Market Efficiency and the
                 Evolution of Information-Processing Technology",
  booktitle =    "Proceedings of the 1995 National Conference on
                 Management of Technology",
  year =         "1995",
  editor =       "C. Houng",
  pages =        "339--348",
  organisation = "Chinese Society of Management of Technology",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1996:MAAMAW,
  author =       "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
  title =        "Modelling Coordination Game as a Multi-Agent Adaptive
                 System by Genetic Programming",
  booktitle =    "Position Papers of the 7th European Workshop on
                 Modelling Autonomous Agents in a Multi-Agent World
                 (MAAMAW'96)",
  year =         "1996",
  editor =       "W. {Van de Velde} and J. W. Perram",
  month =        jan # " 22-25",
  organisation = "Institute for Perception Research, Eindhoven, The
                 Netherlands",
  note =         "Technical Report 96-1",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1996:GPcfe,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming in Computable Financial
                 Economics",
  booktitle =    "Proceedings of the ISCA 11th Conference: Computers and
                 Their Applications",
  year =         "1996",
  pages =        "135--138",
  address =      "San Francisco, California, U.S.A.",
  month =        mar # " 7-9",
  publisher =    "ISCA Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-880843-15-3",
}

@InProceedings{chen:1996:bgntemh,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Bridging the Gap between Nonlinearity Tests and the
                 Efficient Market Hypothesis by Genetic Programming",
  booktitle =    "Proceedings of the IEEE/IAFE 1996 Conference on
                 Computational Intelligence for Financial Engineering",
  year =         "1996",
  pages =        "34--39",
  address =      "Crowne Plaza Manhattan, New York City",
  month =        mar # " 24-26",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-3236-9",
}

@InCollection{chen:1996:GPpsme,
  author =       "Shu-Heng Chen",
  title =        "Genetic Programming, Predictability, and Stock Market
                 Efficiency",
  booktitle =    "Modelling and Control of National and Regional
                 Economies 1995",
  publisher =    "Pergamon",
  year =         "1996",
  editor =       "L. Vlacic and T. Nguyen and D. Cecez-Kecmanovic",
  pages =        "283--288",
  address =      "Oxford, Great Britain",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-08-042376-0",
}

@InProceedings{chen:1996:cale:GPcm,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "On the Coordination and Adaptability of the Large
                 Economy: An Application of Genetic Programming to the
                 Cobweb Model",
  booktitle =    "Preprints of 13th World Congress International
                 Federation of Automatic Control",
  year =         "1996",
  volume =       "L",
  pages =        "279--284",
  address =      "San Francisco, CA, USA",
  month =        jun # " 30-" # jul # " 5",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{chen:1996:aigp2,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming Learning and the Cobweb Model",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "443--466",
  chapter =      "22",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
}

@InProceedings{chen:1996:GPcgcbr,
  author =       "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
  title =        "Genetic Programming in the Coordination Game with a
                 Chaotic Best-Response Function",
  booktitle =    "Evolutionary Programming V: Proceedings of the Fifth
                 Annual Conference on Evolutionary Programming",
  year =         "1996",
  editor =       "Lawrence J. Fogel and Peter J. Angeline and Thomas
                 Baeck",
  pages =        "277--286",
  address =      "San Diego",
  publisher_address = "Cambridge, MA, USA",
  month =        feb # " 29-" # mar # " 3",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-06190-2",
  notes =        "EP-96
                 http://www.natural-selection.com/eps/EP96.html

                 ",
}

@Article{chen:1996:caemh,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Toward a Computable Approach to the Efficient Market
                 Hypothesis: An Application of Genetic Programming",
  journal =      "Journal of Economic Dynamics and Control",
  year =         "1996",
  volume =       "21",
  pages =        "1043--1063",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Minimum description length principle, Mean
                 absolute percentage error, Efficient market
                 hypothesis",
  abstract =     "From a computation-theoretic standpoint, this paper
                 formalizes the notion of unpredictability in the
                 efficient market hypothesis (EMH) by a biological-based
                 search program, i.e., genetic programming (GP). This
                 formalization differs from the traditional notion based
                 on probabilistic independence in its treatment of
                 <I>search</I>. Compared with the traditional notion, a
                 GP-based search provides an explicit and efficient
                 search program upon which an objective measure for
                 predictability can be formalized in terms of search
                 intensity and chance of success in the search. This
                 will be illustrated by an example of applying GP to
                 predict chaotic time series. Then the EMH based on this
                 notion will be exemplified by an application to the
                 Taiwan and US stock market. A short-term sample of
                 TAIEX and S&P 500 with the highest complexity defined
                 by Rissanen's minimum description length principle
                 (MDLP) is chosen and tested. It is found that, while
                 linear models cannot predict better than the random
                 walk, a GP-based search can beat random walk by 50%.
                 It, therefore, confirms the belief that while the
                 short-term nonlinear regularities might still exist,
                 the search costs of discovering them might be too high
                 to make the exploitation of these regularities
                 profitable, hence the efficient market hypothesis is
                 sustained.",
}

@InProceedings{chen:1996:esGP,
  author =       "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
  title =        "Equilibrium Selection Using Genetic Programming",
  booktitle =    "Progress in Neural Information Precessing: Proceedings
                 of the International Conference on Neural Information
                 Processing (ICONIP'96)",
  year =         "1996",
  editor =       "S. Amari and L. Xu and L. Chan and I. King and K.
                 Leung",
  volume =       "2",
  pages =        "1341--1346",
  address =      "Hong Kong Convention and Exhibition Center, Hong
                 Kong",
  publisher_address = "Singapore",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "981-3083-04-2",
  notes =        "

                 ",
}

@InProceedings{chen:1996:GPlcms,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming Learning in the Cobweb Model with
                 Speculators",
  booktitle =    "Proceedings of 3rd Conference on Business Education",
  year =         "1996",
  pages =        "155--176",
  address =      "Department of Business Education, National Changhua
                 University of Education, Chunghua, Taiwan",
  month =        dec # " 5",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1996:GPlcmsICS,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming Learning in the Cobweb Model with
                 Speculators",
  booktitle =    "International Computer Symposium (ICS'96). Proceedings
                 of International Conference on Artificial
                 Intelligence",
  year =         "1996",
  pages =        "39--46",
  address =      "National Sun Yat-Sen University, Kaohsiung, Taiwan,
                 R.O.C.",
  month =        dec # " 19-21",
  keywords =     "genetic algorithms, genetic programming",
}

@Article{chen:1996:itmeeipt,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Information Transmission, Market Efficiency and the
                 Evolution of Information-Processing Technology",
  journal =      "Journal of Technology Management",
  year =         "1996",
  volume =       "1",
  number =       "1",
  pages =        "23--41",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1996:cfaGPothers,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "A Comparison of Forcast Accuracy between Genetic
                 Programming and Other Forcasters: {A} loss-Differential
                 Approach",
  booktitle =    "The First International Workshop on Machine Learning,
                 Forecasting, and Optimization (MALFO96)",
  year =         "1996",
  editor =       "Daniel Borrajo and Pedro Isasi",
  pages =        "39--51",
  address =      "Gatafe, Spain",
  month =        "10--12 " # jul,
  organisation = "Universidad Carlos III de Madrid",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "84-89315-04-3",
  URL =          "http://grial.uc3m.es/~dborrajo/malfo96.html",
}

@InProceedings{chen:1996:gpemh,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming and the Efficient Market
                 Hypothesis",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "45--53",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{chen:1997:stfr,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Speculative Trades and Financial Regulations:
                 Simulations Based on Genetic Programming",
  booktitle =    "Proceedings of the IEEE/IAFE 1997 Computational
                 Intelligence for Financial Engineering (CIFEr'97)",
  year =         "1997",
  pages =        "123--129",
  address =      "New York City, U.S.A.",
  month =        mar # " 24-25",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1997:setpGP,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Simulating Economic Transition Processes by Genetic
                 Programming",
  booktitle =    "Proceedings of the International Conference on
                 Transition to Advanced Market Institutions and
                 Economies: Systems and Operations Research Challenges
                 (Transition'97)",
  year =         "1997",
  editor =       "R. Kulikowski and Z. Nahorski and J. W. Owsinski",
  pages =        "87--93",
  address =      "Warsaw, Poland",
  month =        jun # " 18-21",
  organisation = "System Research Institute and Polish Academy of
                 Sciences",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "83-85847-81-2",
}

@InProceedings{chen:1997:trstpv,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Trading Restrictions, Speculative Trades and Price
                 Volatility: An Application of Genetic Programming",
  booktitle =    "Proceedings of the 3rd International Mendel Conference
                 on Genetic Algorithms, Optimization Problems, Fuzzy
                 Logic, Neural Networks, Rough Sets (Mendel'97)",
  year =         "1997",
  pages =        "31--37.",
  address =      "Brno, Czech Republic",
  publisher_address = "Brno",
  month =        jun # " 25-27",
  publisher =    "PC-DIR",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "80-214-0884-7",
}

@InProceedings{chen:1997:eannGPfd,
  author =       "S-H Chen and C-C ni",
  title =        "Evolutionary Artificial Neural Networks and Genetic
                 Programming: {A} Comparative Study Based on Financial
                 Data",
  booktitle =    "ICANNGA97",
  year =         "1997",
  address =      "University of East Anglia, Norwich, UK",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}

@InProceedings{chen:1997:msGP,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Modeling Speculators with Genetic Programming",
  booktitle =    "Proceedings of the Sixth Conference on Evolutionary
                 Programming",
  year =         "1997",
  editor =       "Peter J. Angeline and Robert G. Reynolds and John R.
                 McDonnell and Russ Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  pages =        "137--147",
  address =      "Indianapolis, Indiana, USA",
  publisher_address = "Berlin",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "EP-97",
}

@InProceedings{chen:1997:eANNGP,
  author =       "Shu-Heng Chen and Chih-Chi Ni",
  title =        "Evolutionary Artificial Neural Networks and Genetic
                 Programming: {A} Comparative Study Based on Financial
                 Data",
  booktitle =    "Artificial Neural Networks and Genetic Algorithms",
  year =         "1997",
  editor =       "G. D. Smith",
  address =      "Vienna",
  publisher =    "Springer-Verlag",
  note =         "Forthcoming",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1997:GPmvfts,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Using Genetic Programming to Model Volatility in
                 Financial Time Series",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "58--63",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  ISBN =         "1-55860-483-9",
  abstract =     "RGP tested by using Nikkei 255 and S&P 500 as an
                 example",
  notes =        "GP-97 Fixed size sliding window of the original time
                 series. BGP used to learn first window, then whole pop
                 used with second window (ie as population seed).
                 Fitness = sum of errors squared also serves to give
                 estimate of volatility.",
}

@InProceedings{chen:1997:GPmvfts:NS+P,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Using Genetic Programming to Model Volatility in
                 Financial Time Series: The Case of Nikkei 225 and
                 {S}\&{P} 500",
  booktitle =    "Proceedings of the 4th JAFEE International Conference
                 on Investments and Derivatives (JIC'97)",
  year =         "1997",
  pages =        "288--306",
  address =      "Aoyoma Gakuin University, Tokyo, Japan",
  month =        jul # " 29-31",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1997:stfr:ICJAI,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Speculative Trades and Financial Regulations:
                 Simulation Bassed on Genetic Programming",
  booktitle =    "Working Notes of The IJCAI-97: Workshop on Business
                 Applications of AI. Fifteenth International Joint
                 Conference on Artificial Intelligence (IJCAI'97)",
  year =         "1997",
  editor =       "A. Ghose",
  pages =        "1--8",
  address =      "Nagoya, Japan",
  month =        aug # " 23-29",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{chen:1997:mscGPo,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Modelling Structural Changes with Genetic Programming:
                 An Outline",
  booktitle =    "Proceedings of 15th IMACS World Congress on Scientific
                 Computation, Moldelling and Applied Mathematics",
  year =         "1997",
  editor =       "A. Sydow",
  volume =       "2",
  pages =        "621--626",
  address =      "Berlin",
  month =        aug # " 24-29",
  publisher =    "Numerical Mathematics, Wissenschaft \& Technik
                 Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-89685-552-2",
}

@InProceedings{chen:1998:GPogmidir,
  author =       "S-H. Chen and C-H. Yeh",
  title =        "Genetic Programming in the Overlapping Generations
                 Model: An Ilustration with the Dynamics of the
                 Inflation Rate",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "829--837",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 National Chengchi University",
}

@InProceedings{chen:1998:opGP,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh and Woh-Chiang Lee",
  title =        "Option Pricing with Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "32--37",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{chen:1998:hdsGP,
  author =       "Chen",
  title =        "Hedging Derivative Securities with Genetic
                 Programming",
  booktitle =    "Application of Machine Learning and Data Mining in
                 Finance: Workshop at ECML-98",
  year =         "1998",
  address =      "Dorint-Parkhotel, Chemnitz, Germany",
  month =        "24 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ECML-98 workshop
                 6

                 http://www.tu-chemnitz.de/informatik/ecml98/ws6_ag.txt",
}

@Article{Chen:2002:EJEMED,
  author =       "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
  title =        "Equilibrium Selection via Adaptation: Using Genetic
                 Programming to Model Learning in a Coordination Game",
  journal =      "The Electronic Journal of Evolutionary Modeling and
                 Economic Dynamics",
  year =         "2002",
  month =        "15 " # jan,
  keywords =     "genetic algorithms, genetic programming, Adaptation,
                 Coordination Game, Equilibrium Selection, Survival of
                 the Fittest",
  ISSN =         "1298-0137",
  URL =          "http://beagle.montesquieu.u-bordeaux.fr/jemed/1002/1002.pdf",
  size =         "44 pages",
  abstract =     "This paper models adaptive learning behavior in a
                 simple coordination game that Van Huyck, Cook and
                 Battalio (1994) have investigated in a controlled
                 laboratory setting with human subjects. We consider how
                 populations of artificially intelligent players behave
                 when playing the same game. We use the genetic
                 programming paradigm, as developed by Koza (1992,
                 1994), to model how a population of players might learn
                 over time. In genetic programming one seeks to breed
                 and evolve highly fit computer programs that are
                 capable of solving a given problem. In our application,
                 each computer program in the population can be viewed
                 as an individual agent's forecast rule. The various
                 forecast rules (programs) then repeatedly take part in
                 the coordination game evolving and adapting over time
                 according to principles of natural selection and
                 population genetics. We argue that the genetic
                 programming paradigm that we use has certain advantages
                 over other models of adaptive learning behavior in the
                 context of the coordination game that we consider. We
                 find that the pattern of behavior generated by our
                 population of artificially intelligent players is
                 remarkably similar to that followed by the human
                 subjects who played the same game. In particular, we
                 find that a steady state that is theoretically unstable
                 under a myopic, bestresponse learning dynamic turns out
                 to be stable under our genetic programming based
                 learning system, in accordance with Van Huyck et al.'s
                 (1994) finding using human subjects. We conclude that
                 genetic programming techniques may serve as a plausible
                 mechanism for modelling human behavior, and may also
                 serve as a useful selection criterion in environments
                 with multiple equilibria.",
  notes =        "RePEc:jem:ejemed:1002",
}

@InProceedings{chen:1998:ecso,
  author =       "Stephen Chen and Stephen F. Smith",
  title =        "Experiments on Commonality in Sequencing Operators",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "471--478",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{chen:1999:GATSSPSNEMCS,
  author =       "Shu-Heng Chen and Wei-Yuan Lin and Chueh-Iong Tsao",
  title =        "Genetic Algorithms, Trading Strategies and Stochastic
                 Processes: Some New Evidence from Monte Carlo
                 Simulations",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "114--121",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{SHChen:1999:gpabmsm,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming in the Agent-Based Modeling of
                 Stock Markets",
  booktitle =    "Fifth International Conference: Computing in Economics
                 and Finance",
  year =         "1999",
  editor =       "David A. Belsley and Christopher F. Baum",
  pages =        "77",
  address =      "Boston College, MA, USA",
  month =        "24-26 " # jun,
  note =         "Book of Abstracts",
  keywords =     "genetic algorithms, genetic programming, Agent-Based
                 Computational Economics, Social Learning, Business
                 School, Artificial Stock Markets, Simulated Annealing,
                 Peer Pressure",
  URL =          "http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf",
  size =         "22 pages",
  abstract =     "In this paper, we propose a new architecture to study
                 artificial stock markets. This architecture rests on a
                 mechanism called school which is a procedure to map the
                 phenotype to the genotype or, in plain English, to
                 uncover the secret of success. We propose an
                 agent-based model of school, and consider school as an
                 evolving population driven by single-population GP
                 (SGP). The architecture also takes into consideration
                 traders' search behavior. By simulated annealing,
                 traders' search density can be connected to
                 psychological factors, such as peer pressure or
                 economic factors such as the standard of living. This
                 market architecture was then implemented in a standard
                 artificial stock market. Our econometric study of the
                 resultant artificial time series evidences that the
                 return series is independently and identically
                 distributed (iid), and hence supports the efficient
                 market hypothesis (EMH). What is interesting though is
                 that this iid series was generated by traders, who do
                 not believe in the EMH at all. In fact, our study
                 indicates that many of our traders were able to find
                 useful signals quite often from business school, even
                 though these signals were short-lived.",
  notes =        "PDF and abstract on paper differ in detail. Using PDF
                 info",
}

@InProceedings{chen:1999:IGASSRAFSS,
  author =       "Stephen Chen and Stephen F. Smith",
  title =        "Improving Genetic Algorithms by Search Space
                 Reductions (with Applications to Flow Shop
                 Scheduling)",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "135--140",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{chen:1999:INACCS,
  author =       "Stephen Chen and Stephen F. Smith",
  title =        "Introducing a New Advantage of Crossover:
                 Commonality-Based Selection",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "122--128",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{chen:1999:NCSRCFSS,
  author =       "Stephen Chen and Cesar Guerra-Salcedo and Stephen F.
                 Smith",
  title =        "Non-Standard Crossover for a Standard Representation
                 -- Commonality-Based Feature Subset Selection",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "129--134",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{chen:1999:TAFFEAABGAM,
  author =       "Shu-Heng Chen and Tzu-Wen Kuo",
  title =        "Towards an Agent-Based Foundation of Financial
                 Econometrics: An Approach Based on Genetic-Programming
                 Artificial Markets",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "966",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{chen:1999:GPAASM,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming in the Agent-Based Artificial
                 Stock Market",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "834--841",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, algorithms",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{Shu-HengChen2:2000:CEF,
  author =       "Shu-Heng Chen and Chung-Chi Liao and Chi-Hsuan Yeh",
  title =        "Toward an integration of social learning and
                 individual learning in agent-based computational stock
                 markets:the approach based on population genetic
                 programming",
  booktitle =    "Computing in Economics and Finance",
  year =         "2000",
  address =      "Universitat Pompeu Fabra, Barcelona, Spain",
  month =        "6-8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://enginy.upf.es/SCE/papers/paper338.ps.gz",
  abstract =     "This paper extends the architecture built by Chen and
                 Yeh (2000) and takes into account the aspect of
                 individual learning of traders in the agent-based
                 artificial stock markets. By hybridizing population
                 genetic programming with individual genetic
                 programming, each trader can adapts his beliefs of the
                 market dyanmics either through class-taking in the
                 business school (B-school) or mediation by themselves.
                 This design provides us an opportunity to examine the
                 role of social and individual learning in the complex
                 adaptive systems.",
  notes =        "http://enginy.upf.es/SCE/index2.html",
}

@InProceedings{Shu-HengChen:2000:CEF,
  author =       "Shu-Heng Chen",
  title =        "On Bargaining Strategies in the {SFI} Double Auction
                 Tournaments: Is Genetic Programming the Answer?",
  booktitle =    "Computing in Economics and Finance",
  year =         "2000",
  address =      "Universitat Pompeu Fabra, Barcelona, Spain",
  month =        "6-8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "While early computational studies of bargaining
                 strategies, such as Rust, Miller and Palmer (1993,
                 1994) and Andrew and Prager (1996) all indicates the
                 significance of agent-based modeling in the follow-up
                 research, a real agent-based model of bargaining
                 strategies in DA markets has never been taken. This
                 paper attempts to take the fisrt step toward it.

                 In this paper, genetic programming is employed to
                 evolve bargaining strategies within the context of SFI
                 double auction tournaments. We are interested in
                 knowing that given a set of traders, each with a fixed
                 trading strategies, can the automated trader driven by
                 genetic programming eventually develop bargaining
                 strategies which can outperform its competitors'
                 strategies? To see how GP trader can survive in various
                 environments, different sets of traders characterized
                 by different compositions of bargaining strategies are
                 chosen to compete with the single GP trader. To give a
                 measure of the difficult level of the DA auction
                 markets facing the GP trader, the program length is
                 used to define the intelligence of chosen traders. In
                 one experiment, the chosen traders are all naive; in
                 another experiment, the traders are all sophisticated.
                 Other experiments are placed in the middle of these two
                 extremes.",
  notes =        "http://enginy.upf.es/SCE/index2.html",
}

@PhdThesis{YuehuiChen:thesis,
  author =       "Yuehui Chen",
  title =        "Hybrid Soft Computing Approach to Identification and
                 Control of Nonlinear Systems",
  school =       "Department of Computer Science, Kumamoto University",
  year =         "2001",
  address =      "Japan",
  month =        mar,
  email =        "CHEN Yuehui <chen@memory-tech.co.jp>",
  keywords =     "genetic algorithms, genetic programming, PIPE
                 Algorithm",
  URL =          "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.001",
  URL =          "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.002",
  URL =          "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.003",
  url_2 =        "ftp://cs.ucl.ac.uk/genetic/papers/yuehui.chen/YuehuiChenThesis.pdf",
  size =         "182 pages",
  abstract =     "

                 Recently, complex industrial plants such as mobile
                 robots, flexible manufacturing system etc., are often
                 required to perform complex tasks with high precision
                 under ill-defined conditions, and conventional control
                 techniques may not be quite effective in these systems.
                 Soft computing approaches are some computational models
                 inspired by the simulated human and/or natural
                 intelligence, and includes fuzzy logic, artificial
                 neural networks, genetic and evolutionary algorithms.
                 There have been many successful researches for the
                 identification and control of nonlinear systems by
                 using various soft computing techniques with different
                 computational architectures. The experiences gained
                 over the past decade indicate that it can be more
                 effective to use the various soft computing approaches
                 in a combined manner. But there is no common
                 recognition about how to combine them in an effective
                 way, and a unified framework of hybrid soft computing
                 models in which various soft computing models can be
                 developed, evolved and evaluated has not been
                 established.",
  abstract =     "In this research, a unified framework of hybrid soft
                 computing models is proposed and it is applied to the
                 identification and control of industrial plants. First,
                 a scheme for identification and control of nonlinear
                 systems using probabilistic incremental program
                 evolution algorithm (PIPE) is proposed. Based on the
                 modified PIPE (MPIPE) and some parameter tuning
                 strategies, a unified framework of hybrid soft
                 computing models is constructed for the identification
                 of nonlinear systems, and then the hybrid soft
                 computing based controller design principles and
                 methods are developed. As an application, the proposed
                 methods are applied to the identification and control
                 of the thrust force (cutting torque) in the drilling
                 system.

                 This dissertation consists of six chapters as
                 follows:

                 In Chapter 1, the background and the current state of
                 soft computing researches, and the purpose of the
                 thesis are described briefly.

                 In chapter 2, the basic elements of soft computing
                 technique are discussed, including the evolutionary
                 algorithms and random search algorithm, neural networks
                 and fuzzy logic systems. The problems and disadvantages
                 of the soft computing approaches are pointed out and
                 their modification and improvements are given.",
  abstract =     "In chapter 3, in order to cope with the problems of
                 architecture selection and parameter optimization of
                 soft computing models simultaneously, a unified
                 framework is constructed in which various hybrid soft
                 computing models can be developed, evolved and
                 evaluated. In the proposed method, the architecture of
                 the hybrid soft computing models is evolved by MPIPE
                 and the parameters used in soft computing models are
                 optimized by hybrid or non-hybrid parameter
                 optimization strategy, respectively. The effectiveness
                 of the proposed method has been confirmed by simulation
                 studies.

                 In chapter 4, some common soft computing based
                 controller design principles are discussed briefly.
                 Then a new control scheme for nonlinear systems based
                 on PIPE algorithm is proposed. Finally, based on the
                 basis function networks a unified framework for control
                 of affine and non-affine nonlinear systems is presented
                 with guaranteed stability analysis. The simulation and
                 experimental results show the effectiveness of the
                 proposed controller.",
  abstract =     "In chapter 5, the soft computing based identification
                 and control schemes developed in chapter 3 and 4 are
                 applied to the drilling system. In order to control
                 thrust force (cutting torque) in the process of drill,
                 a number of thrust force (cutting torque)
                 identification methods are developed, and then thrust
                 force (cutting torque) soft model based neural control
                 scheme are presented. Real time implementations show
                 that the soft computing approaches based control
                 schemes are efficient and effective.

                 Finally in chapter 6, the results obtained in previous
                 chapter are summarized, and some topics for future
                 research in this direction are given.

                 In this research, the applicability of PIPE algorithm
                 to identification and control of nonlinear systems is
                 confirmed. Based on the MPIPE and some parameter tuning
                 strategies, a unified framework of hybrid soft
                 computing models is constructed. Simulation and
                 experiments results for the identification and control
                 of nonlinear systems show the effectiveness of the
                 proposed methods. The key point of the research is that
                 various soft computing based identification and control
                 schemes can be re-evaluated in a unified framework and
                 then it is valuable for the proposed approach in order
                 to construct the unified soft computing theories and
                 applications.",
  notes =        "my PDF reader barfed 20 July 2001. url_2 ok",
}

@InCollection{Cheng:1997:rphGPri,
  author =       "Cleve Cheng",
  title =        "Recognizing Poker Hands with Genetic Programming and
                 Restricted Iteration",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "18--27",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{chia-hsuanyeh:2001:gecco,
  title =        "The Differences between Social and Individual Learning
                 on the Time Series Properties: The Approach Based on
                 Genetic Programming",
  author =       "Chia-Hsuan Yeh and Shu-Heng Chen",
  pages =        "191",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 Social Learning, Individual Learning, Artificial Stock
                 Market, Agent-Based Modeling",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{chidambaran:1998:aeaopGP,
  author =       "N. K. Chidambaran and C. H. Jevons Lee and Joaquin R.
                 Trigueros",
  title =        "An Adaptive Evolutionary Approach to Option Pricing
                 via Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "38--41",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{chien:2000:GTRUEM,
  author =       "Edward K. Chien",
  title =        "Grid-Based Trace Routing Using Evolutionary Methods",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "90--97",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{chikara:1999:CLASCS,
  author =       "Maezawa Chikara and Atsumi Masayasu",
  title =        "Collaborative Learning Agents with Structural
                 Classifier Systems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "777",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cho:1996:mNNeGP,
  author =       "Sung-Bae Cho and Katsunori Shimohara",
  title =        "Modular Neural Networks Evolved by Genetic
                 Programming",
  booktitle =    "Proceedings of the 1996 {IEEE} International
                 Conference on Evolutionary Computation",
  year =         "1996",
  volume =       "1",
  pages =        "681--684",
  address =      "Nagoya, Japan",
  month =        "20-22 " # may,
  organisation = "IEEE Neural Network Council",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-2902-3",
  notes =        "ICEC-96 Evolves ANN network for recognising human
                 written characters",
}

@Article{cho:1998:mNNeGP,
  author =       "Sung-Bae Cho and Katsunori Shimohara",
  title =        "Evolutionary Learning of Modular Neural Networks with
                 Genetic Programming",
  journal =      "Applied Intelligence",
  year =         "1998",
  volume =       "9",
  number =       "3",
  pages =        "191--200",
  month =        nov # "/" # dec,
  keywords =     "genetic algorithms, genetic programming, neural
                 networks, evolutionary computation, modules, emergence,
                 handwritten digits, OCR",
  ISSN =         "0924-669X",
  notes =        "Evolves ANN network for categorizing human written
                 characters. USA Federal post office dataset online?

                 ",
}

@InProceedings{D.Y.Cho:1998:GPmacstt,
  author =       "D. Y. Cho and B. T. Zhang",
  title =        "Genetic programming of multi-agent cooperation
                 strategies for table transport",
  booktitle =    "The Third Asian Fuzzy Systems Symposium",
  year =         "1998",
  editor =       "K. C. Min",
  pages =        "170--175",
  address =      "Kyungnam University, Masan, Korea",
  month =        "18-21 " # jun,
  organisation = "Korea Fuzzy Logic and Intelligent Systems Society
                 (KFIS)",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AFSS'98",
}

@InProceedings{cho:1999:GPalecri,
  author =       "D. Y. Cho and B. T. Zhang",
  title =        "Genetic programming-based Alife techniques for
                 evolving collective robotic intelligence",
  booktitle =    "Proceedings 4th International Symposium on Artificial
                 Life and Robotics",
  year =         "1999",
  editor =       "M. Sugisaka",
  pages =        "236--239",
  address =      "B-Con Plaza, Beppu, Oita, Japan",
  month =        "19-22 " # jan,
  keywords =     "genetic algorithms, genetic programming, artificial
                 life",
  notes =        "AROB'99 Details from www site etc",
}

@InProceedings{Choenni:1999:SGB,
  author =       "Sunil Choenni",
  title =        "On the Suitability of Genetic-Based Algorithms for
                 Data Mining",
  booktitle =    "Advances in Database Technologies",
  editor =       "Yahiko Kambayashi and Dik Lun Lee and Ee-Peng Lim and
                 Mukesh Kumar Mohania and Yoshifumi Masunaga",
  series =       "LNCS",
  volume =       "1552",
  pages =        "55--67",
  year =         "1999",
  CODEN =        "LNCSD9",
  ISSN =         "0302-9743",
  bibdate =      "Tue Sep 14 06:09:05 MDT 1999",
  acknowledgement = ack-nhfb,
  keywords =     "genetic algorithms, genetic programming, ADT;
                 conceptual modelling; database technologies; mobile
                 data access; spatio-; temporal data management",
  address =      "Singapore",
  month =        "19-20 " # nov # " 1998",
  publisher =    "Springer-Verlag",
  email =        "choenni@nrl.nl",
  keywords =     "genetic algorithms",
  ISBN =         "3-540-65690-1",
  notes =        "DWDM98 ER'98 Workshop on Data Warehousing and Data
                 Mining, Mobile Data Access, and Collaborative Work
                 Support and Spatio-Temporal Data Management

                 Also available as Dutch military {"}National Aerospace
                 Laboratory{"} NLR tech report. choenni:1998:SGADM NLR
                 Technical Publications 98484-tp.pdf NLR-TP-98484 Also
                 {"}University of Twente{"}.

                 Fixed length representation, one locus per database
                 attribute. Attributes either 1) not used 2) actual
                 value (categorical data) or 3) range, eg [3,34]. All
                 attributes anded together to give query. Mutation and
                 crossover a little bit smart.

                 Microsoft Access interface. Interactive. User specifies
                 initial topic to be mind and can interactively update
                 this.

                 http://wwwhome.cs.utwente.nl/~choenni/
                 http://www.nlr.nl/public/library/index.html#diagram",
}

@TechReport{choenni:1998:SGADM,
  author =       "Sunil Choenni",
  title =        "On the Suitability of Genetic-Based Algorithms for
                 Data Mining",
  institution =  "National Aerospace Laboratory",
  year =         "1998",
  number =       "NLR-TP-98484",
  address =      "Amsterdam",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "shorter version published as Choenni:1999:SGB

                 page 22 {"}real-life database, FAA incident database,
                 contains aircraft incident data 1978-95{"}",
  size =         "pages",
}

@TechReport{choenni:1999:ieGDMa,
  author =       "Sunil Choenni",
  title =        "Implementation and Evaluation of a Genetic-Based Data
                 Mining Algorithm",
  institution =  "National Aerospace Laboratory",
  year =         "1999",
  number =       "NLR-TR-99281",
  address =      "Amsterdam",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "GA can be rapidly implemented for DM yielding
                 reasonable results. However, building an operational
                 tool requires more effort",
  notes =        "Jan 2000 not (yet) published. SQL queries generated.
                 Implemented in Visual Basic. Individuals are
                 conjenctions of predicates over database attributes
                 implemented as binary tables.

                 p8 DM specific limits on mutation of table rows. data
                 mining of FAA Aircraft incident records (cleaned up,
                 normalised) http://www.asy.faa.gov/asp/asy_fids.asp p9
                 User must specify mining question, beta fraction
                 coressponding to maximum fitness p10 individual must
                 contain at least two elementary expressions ad-hoc rule
                 no expression to cover more tha 10% of a domian.
                 profiles of risky flights.

                 ",
  size =         "13 pages",
}

@InProceedings{choi:1996:LANGA,
  author =       "Andy Choi",
  title =        "Optimizing Local Area Networks Using Genetic
                 Algorithms",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "467--472",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 GA paper",
}

@InCollection{choi:1995:OLANUGA,
  author =       "Andy Choi",
  title =        "Optimizing Local Area Networks Using Genetic
                 Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "49--58",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@MastersThesis{p.chong:mastersthesis,
  author =       "Fuey Sian Chong",
  title =        "A Java based Distributed Approach to Genetic
                 Programming on the Internet",
  school =       "Computer Science, University of Birmingham",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/p.chong/p.chong.msc.25-sep-98.ps.gz",
  size =         "103 pages",
  abstract =     "This paper presents a distributed approach to
                 parallelise Genetic Programming on the Internet. The
                 motivation for the approach is to harness the wealth of
                 computing resources available on the Internet to
                 provide the computing power required for solving
                 difficult problems. A distributed genetic programming
                 system termed DGP is developed in the Java programming
                 language to demonstrate the feasibility of distributing
                 genetic programming on the Internet. Unique features of
                 the DGP system include the use of Java Servlets to
                 handle the communication between DGP clients, the use
                 of a population pool to neutralise differences in
                 speeds of hosts, the interactive user interface and
                 graphical displays of the evolution process. The DGP
                 system has been implemented over the Internet and the
                 results are favourable. Experiments were conducted to
                 determine the performance of the DGP system. Results
                 showed that the DGP system has a much higher
                 probability of finding solutions as compared to the
                 distributed approaches taken in our previous studies
                 and the single population Genetic Programming.",
  notes =        "Code available at
                 ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/gp-code/DGP

                 Phyllis Chong Awarded a distinction in MSc in Advanced
                 Computer Science",
}

@TechReport{chong:1999:jDGPiTR,
  author =       "Fuey Sian Chong",
  title =        "Java based Distributed Genetic Programming on the
                 Internet",
  institution =  "University of Birmingham, School of Computer Science",
  year =         "1999",
  number =       "CSRP-99-7",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, DGP",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1999/CSRP-99-07.ps.gz",
  abstract =     "We proposed a distributed approach for parallelising
                 Genetic Programming on the Internet. The approach
                 harnesses the wealth of computing resources available
                 on the Internet to provide the computing power required
                 by Genetic Programming to solve hard problems. A
                 distributed genetic programming system termed DGP is
                 developed in the Java progamming language to
                 demonstrate the feasibility of our approach. Features
                 of the DGP system include the use of Java Servlets to
                 handle communication between distributed machines and
                 the use of a population pool to facilitate migrations.
                 In addition, the DGP system has an interactive user
                 interface for controlling the run and graphical
                 displays of the evolution process. The DGP system has
                 been implemented live over the Internet and the results
                 prove that the approach is feasible. An experiment was
                 conducted to determine the performance of the DGP
                 system and results showed that the DGP system has a
                 much higher probability of finding solutions than the
                 distributed approaches taken in our previous work and
                 the conventional single population Genetic Programming
                 approach.",
  notes =        "long version of chong:1999:jDGPi Phyllis Chong",
  size =         "8 pages",
}

@InProceedings{chong:1999:jDGPi,
  author =       "Fuey Sian Chong and W. B. Langdon",
  title =        "Java based Distributed Genetic Programming on the
                 Internet",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1229",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  note =         "Full text in technical report CSRP-99-7",
  keywords =     "genetic algorithms, genetic programming, DGP,
                 Distributed Computing, Java Applet / Application, World
                 Wide Computing, Internet, Servlets",
  ISBN =         "1-55860-611-4",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/p.chong/DGPposter.ps.gz",
  size =         "1 page",
  abstract =     "A distributed approach for parallelising Genetic
                 Programming (GP) on the Internet is proposed and its
                 feasibility demonstrated with a distributed GP system
                 termed DGP developed in Java. DGP is run successfully
                 across the world over the Internet on heterogeneous
                 platforms without any central co-ordination. The run
                 results and the outcome of an experiment to determine
                 DGP's performance are reported together with a
                 description of DGP.",
  notes =        "GECCO-99, part of banzhaf:1999:gecco99, A joint
                 meeting of the eighth international conference on
                 genetic algorithms (ICGA-99) and the fourth annual
                 genetic programming conference (GP-99)

                 see also chong:1999:jDGPi Phyllis Chong",
}

@InProceedings{chong:1999:parGA,
  author =       "Fuey Sian Chong",
  title =        "Java based Distributed Genetic Programming on the
                 Internet",
  booktitle =    "Evolutionary computation and parallel processing",
  year =         "1999",
  editor =       "Erick Cantu-Paz and Bill Punch",
  pages =        "163--166",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-illigal.ge.uiuc.edu/~cantupaz/parallel/chong.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/p.chong/GeccoWkShop.ps.gz",
  size =         "4 pages",
  notes =        "GECCO'99 WKSHOP Phyllis Chong",
}

@Misc{chong:1999:jDGPis,
  author =       "Fuey Sian Chong and W. B. Langdon",
  title =        "Java based Distributed Genetic Programming on the
                 Internet",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  pages =        "345",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, distributed,
                 evolutionary programming, Internet, java, parallel",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/p.chong/DGPposter.ps.gz",
  abstract =     "GECCO'99 graduate WKSHOP Phyllis Chong",
}

@InCollection{chong:2002:GAACG,
  author =       "Sanders Chong",
  title =        "Genetic Algorithms Applied to Computational Genomics",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "58--64",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InCollection{choo:2000:EDLBC,
  author =       "Shou-yen Choo",
  title =        "Emergence of a Division of Labor in a Bee Colony",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "98--107",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@Article{BC-telepar:97,
  author =       "B. Chopard and Y. Baggi and P. Luthi and J. F. Wagen",
  title =        "Wave Propagation and Optimal Antenna Layout using a
                 Genetic Algorithm",
  journal =      "Speedup",
  year =         "1997",
  volume =       "11",
  number =       "2",
  pages =        "42--47",
  month =        nov,
  note =         "TelePar Conference, EPFL, 1997",
  notes =        "SPEEDUP Journal speedup@cscs.ch

                 ",
}

@InProceedings{christensen:2002:EuroGP,
  title =        "An Analysis of {Koza}'s Computational Effort Statistic
                 for Genetic Programming",
  author =       "Steffen Christensen and Franz Oppacher",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "182--191",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "As research into the theory of genetic programming
                 progresses, more effort is being placed on
                 systematically comparing results to give an indication
                 of the effectiveness of sundry modifications to
                 traditional GP. The statistic that is commonly used to
                 report the amount of computational effort to solve a
                 particular problem with 99% probability is Koza's I(M,
                 i, z) statistic. This paper analyzes this measure from
                 a statistical perspective. In particular, Koza's I
                 tends to underestimate the true computational effort,
                 by 25% or more for commonly used GP parameters and run
                 sizes. The magnitude of this underestimate is
                 nonlinearly decreasing with increasing run count,
                 leading to the possibility that published results based
                 on few runs may in fact be unmatchable when replicated
                 at higher resolution. Additional analysis shows that
                 this statistic also under reports the generation at
                 which optimal results are achieved.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{chu:1999:DDCNDAGAA,
  author =       "Chao-Hsien Chu and G. Premkumar and Carey Chou and
                 Jianzhong Sun",
  title =        "Dynamic Degree Constrained Network Design: {A} Genetic
                 Algorithm Approach",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "141--148",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ciesielski:2002:poecigpbrosp,
  author =       "Vic Ciesielski and Dylan Mawhinney",
  title =        "Prevention of Early Convergence in Genetic Programming
                 by Replacement of Similar Programs",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "67--72",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
}

@TechReport{clack:1996:adca,
  author =       "Chris Clack and Jonny Farringdon and Peter Lidwell and
                 Tina Yu",
  title =        "An Adaptive Document Classification Agent",
  institution =  "University College London",
  year =         "1996",
  type =         "Research Note",
  number =       "RN/96/45",
  address =      "Computer Science, Gower Street, London, WC1E 6BT, UK",
  month =        "21 " # jun,
  note =         "Submitted to BCS-ES96",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/j.farringdon/GP/Papers-es96/paper02.html",
  abstract =     "The development of an intelligent text classification
                 application is discussed which utilises genetic
                 programming methods. Learning capabilities are used to
                 effect a adaptive system in order to meet the needs of
                 dynamic-information users. Deriving structure and
                 priority from text, target environments are discussed
                 where large volumes of (on-line) textual documents are
                 manipulated.",
  notes =        "3 figures as separte ps files in the same directory",
}

@TechReport{clack:1996:adcb,
  author =       "Chris Clack and Jonny Farringdon and Peter Lidwell and
                 Tina Yu",
  title =        "Autonomous Document Classification for Business",
  institution =  "University College London",
  year =         "1996",
  type =         "Research Note",
  number =       "RN/96/48",
  address =      "Computer Science, Gower Street, London, WC1E 6BT, UK",
  month =        jun,
  note =         "Appears in Autonomous Agents '97",
  keywords =     "genetic algorithms, genetic programming, Softbot,
                 agent architecture, pattern recognition, long term
                 adaptation and learning",
  URL =          "http://www.cs.ucl.ac.uk/staff/j.farringdon/GP/Papers-agent97/outline3.txt",
  abstract =     "With the continuing exponential growth of the Internet
                 and the more recent growth of business Intranets, the
                 commercial world is becoming increasingly aware of the
                 problem of electronic information overload. This has
                 encouraged interest in developing agents/softbots that
                 can act as electronic personal assistants and can
                 develop and adapt representations of users information
                 needs, commonly known as profiles.

                 As the result of collaborative research with Friends of
                 the Earth, an environmental issues campaigning
                 organisation, we have developed a general purpose
                 information classification agent architecture and have
                 applied it to the problem of document classification
                 and routing. Collaboration with Friends of the Earth
                 allows us to test our ideas in a non-academic context
                 involving high volumes of documents.

                 We use the technique of genetic programming (GP), (Koza
                 and Rice 1992), to evolve classifying agents. This is a
                 novel approach for document classification, where each
                 agent evolves a parse-tree representation of a user's
                 particular information need. The other unusual feature
                 of our research is the longevity of our agents and the
                 fact that they undergo a continual training process;
                 feedback from the user enables the agent to adapt to
                 the user's long-term information requirements.",
  notes =        "see also clack:1997:adcb",
}

@InProceedings{clack:1997:adcb,
  author =       "Chris Clack and Jonny Farringdon and Peter Lidwell and
                 Tina Yu",
  title =        "Autonomous Document Classification for Business",
  booktitle =    "The First International Conference on Autonomous
                 Agents (Agents '97)",
  year =         "1997",
  editor =       "W. Lewis Johnson",
  pages =        "201--208",
  address =      "Marina del Rey, California, USA",
  publisher_address = "1515 Broadway, New York, NY 10036, USA",
  month =        feb # " 5-8",
  organisation = "ACM SIGART",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-89791-877-0",
  URL =          "http://www.cs.ucl.ac.uk/staff/J.Farringdon/GP/Papers-agent97/agent97.html",
  size =         "11 pages",
  abstract =     "With the continuing exponential growth of the Internet
                 and the more recent growth of business Intranets, the
                 commercial world is becoming increasingly aware of the
                 problem of electronic information overload. This has
                 encouraged interest in developing agents/softbots that
                 can act as electronic personal assistants and can
                 develop and adapt representations of users information
                 needs, commonly known as profiles.

                 As the result of collaborative research with Friends of
                 the Earth, an environmental issues campaigning
                 organisation, we have developed a general purpose
                 information classification agent architecture and have
                 applied it to the problem of document classification
                 and routing. Collaboration with Friends of the Earth
                 allows us to test our ideas in a non-academic context
                 involving high volumes of documents.

                 We use the technique of genetic programming (GP), (Koza
                 and Rice 1992), to evolve classifying agents. This is a
                 novel approach for document classification, where each
                 agent evolves a parse-tree representation of a user's
                 particular information need. The other unusual features
                 of our research are the longevity of our agents and the
                 fact that they undergo a continual training process;
                 feedback from the user enables the agent to adapt to
                 the user's long-term information requirements.",
  notes =        "http://www.isi.edu/isd/AA97/info.html see also
                 clack:1996:adcb",
}

@TechReport{clack:1997:edc,
  author =       "Chris Clack",
  title =        "Software -- The Next Generation: Evolving Document
                 Classification",
  institution =  "UCL, Andersen Consulting",
  year =         "1997",
  type =         "white paper",
  address =      "University College London, Gower Street, London",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "55--67",
  notes =        "Part of {"}Emerging Technologies White Papers:
                 Software -- The Next Generation{"} which reports the
                 1996 workshop on Emerging technologies held in UCL
                 Computer Science dept. for Andersen Consulting's
                 Emerging Technologies Group and others.",
  size =         "13 pages",
}

@InCollection{clark:1995:PISW,
  author =       "Adam Clark",
  title =        "Predator-Prey Interactions in a Simulated World",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "59--64",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{clergue:2002:gecco,
  author =       "Manuel Clergue and Philippe Collard and Marco
                 Tomassini and Leonardo Vanneschi",
  title =        "Fitness Distance Correlation And Problem Difficulty
                 For Genetic Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "724--732",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, distance
                 between genotypes, fitness distance correlation,
                 problem difficulty, royal trees, trap functions",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)

                 Nominated for best at GECCO award",
}

@InProceedings{coates:1997:GPx3dw,
  author =       "T. Broughton and A. Tan and Paul S. Coates",
  title =        "The use of Genetic programing in Exploring 3{D} Design
                 Worlds",
  booktitle =    "CAAD Futures 97",
  year =         "1997",
  editor =       "Richard Junge",
  pages =        "885--917",
  address =      "Technical University Munich, Germany",
  month =        "4-6 " # aug,
  publisher =    "Kluwer Academic Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7923-4726-9",
  abstract =     "1) Using Genetic Programming in an interactive 3d
                 shape grammar (Amy Tan and P S Coates) A report of a
                 generative system combining genetic programming(GP) and
                 3D shape grammars. The reas oning that backs up the
                 basis for this work depends on the interpretation of
                 design as search In this system, a 3D form is a
                 computer program made up of functions (transformations
                 and terminals (building blocks). Each program evaluates
                 into a structure. Hence, in this instance a program is
                 synonymous with form. Building blocks of form are
                 platonic solids (box, cylinder....etc.). A Variety of
                 combinations of the simple affine transformations of
                 translation, scaling, rotation together with Boolean
                 operations of union, subtraction and intersection
                 performed on the building blocks generate different
                 configurations of 3D fo rms. Using to the methodology
                 of genetic programming, an initial population of such
                 programs are randomly generated,subjected to a test for
                 fitness (the eyeball test). Individual programs that
                 have passed the test are selected to be parents for
                 reproducing the next generation of programs via the
                 process of recombination. 2) Using a GA to evolve rule
                 sets to achieve a goal configuration (T.Broughton and
                 P.Coates). The aim of these experiments was to build a
                 framework in which a structure's form could be defined
                 by a set of instructions encoded into its genetic
                 make-up. This was achieved by combining a generative
                 rule system commonly used to model biological growth
                 with a genetic algorithm simulaoing the evolutionary
                 process of selection to evolve nn adaptive rule system
                 capable of replicating any preselected 3-D shape. The
                 generative modelling technique used is a string
                 rewnting Lindenmayer system the genes of the emergent
                 structures are the production rules of the L-system,
                 and the spatial representation of the structures uses
                 the geometry of iso-spatial dense-packed spheres.",
  notes =        "University of East London, GB",
}

@InProceedings{coelho:1998:xcsf,
  author =       "Leandro dos Santos Coelho and Antonio Augusto
                 Rodrigues Coelh",
  title =        "An Experimental and Comparative Study of Fuzzy {PID}
                 Controller Structures",
  booktitle =    "Advances in Soft Computing - Engineering Design and
                 Manufacturing",
  year =         "1998",
  editor =       "R. Roy and T. Furuhashi and P. K. Chawdhry",
  month =        "21-30 " # jun,
  keywords =     "Fuzzy logic control, Fuzzy PID Control, Experimental
                 process, Control applications.",
  ISBN =         "1-85233-062-7",
  URL =          "https://www.cranfield.ac.uk/wsc3/tech-sessions/papers/ic-2/ic-2.htm",
  abstract =     "Structures and design issues of fuzzy PID
                 (proportional-integral-derivative) controllers
                 (FLC-PID’s) are presented and evaluated in this paper.
                 Configuration and basic characteristic of several
                 structures of FLC-PID based on models proposed in the
                 literature — (PD + I), (PI + D conventional),
                 incremental (PD + I), (PD + PI) — are here reviewed and
                 implemented. FLC-PID’s are assessed on a horizontal
                 balance process, consisting of two propellers driven by
                 two DC motors. Such process offers control complexities
                 and can become unstable by using classical controllers.
                 Experimental results, robustness and performance of
                 FLC-PID’s are illustrated and discussed.",
  notes =        "WSC3",
}

@InProceedings{coletti:1999:CPLEMGA,
  author =       "Mark Coletti and Thomas D. Lash and Ryszard Michalski
                 and Craig Mandsager and Rida Moustafa",
  title =        "Comparing Performance of the Learnable Evolution Model
                 and Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "779",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{colin:1997:DMGP,
  author =       "Andre Colin",
  title =        "Data-Mining and Genetic Programming",
  journal =      "PC AI",
  year =         "1997",
  volume =       "11",
  number =       "5",
  pages =        "23",
  month =        sep # "/" # oct,
  publisher =    "Knowledge Technology, Inc.",
  address =      "Phoenix, AZ, USA",
  email =        "acolin@zurich.com.au",
  keywords =     "genetic algorithms, genetic programming, data mining",
  ISSN =         "0894-0711",
  URL =          "http://www.primenet.com/pcai/New_Home_Page/issues/pcai_11_5_toc.html#Editorial",
  size =         "3 pages",
  notes =        "easy going introduction to GP but little data mining
                 information.

                 Code available on line
                 http://www.primenet.com/pcai/New_Home_Page/pcai_info/All_Lists.html",
}

@InProceedings{collet:1999:IGAVRCP,
  author =       "Pierre Collet and Evelyne Lutton and Frederic Raynal
                 and Marc Schoenauer",
  title =        "Individual {GP}: an Alternative Viewpoint for the
                 Resolution of Complex Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "974--981",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, IFS,
                 fractals",
  ISBN =         "1-55860-611-4",
  URL =          "http://www-rocq.inria.fr/fractales/Publications/GP-467.ps.gz",
  abstract =     "An unususal GP implementation is proposed, based on a
                 more {"}economic{"} exploitation of the GP algorithm:
                 the {"}individual{"} approach, where each individual of
                 the population embodies a single function rather than a
                 set of functions. The final solution is then a set of
                 individuals. Examples are presented where results are
                 obtained more rapidly than with the conventional
                 approach, where all individuals of the final generation
                 but one are discarded.",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{collet:1999:RR-3849,
  author =       "Pierre Collet and Evelyne Lutton and Frederic Raynal
                 and Marc Schoenauer",
  title =        "Polar {IFS} + Individual Genetic Programming =
                 Efficient {IFS} Inverse Problem Solving",
  institution =  "INRIA",
  year =         "1999",
  number =       "RR-3849",
  address =      "Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le
                 Chesnay Cedex France",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-rocq.inria.fr/fractales/Publications/RR-PolarIFS.ps.gz",
  abstract =     "The inverse problem for Iterated Functions Systems
                 (finding an IFS whose attractor is a target 2D shape)
                 with non-affine IFS is a very complex task. Successful
                 approaches have been made using Genetic Programming,
                 but there is still room for improvement in both the IFS
                 and the GP parts. The main difficulty with non-linear
                 IFS is the efficient handling of contractance
                 constraints. This paper introduces Polar IFS, a
                 specific representation of IFS functions that shrinks
                 the search space to mostly contractive functions.
                 Moreover, the Polar representation gives direct access
                 to the fixed points of the functions, whereas the fixed
                 point of general non-linear IFS can only be numerically
                 estimated. On the evolutionary side, the
                 {"}individual{"} approach is similar to the Michigan
                 approach of Classifier Systems: each individual of the
                 population embodies a single function rather than the
                 whole IFS. A solution to the inverse problem is then
                 built from a set of individuals. Both improvements show
                 a drastic cut-down on CPU-time: good results are
                 obtained with small populations in few generations.",
  abstract =     "Lorsque l'on s'intéresse aux IFS (systèmes de
                 fonctions itérées) non affines, la résolution du
                 problème inverse (c'est-à-dire trouver l'IFS dont
                 l'attracteur approxime au mieux une forme
                 bidimensionnelle donnée) devient un problème très
                 complexe. Ce problème a déjà été résolu avec succès à
                 l'aide de stratégies de programmation génétique,
                 fondées sur une représentation des fonctions sous forme
                 d'arbres. La principale difficulté de cette approche
                 étant la gestion efficace des contraintes de
                 contractance sur les fonctions, nous proposons ici
                 l'emploi d'une représentation polaire des IFS non
                 affines, centrée sur le point fixe de chaque fonction.
                 Cette représentation a deux principaux avantages :

                 une contrainte simple sur la définition de la
                 composante radiale de chaque fonction assure sa
                 convergence vers un point fixe (le point central de sa
                 representation polaire),

                 l'accès au point fixe de chaque fonction est direct (il
                 n'est plus nécessaire de l'estimer comme dans
                 l'approche en coordonnées cartésiennes).

                 Nous présentons ensuite une stratégie originale de
                 programmation génétique, fondée sur une exploitation
                 plus {"}économique{"} des stratégies évolutionnaires :
                 l'approche {"}individuelle{"}, où chaque individu de la
                 population représente une seule fonction (au lieu d'un
                 IFS complet). La solution au problème étant fournie par
                 un ensemble d'individus de la population finale, des
                 résultats sont obtenus de façon plus rapide et plus
                 efficace que dans la version classique où tous les
                 individus de la population finale sauf un (le meilleur)
                 sont écartés.",
  notes =        "in english",
  size =         "30 pages",
}

@Article{collet:2000:IFSpGP,
  author =       "Pierre Collet and Evelyne Lutton and Frederic Raynal
                 and Marc Schoenauer",
  title =        "Polar {IFS}+Parisian Genetic Programming=Efficient
                 {IFS} Inverse Problem Solving",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "4",
  pages =        "339--361",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, fractals,
                 Iterated Functions System, inverse problem for IFS,
                 polar IFS",
  ISSN =         "1389-2576",
  URL =          "http://www-rocq.inria.fr/fractales/Publications/PolarIFS-GPEM-New.ps.gz",
  abstract =     "This paper proposes a new method for treating the
                 inverse problem for Iterated Functions Systems (IFS)
                 using Genetic Programming. This method is based on two
                 original aspects. On the fractal side, a new
                 representation of the IFS functions, termed Polar
                 Iterated Functions Systems, is designed, shrinking the
                 search space to mostly contractive functions. Moreover,
                 the Polar representation gives direct access to the
                 fixed points of the functions. On the evolutionary
                 side, a new variant of GP, the {"}Parisian{"} approach
                 is presented. The paper explains its similarity to the
                 {"}Michigan{"} approach of Classifier Systems: each
                 individual of the population only represents a part of
                 the global solution. The solution to the inverse
                 problem for IFS is then built from a set of
                 individuals. A local contribution to the global fitness
                 of an IFS is carefully defined for each one of its
                 member functions and plays a major role in the fitness
                 of each individual. It is argued here that both
                 proposals result in a large improvement in the
                 algorithms. We observe a drastic cut-down on CPU-time,
                 obtaining good results with small populations in few
                 generations.",
}

@TechReport{collet:2001:RR4421,
  author =       "Pierre Collet and Marc Schoenauer and Evelyne Lutton
                 and Jean Louchet",
  title =        "{EASEA} : un langage de spécification pour les
                 algorithmes évolutionnaires",
  institution =  "INRIA",
  year =         "2001",
  number =       "RR4218",
  address =      "Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le
                 Chesnay Cedex France",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, EASEA, Java",
  URL =          "http://www-rocq.inria.fr/fractales/Publications/RR4218.ps.gz",
  abstract =     "Contrairement aux apparences, il n'est pas simple
                 d'&eacute;crire un programme informatique
                 r&eacute;alisant un algorithme &eacute;volutionnaire,
                 d'autant que le manque de langage
                 sp&eacute;cialis&eacute; oblige l'utilisateur &agrave;
                 utiliser C, C++ ou JAVA. La plupart des algorithmes
                 &eacute;volutionnaires, cependant, poss&egrave;dent une
                 structure commune, et la part r&eacute;ellement
                 sp&eacute;cifique est constitu&eacute;e par une faible
                 portion du code. Ainsi, il semble que rien ne s'oppose
                 en th&eacute;orie &agrave; ce qu'un utilisateur puisse
                 construire, puis faire tourner son algorithme
                 &eacute;volutionnaire &agrave; partir d'une interface
                 graphique, afin de limiter son effort de programmation
                 &agrave; la fonction &agrave; optimiser.
                 L'&eacute;criture d'une telle interface graphique pose
                 tout d'abord le probl&egrave;me de sauver et de
                 recharger l'algorithme &eacute;volutionnaire sur lequel
                 l'utilisateur travaille, puis celui de transformer ces
                 informations en code compilable. Cela ressemble fort
                 &agrave; un language de sp&eacute;cification et son
                 compilateur. Le logiciel EASEA a &eacute;t&eacute;
                 cr&eacute;&eacute; dans ce but, et &agrave; notre
                 connaissance, il est actuellement le seul et unique
                 compilateur de langage sp&eacute;cifique aux
                 algorithmes &eacute;volutionnaires. Ce rapport
                 d&eacute;crit comment EASEA a &eacute;t&eacute;
                 construit et quels sont les probl&egrave;mes qui
                 restent &agrave; r&eacute;soudre pour achever son
                 implantation informatique.",
  abstract =     "Evolutionary algorithms are not straightforward to
                 implement and the lack of any specialised language
                 forces users to write their algorithms in C, C++ or
                 JAVA. However, most evolutionary algorithms follow a
                 similar design, and the only really specific part is
                 the code representing the problem to be solved.
                 Therefore, it seems that nothing, in theory, could
                 prevent a user from being able to design and run his
                 evolutionary algorithm from a Graphic User Interface,
                 without any other programming effort than the function
                 to be optimised. Writing such a GUI rapidly poses the
                 problem of saving and reloading the evolutionary
                 algorithm on which the user is working, and translating
                 the information into compilable code. This very much
                 sounds like a specifying language and its compiler. The
                 EASEA software was created on this purpose, and to our
                 knowledge, it is the first and only usable compiler of
                 a language specific to evolutionary algorithms. This
                 reprot describes how EASEA has been designed and the
                 problems which needed to be solved to achieve its
                 implementation.",
  notes =        "in english",
  size =         "17 pages",
}

@InProceedings{collins:1998:mbiaia,
  author =       "J. J. Collins",
  title =        "Modeling the Behaviour of Interacting Autonomous
                 Intelligent Agents",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.csis.ul.ie/staff/jjcollins/gp98.html",
  notes =        "GP-98LB, GP-98PhD Student Workshop",
}

@InProceedings{collins:1999:ACSSVT,
  author =       "Trevor D. Collins",
  title =        "A Comparison of Search Space Visualization
                 Techniques",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "780",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{collins:1999:GPMRNS,
  author =       "J. J. Collins and Lucia Sheehan and Conor Casey",
  title =        "Genetic Planner for a Mobile Robot Navigation System",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "782",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{collins:1999:NFOPI,
  author =       "J. J. Collins and Conor Ryan",
  title =        "Non-stationary Function Optimization using Polygenic
                 Inheritance",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "781",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{Comellas:1998:GPD,
  author =       "F. Comellas and G. Gim{\'e}nez",
  title =        "Genetic Programming to Design Communication Algorithms
                 for Parallel Architectures",
  journal =      "Parallel Processing Letters",
  year =         "1998",
  volume =       "8",
  number =       "4",
  pages =        "549--560",
  keywords =     "genetic algorithms, genetic programming, broadcasting,
                 networks, butterfly graph",
  size =         "12 pages",
  abstract =     "Broadcasting is an information dissemination problem
                 in which a message originating at one node of a
                 communication network (modeled as a graph) is to be
                 sent to all other nodes as quickly as possible. This
                 paper describes a new way of producing broadcasting
                 schemes using genetic programming. This technique has
                 proven successful by easily finding optimal algorithms
                 for several well-known families of networks (grids,
                 hypercubes and cycle connected cubes) and has indeed
                 generated a new scheme for butterflies that improves
                 the known upper bound for the broadcasting time of
                 these networks.",
  notes =        "GPQUICK. Tried on 4 problems (5x5 directed grid,
                 torroidal, hypercube, cube connected cycles) finds
                 known optima.

                 {"}5.5 Butterfly graph For these graphs no optimal
                 broadcasting algorithm is known... we improve the upper
                 bound to BF_k \le 2k-2{"} for k=7,8...16",
  CODEN =        "PPLTEE",
  ISSN =         "0129-6264",
  URL =          "http://www-mat.upc.es/~comellas/genprog/genprog.html",
  acknowledgement = ack-nhfb,
  bibdate =      "Mon Nov 09 07:22:43 1998",
}

@Article{ga95aCona:1995:dGPs,
  author =       "John Cona",
  title =        "Developing a Genetic Programming System",
  journal =      "AI Expert",
  year =         "1995",
  pages =        "20--29",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, C++, Object
                 Orientated",
  size =         "10 pages",
  abstract =     "We can use an object-oriented C++ approach to develop
                 gentic base classes. Discusses practical speed/memory
                 tradeoffs for an (IBM) PC environment.",
  notes =        "BLDSC shelfmark 0772.341000, UK Floor 6-1 {"}Exciting
                 prospects of language and communication{"},
                 {"}memory{"}, notes on more recent features of C++.

                 ",
}

@InProceedings{congdon:2000:GA,
  author =       "Clare Bates Congdon and Emily F. Greenfest",
  title =        "Gaphyl: {A} genetic algorithm approach to cladistics",
  booktitle =    "Data Mining with Evolutionary Algorithms",
  year =         "2000",
  editor =       "Alex A. Freitas and William Hart and Natalio Krasnogor
                 and Jim Smith",
  pages =        "85--88",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{conrads:1998:ssdGP,
  author =       "Markus Conrads and Peter Nordin and Wolfgang Banzhaf",
  title =        "Speech Sound Discrimination With Genetic Programming",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "113--129",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "The question that we investigate in this paper is,
                 whether it is possible for Genetic Programming to
                 extract certain regularities from raw time series data
                 of human speech. We examine whether a genetic
                 programming algorithm can find programs that are able
                 to discriminate certain spoken vowels and consonan ts.
                 We present evidence that this can indeed be achieved
                 with a surprisingly simple approach that does not need
                 preprocessing. The data we have collec ted on the
                 system's behavior show that even speaker-independent
                 discriminatio n is possible with GP.",
  notes =        "EuroGP'98",
}

@InCollection{coon:1994:csgp,
  author =       "Brett W. Coon",
  title =        "Circuit Synthesis through Genetic Programming",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "11--20",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html

                 GP used to synthesis simple logic circuits. Able to
                 simplify them. On some problems able to do as well as
                 commercial tool {"}Synopsys{"}.",
}

@InProceedings{cooper:2002:gecco,
  author =       "Jason Cooper and Chris Hinde",
  title =        "Comparison Of Evolving Against Peers And Fixed
                 Opponents Using Corewars",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "887",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, poster paper,
                 Corewars",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@Article{cordon:1999:sedpuhedat,
  author =       "Oscar Cordon and Francisco Herrera and Luciano
                 Sanchez",
  title =        "Solving Electrical Distribution Problems Using Hybrid
                 Evolutionary Data Analysis Techniques",
  journal =      "Applied Intelligence",
  year =         "1999",
  volume =       "10",
  number =       "1",
  pages =        "5--24",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, electrical
                 engineering, data analysis, evolutionary algorithms,
                 genetic algorithm program, genetic fuzzy rule-based
                 systems",
  ISSN =         "0924-669X",
  notes =        "Two modeling problems from the Spanish electrical
                 system are solved. In each a comparison of statistical
                 regression, GA-P, genetic fuzzy rule based and
                 artificial neural networks is made. Uses modification
                 of howard:1995:GA-P",
}

@InProceedings{cordon:ppsn2002:pp710,
  author =       "Oscar Cordon and Enrique Herrera-Viedma and Mar'ia
                 Luque",
  title =        "Evolutionary Learning of Boolean Queries by
                 Multiobjective Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "710 ff.",
  year =         "2002",
  editor =       "J.-J. Merelo Guerv\'os and P. Adamidis and H.-G. Beyer
                 and J.-L. Fern\'andez-Villaca\~nas and H.-P. Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Keywords: Activities::Pattern recognition and
                 classification/datamining, Application::Web services,
                 Technique::Genetic programming - general,
                 Technique::Multi-objective",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900710.pdf",
}

@InProceedings{corney:1999:NSMUGP,
  author =       "David Corney and Ian Parmee",
  title =        "{N}-Dimensional Surface Mapping Using Genetic
                 Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1230",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  URL =          "http://www.cs.ucl.ac.uk/staff/D.Corney/GECCO_Poster.zip",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 MSc Thesis -- text from
                 http://www.cs.ucl.ac.uk/staff/D.Corney/MSc_thesis_abstract.html

                 N-Dimensional Surface Mapping Using Genetic
                 Programming

                 This work introduces an extension to Genetic
                 Programming (GP) known as {"}GP-UDF{"} which uses
                 multiple User-Defined Functions (UDFs) to solve
                 surface-mapping problems. These UDFs are high-level
                 primitives, such as hills and polynomials, which
                 compress the information required to map a surface.
                 UDFs can be used to add real-world knowledge to a
                 genetic search, and also to analyse and classify
                 high-dimensional surfaces. GP-UDF also produces more
                 readable solutions than standard GP.

                 The results show that, for the problems considered,
                 GP-UDF does not produce more accurate models than
                 standard GP. However, the results also suggest that
                 GP-UDF could be used as a {"}landscape classifier{"}, a
                 tool for analysing high-dimensional surfaces to
                 identify characteristic features.

                 An important consideration in systems identification is
                 the transparency (i.e. readability), of a model. GP-UDF
                 is compared with neural networks (both MLP and RBF
                 networks), and is shown to be far more readable, with
                 the cost of being less accurate.",
}

@InProceedings{cad_sac98,
  author =       "F. Corno and M. Sonza Reorda and G. Squillero",
  title =        "The Selfish Gene Algorithm: a New Evolutionary
                 Optimization Strategy",
  booktitle =    "SAC: ACM Symposium on Applied Computing",
  year =         "1998",
  pages =        "349--355",
  keywords =     "Genetic Algorithms, Approximate Methods, Equivalence
                 Checking, Evolutionary Algorithms, Gate-Level,
                 Simulation-Based Approaches",
  URL =          "http://www.cad.polito.it/FullDB/exact/sac98.html",
  URL =          "http://www.cad.polito.it/pap/db/sac98.pdf",
  keywords =     "Approximate Methods, Evolutionary Algorithms, Selfish
                 Gene",
  abstract =     "This paper proposes a new general approach for
                 optimization algorithms in the Evolutionary Computation
                 field. The approach is inspired by the Selfish Gene
                 theory, an interpretation of the Darwinian theory given
                 by the biologist Richard Dawkins, in which the basic
                 element of evolution is the gene, rather than the
                 individual. The paper defines the Selfish Gene
                 Algorithm, that implements such a view of the evolution
                 mechanism. We tested the approach by implementing a
                 Selfish Gene Algorithm on a case study, and we found
                 better results than those provided by a Genetic
                 Algorithm on the same problem and with the same fitness
                 function.",
}

@InProceedings{cad_iccd98a,
  author =       "F. Corno and M. Sonza Reorda and G. Squillero",
  title =        "{VEGA}: {A} Verification Tool Based on Genetic
                 Algorithms",
  booktitle =    "ICCD: International Conference on Circuit Design",
  year =         "1998",
  pages =        "321--326",
  URL =          "http://www.cad.polito.it/FullDB/exact/iccd98a.html",
  URL =          "http://www.cad.polito.it/pap/db/iccd98a.pdf",
  abstract =     "While modern state-of-the-art optimization techniques
                 can handle designs with up to hundreds of flip-flops,
                 equivalence verification is still a challenging task in
                 many industrial design flows. This paper presents a new
                 verification methodology that, while sacrificing
                 exactness, is able to handle larger circuits and give
                 designers the opportunity to trade off CPU time with
                 confidence on the result. The proposed methodology is
                 able to fruitfully support an exact verification tool,
                 dramatically increasing the confidence on the validity
                 of an optimization process. A prototypical tool has
                 been developed and preliminary experimental results
                 that support this claim are shown in the paper.",
}

@InProceedings{corno:2000:avpi,
  author =       "Fulvio Corno and Matteo Sonza Reorda and Giovannie
                 Squillero",
  title =        "Automatic Validation of Protocol Interfaces Described
                 in {VHDL}",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and George D. Smith
                 and David Corne and Martin Oates and Emma Hart and Pier
                 Luca Lanzi and Egbert Jan Willem and Yun Li and Ben
                 Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "205--213",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, ASIC, Approximate Methods,
                 Evolutionary Algorithms, Gate-Level, Low Power, Selfish
                 Gene, Simulation-Based Approaches",
  ISBN =         "3-540-67353-9",
  URL =          "http://www.cad.polito.it/FullDB/exact/evotel2000a.html",
  abstract =     "Modern VLSI design methodologies and manufacturing
                 technologies are making circuits increasingly fast. The
                 quest for higher circuit performance and integration
                 density stems from fields such as the telecommunication
                 one where high speed and capability of dealing with
                 large data sets is mandatory. The design of high-speed
                 circuits is a challenging task, and can be carried out
                 only if designers can exploit suitable CAD tools. Among
                 the several aspects of high-speed circuit design,
                 controlling power consumption is today a major issue
                 for ensuring that circuits can operate at full speed
                 without damages. In particular, tools for fast and
                 accurate estimation of power consumption of high-speed
                 circuits are required. In this paper we focus on the
                 problem of predicting the maximum power consumption of
                 sequential circuits. We formulate the problem as a
                 constrained optimization problem, and solve it
                 resorting to an evolutionary algorithm. Moreover, we
                 empirically assess the effectiveness of our problem
                 formulation with respect to the classical unconstrained
                 formulation. Finally, we report experimental results
                 assessing the effectiveness of the prototypical tool we
                 implemented.",
  notes =        "EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel,
                 EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland,
                 UK, April 17, 2000
                 Proceedings

                 http://evonet.dcs.napier.ac.uk/evoworkshops/

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67353-9",
}

@InProceedings{corno:2002:emctpi,
  author =       "F. Corno and G. Cumani and M. Sonza Reorda and G.
                 Squillero",
  title =        "Efficient Machine-Code Test-Program Induction",
  booktitle =    "CEC2002: Congress on Evolutionary Computation",
  year =         "2002",
  address =      "Honolulu, Hawaii, USA",
  keywords =     "genetic algorithms, genetic programming, ATPG,
                 Approximate Methods, Evolutionary Algorithms,
                 Micro-Processors, Simulation-Based Approaches",
  URL =          "http://www.cad.polito.it/pap/db/cec2002.pdf",
  abstract =     "Technology advances allow integrating on a single chip
                 entire system, including memories and peripherals. The
                 test of these devices is becoming a major issue for
                 manufacturing industries. This paper presents a
                 methodology for inducing test-programs similar to
                 genetic programming. However, it includes the ability
                 to explicitly specify registers and resorts to directed
                 acyclic graphs instead of trees. Moreover, it exploits
                 a database containing the assembly-level semantic
                 associated to each graph node. This approach is
                 extremely efficient and versatile: candidate solutions
                 are translated into source-code programs allowing
                 millions of evaluations per second. The proposed
                 approach is extremely versatile: the macro library
                 allows easily changing target processor and
                 environment. The approach was verified on three
                 processors with different instruction sets, different
                 formalisms and different conventions. A complete set of
                 experiments on a test function are also reported for
                 the SPARC processor.",
}

@InProceedings{Costa:1997:acsqrscgp,
  author =       "Paolo Costa",
  title =        "A Methodology for the Analysis of Complex Systems
                 based on Qualitative Reasoning, Stochastic Complexity
                 and Genetic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "35--41",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{costa:1999:GGOMINP,
  author =       "Lino Costa and Pedro Oliveira",
  title =        "{GA}s in Global Optimization of Mixed Integer
                 Non-Linear Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1773",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cotta:1999:ISDOFRTGR,
  author =       "Carlos Cotta and Enrique Alba and Jose M. Troya",
  title =        "Improving the Scalability of Dynastically Optimal
                 Forma Recombination by Tuning the Granularity of the
                 Representation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "783",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{cotta:ppsn2002:pp720,
  author =       "Carlos Cotta and Pablo Moscato",
  title =        "Inferring Phylogenetic Trees Using Evolutionary
                 Algorithms",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "720 ff.",
  year =         "2002",
  editor =       "J.-J. Merelo Guerv\'os and P. Adamidis and H.-G. Beyer
                 and J.-L. Fern\'andez-Villaca\~nas and H.-P. Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Keywords: Application::Biology and chemistry,
                 Technique::Comparisons of representations",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900720.pdf",
}

@Article{craig:1999:gpds,
  author =       "Iain Craig",
  title =        "Genetic Programming and Data Structures",
  journal =      "Robotica",
  year =         "1999",
  volume =       "17",
  number =       "4",
  pages =        "462",
  note =         "Review",
  keywords =     "genetic algorithms, genetic programming",
  size =         "0.25 pages",
  notes =        "Review of langdon:book",
}

@InProceedings{icga85:cramer,
  author =       "Nichael Lynn Cramer",
  title =        "A representation for the Adaptive Generation of Simple
                 Sequential Programs",
  year =         "1985",
  booktitle =    "Proceedings of an International Conference on Genetic
                 Algorithms and the Applications",
  address =      "Carnegie-Mellon University, Pittsburgh, PA, USA",
  month =        "24-26 " # jul,
  editor =       "John J. Grefenstette",
  pages =        "183--187",
  size =         "5 pages",
  URL =          "ftp://ftp.bbn.com/pub/ncramer/gp/icga85.txt",
  keywords =     "genetic algorithms, genetic programming, memory",
  abstract =     "An adaptive system for generating short sequential
                 computer functions is described. The created functions
                 are written in the simple {"}number-string{"} language
                 JB, and in TB, a modified version of JB with a
                 tree-like structure. These languages have the feature
                 that they can be used to represent well-formed, useful
                 computer programs while still being amenable to
                 suitably defined genetic operators. The system is used
                 to produce two-input, single-output multiplication
                 functions that are concise and well-defined. Future
                 work, dealing with extensions to more complicated
                 functions and generalizations of the techniques, is
                 also discussed.",
  notes =        "The earliest description of the tree-like
                 representation and operators for use in the application
                 of Genetic Algorithms to computer programs -
                 N.L.Cramer

                 Evolves a multiplier, {"}72% more often than control
                 sample{"} {"}PL- not fully Turing Equivalent{"},
                 addition of :SET and :BLOCK lead to JB language (nb a
                 list of statements language). JB has problems with
                 crossover -> TB which is as JB but instead of calls to
                 other statements, these other statements are expanded
                 in the first yielding a tree shaped syntax. Crossover
                 operator changed to deal with sub trees! Both languages
                 contain small numbers of global integers. TB Mutation
                 restricted to frindges of tree, ie leaves or first
                 level functions. Inversion: crossover within same
                 program! Goldberg(1989, p 303) says {"}Cramer does not
                 present any results from the use of JB in any genetic
                 trials; however he abandoned these first efforts
                 because of some limited computational
                 experiments{"}.

                 Fitness based, to some extent, upon internals of
                 program. Limits on prog size via fitness. Forced
                 timeout Goldberg(1987) says timed out progs fitness was
                 calculated.

                 =>Smith,S.F. IJCAI-83

                 Publisher not known, sponsored by USA Navy.",
}

@InProceedings{crapper:1997:mrrr,
  author =       "P. F. Crapper and P. A. Whigham",
  title =        "Modelling Rainfall-runoff Relationships",
  booktitle =    "24th Hydrology and Water Resources Symposium",
  year =         "1997",
  address =      "Auckland, New Zealand",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{crawford:1999:MGTNSV,
  author =       "Kelly D. Crawford and Michael D. McCormack and Donald
                 J. MacAllister",
  title =        "Modified Gradient Techniques for Normalized Solution
                 Vectors",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1498--1503",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{crawford-marks:2002:gecco,
  author =       "Raphael Crawford-Marks and Lee Spector",
  title =        "Size Control Via Size Fair Genetic Operators In The
                 {PushGP} Genetic Programming System",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "733--739",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InCollection{creighton:2000:SSOGA,
  author =       "Steven L. Creighton",
  title =        "Structural Shape Optimization using a Genetic
                 Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "108--116",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{crepeau:1995:GEMS,
  author =       "Ronald L. Crepeau",
  title =        "Genetic Evolution of Machine Language Software",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "121--134",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  size =         "14 pages",
  abstract =     "Z80 Machine code evolved to write {"}Hello World{"}
                 660 instructions and 255 byte RAM (modular arithmentic
                 used to address indexed memory)",
  notes =        "GEMS genetic evolution of machine language software
                 Breeding system similar to crowding and Tackett's
                 Softbrood selection (max litter size of 12). GA like
                 crossover acts on code and contents of memory. Pool of
                 1500 member 0.20 mutation rate.

                 {"}indicates that the problem difficulty, over the
                 range of the test and in terms of required spawns,
                 while increasing rapidly, does not appear to be
                 cominatorial or exponential{"} (suggests O(n**3)
                 ).

                 Discussion of statistics of number of useful terminals
                 in random and later populations.

                 Memory initialised to random values part of
                 rosca:1995:ml",
}

@InCollection{Cretin:al:EA95,
  author =       "G. Cretin and E. Lutton and J. Levy-Vehel and P.
                 Glevarec and C. Roll",
  title =        "Mixed {IFS}: Resolution of the Inverse Problem Using
                 Genetic Programming",
  booktitle =    "Artificial Evolution",
  publisher =    "Springer Verlag",
  year =         "1996",
  editor =       "J.-M. Alliot and E. Lutton and E. Ronald and M.
                 Schoenauer and D. Snyers",
  volume =       "1063",
  series =       "LNCS",
  pages =        "247--258",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Selected papers from two conferences: Evolution
                 Artificielle 94 and Evolution Artificielle 95
                 http://www.cmap.polytechnique.fr/www.eark/ea95.html

                 see also lutton:1995:IFScs",
}

@InProceedings{cribbs:1999:AMGAA,
  author =       "H. Brown Cribbs III",
  title =        "Aircraft Maneuvering via Genetics-Based Adaptive
                 Agent",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1249--1256",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{crosbie:1995:aGPid,
  title =        "Applying Genetic Programming to Intrusion Detection",
  author =       "Mark Crosbie and Eugene H. Spafford",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "1--8",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InProceedings{crosbie:1996:eedp,
  author =       "Mark Crosbie and Eugene H. Spafford",
  title =        "Evolving Event Driven Programs",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "273--278",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@Article{Csukas:1998:CI,
  author =       "Bela Csukas and Sandor Balogh",
  title =        "Combining genetic programming with generic simulation
                 models in evolutionary synthesis",
  journal =      "Computers in Industry",
  volume =       "36",
  pages =        "181--197",
  year =         "1998",
  number =       "3",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V2D-3VW737S-3/1/87e285c0690af97d9d081c4f2582fdcd",
  abstract =     "In the proposed combined model of the engineering
                 synthesis, the simulation and the parametric design are
                 organized by the genetic building elements, while the
                 genetic possibilities are evaluated by the experiences,
                 obtained from the detailed dynamic simulation. Using
                 this methodology, a new, integrated toolkit can de
                 developed for the creative problem solving in
                 (chemical) process engineering. The combination of the
                 structural modeling with the genetic programming
                 suggests a possible theoretical framework and proposes
                 a practical methodology for the solution of the various
                 synthesis (design, planning, scheduling, ...)
                 problems.",
}

@InProceedings{cvetkovic:1999:UPGMO,
  author =       "Dragan Cvetkovic and Ian C. Parmee",
  title =        "Use of Preferences for {GA}-based Multi-objective
                 Optimisation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1504--1509",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{daida:1995:bsmch,
  author =       "J. M. Daida and S. J. Ross and B. C. Hannan",
  title =        "Biological Symbiosis as a Metaphor for Computational
                 Hybridization",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "248--255",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Algorithms",
  ISBN =         "1-55860-370-0",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/icga95.pdf",
  size =         "8 pages",
}

@InProceedings{Daida:1995:SARice,
  author =       "J. M. Daida and J. D. Hommes and S. J. Ross and J. F.
                 Vesecky",
  title =        "Extracting curvilinear features from {SAR} images of
                 arctic ice: Algorithm discovery using the genetic
                 programming paradigm",
  booktitle =    "Proceedings of IEEE International Geoscience and
                 Remote Sensing",
  year =         "1995",
  editor =       "T. Stein",
  pages =        "673--675",
  address =      "Florence, Italy",
  publisher_address = "Washington",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_GP.pdf",
  notes =        "The research on algorithm discovery uses the genetic
                 programming paradigm to assist geoscientists in
                 extracting textural features from satellite synthetic
                 aperture radar imagery (i.e., ERS-1). Manual methods
                 are extremely time consuming and limited to a few
                 frames (in this case, a 1k by 1k low-res data product,
                 or a 8k by 8k hi-res data product). Desirable are
                 semi-automated, automated, or computer-assisted
                 algorithm developmental tools for data analysis.
                 (gp-list 13 Apr 95)

                 Firenze, Italy",
}

@InProceedings{Daida:1995:ehspsSAR,
  author =       "J. M. Daida and A. Freeman and R. Onstott",
  title =        "Evaluation of hybrid symbiotic systems on segmenting
                 {SAR} imagery",
  booktitle =    "Proceedings of IEEE International Geoscience and
                 Remote Sensing",
  year =         "1995",
  editor =       "T. Stein",
  pages =        "1415--1417",
  address =      "Florence, Italy",
  publisher_address = "Washington",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_symbiosis.pdf",
  notes =        "

                 Invited Paper Firenze, Italy",
}

@InProceedings{Daida:1995:mtssw,
  author =       "J. M. Daida and D. E. Lund and C. Wolf and G. A.
                 Meadows and K. Schroeder and J. F. Vesecky and D. R.
                 Lyzenga and R. Bertram",
  title =        "Measuring topography of small-scale waves",
  booktitle =    "Proceedings of IEEE International Geoscience and
                 Remote Sensing",
  year =         "1995",
  editor =       "T. Stein",
  pages =        "1881--1883",
  address =      "Florence, Italy",
  publisher_address = "Washington",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_GA.pdf",
  notes =        "

                 Firenze, Italy",
}

@InCollection{daida:1996:aigp2,
  author =       "Jason M. Daida and Jonathan D. Hommes and Tommaso F.
                 Bersano-Begey and Steven J. Ross and John F. Vesecky",
  title =        "Algorithm Discovery Using the Genetic Programming
                 Paradigm: Extracting Low-Contrast Curvilinear Features
                 from {SAR} Images of Arctic Ice",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "417--442",
  chapter =      "21",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming, GAIA",
  ISBN =         "0-262-01158-1",
  URL =          "ftp://gip.eecs.umich.edu/people/daida/gp2/Cha21_GP2.ps",
  notes =        "see also
                 http://www.sprl.umich.edu/acers/gaia/aigpGaia.html",
}

@InProceedings{daida:1996:scas,
  author =       "J. M. Daida and C. S. Grasso and S. A. Stanhope and S.
                 J. Ross",
  title =        "Symbionticism and Complex Adaptive Systems {I}:
                 Implications of Having Symbiosis Occur in Nature",
  booktitle =    "Evolutionary Programming V: Proceedings of the Fifth
                 Annual Conference on Evolutionary Programming",
  year =         "1996",
  editor =       "Lawrence J. Fogel and Peter J. Angeline and Thomas
                 Baeck",
  pages =        "177--186",
  address =      "San Diego",
  publisher_address = "Cambridge, MA, USA",
  month =        feb # " 29-" # mar # " 3",
  publisher =    "MIT Press",
  ISBN =         "0-262-06190-2",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/EP96_symbiosis.pdf",
  notes =        "EP-96, Invited Paper
                 http://www.natural-selection.com/eps/EP96.html",
}

@InProceedings{daida:1996:cadic,
  author =       "Jason M. Daida and Tommaso F. Bersano-Begey and Steven
                 J. Ross and John F. Vesecky",
  title =        "Computer-Assisted Design of Image Classification
                 Algorithms: Dynamic and Static Fitness Evaluations in a
                 Scaffolded Genetic Programming Environment",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "279--284",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/GP96_image.pdf",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{daida:1996:ircERSSARias,
  author =       "J. M. Daida and R. G. Onstott and T. F. Bersano-Begey
                 and S. J. Ross and J. F. Vesecky",
  title =        "Ice Roughness Classification and {ERS} {SAR} Imagery
                 of Arctic Sea Ice: Evaluation of Feature-Extraction
                 Algorithms by Genetic Programming",
  booktitle =    "Proceedings of the 1996 International Geoscience and
                 Remote Sensing Symposium",
  year =         "1996",
  pages =        "1520--1522",
  publisher_address = "Washington",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GP_Valid.pdf",
}

@InProceedings{daida:1996:efxa,
  author =       "J. M. Daida and T. F. Bersano-Begey and S. J. Ross and
                 J. F. Vesecky",
  title =        "Evolving Feature-Extraction Algorithms: Adapting
                 Genetic Programming for Image Analysis in Geoscience
                 and Remote Sensing",
  booktitle =    "Proceedings of the 1996 International Geoscience and
                 Remote Sensing Symposium",
  year =         "1996",
  pages =        "2077--2079",
  publisher_address = "Washington",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GP.pdf",
}

@InProceedings{daida:1996:,
  author =       "J. M. Daida and R. R. Bertram and D. R. Lyzenga and C.
                 Wolf and D. T. Walker and S. A. Stanhope and G. A.
                 Meadows and J. F. Vesecky and D. E. Lund",
  title =        "Measuring Small-Scale Water Surface Waves: Nonlinear
                 Interpolation and Integration Techniques for Slope
                 Image Data",
  booktitle =    "Proceedings of the 1996 International Geoscience and
                 Remote Sensing Symposium",
  year =         "1996",
  pages =        "2219--2221",
  publisher_address = "Washington",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GA/igarss96_GAfig.pdf",
  notes =        "note: these pages are reverse ordered",
}

@InProceedings{daida:1997:vrmGP,
  author =       "Jason Daida and Steven Ross and Jeffrey McClain and
                 Derrick Ampy and Michael Holczer",
  title =        "Challenges with Verification, Repeatability, and
                 Meaningful Comparisons in Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "64--69",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/GP97challenges.pdf",
  notes =        "GP-97",
}

@InProceedings{Daida:1997:taging,
  author =       "Jason M. Daida and Robert R. Bertram and Catherine S.
                 Grasso and Stephen A. Stanhope",
  title =        "Tagging as a Means for Self-Adaptive Hybridization",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "42--50",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InCollection{daida:1999:aigp3,
  author =       "Jason M. Daida and Robert R. Bertram and John A.
                 {Polito~2} and Stephen A. Stanhope",
  title =        "Analysis of Single-Node (Building) Blocks in Genetic
                 Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "10",
  pages =        "217--241",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{daida:1999:fogp,
  author =       "Jason M. Daida",
  title =        "Reconnoiter by Candle: Identifying Assumptions in
                 Genetic Programming",
  booktitle =    "Foundations of Genetic Programming",
  year =         "1999",
  editor =       "Thomas Haynes and William B. Langdon and Una-May
                 O'Reilly and Riccardo Poli and Justinian Rosca",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/daida.ps.gz",
  size =         "2 pages",
  notes =        "GECCO'99 WKSHOP, part of haynes:1999:fogp",
}

@InProceedings{daida:1999:CVRMCGPGM,
  author =       "Jason M. Daida and Derrick S. Ampy and Michael
                 Ratanasavetavadhana and Hsiaolei Li and Omar A.
                 Chaudhri",
  title =        "Challenges with Verification, Repeatability, and
                 Meaningful Comparison in Genetic Programming: Gibson's
                 Magic",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1851--1858",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, methodology,
                 pedagogy and philosophy",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{daida:1999:MSWMPGATDPGP,
  author =       "Jason M. Daida and John A. Polito and Steven A.
                 Stanhope and Robert R. Bertram and Jonathan C. Khoo and
                 Shahbaz A. Chaudhary",
  title =        "What Makes a Problem {GP}-Hard? Analysis of a Tunably
                 Difficult Problem in Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "982--989",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{daida:1999:OMDDGPC,
  author =       "Jason M. Daida and Seth P. Yalcin and Paul M. Litvak
                 and Gabriel A. Eickhoff and John A. Polito",
  title =        "Of Metaphors and Darwinism: Deconstructing Genetic
                 Programming's Chimera",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "453--462",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, biomodeling",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/CEC99metaphors.pdf",
  size =         "10 pages",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@Article{daida:2001:GPEM,
  author =       "Jason M. Daida and Robert R. Bertram and Stephen A.
                 Stanhope and Jonathan C. Khoo and Shahbaz A. Chaudhary
                 and Omer A. Chaudhri and John A. {Polito II}",
  title =        "What Makes a Problem {GP}-Hard? Analysis of a Tunably
                 Difficult Problem in Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "2",
  pages =        "165--191",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, problem
                 difficulty, test problems, fitness landscapes, GP
                 theory",
  ISSN =         "1389-2576",
  URL =          "http://ipsapp009.lwwonline.com/content/getfile/4723/5/5/fulltext.pdf",
  abstract =     "This paper addresses the issue of what makes a problem
                 genetic programming (GP)-hard by considering the
                 binomial-3 problem. In the process, we discuss the
                 efficacy of the metaphor of an adaptive fitness
                 landscape to explain what is GP-hard. We indicate that,
                 at least for this problem, the metaphor is
                 misleading.",
}

@InProceedings{daida:2002:lteigplm,
  author =       "Jason M. Daida",
  title =        "Limits to Expression in Genetic Programming:
                 Lattice-Aggregate Modeling",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "273--278",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper describes a general theoretical model of
                 size and shape evolution in genetic programming. The
                 proposed model incorporates a mechanism that is
                 analogous to ballistic accretion in physics. The model
                 indicates a four-region partition of GP search space.
                 It further suggests that two of these regions are not
                 searchable by GP.",
}

@InProceedings{dain:1997:GPmrwfa,
  author =       "Robert A. Dain",
  title =        "Genetic Programming For Mobile Robot Wall-Following
                 Algorithms",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "70--76",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  size =         "pages",
  notes =        "GP-97",
}

@Article{dain:1998:GPmrwfa,
  author =       "Robert A. Dain",
  title =        "Developing Mobile Robot Wall-Folowing Algorithms Using
                 Genetic Programming",
  journal =      "Applied Intelligence",
  year =         "1998",
  volume =       "8",
  number =       "5",
  pages =        "33--41",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, computational
                 genetics, machine learning, adaptive systems",
  ISSN =         "0924-669X",
  notes =        "Special Issues on Evolutionary Learning, Xin Yao and
                 Don Potter, Guest Editors",
}

@TechReport{dallaway:1993:GPcm,
  author =       "Richard Dallaway",
  title =        "Genetic programming and cognitive models",
  institution =  "School of Cognitive \& Computing Sciences, University
                 of Sussex,",
  year =         "1993",
  number =       "CSRP 300",
  address =      "Brighton, UK",
  note =         "In: Brook \& Arvanitis, eds., 1993 The Sixth White
                 House Papers: Graduate Research in the Cognitive \&
                 Computing Sciences at Sussex",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dallaway.demon.co.uk/evolution/evocog.html",
  abstract =     "Genetic programming (GP) is a general purpose method
                 for evolving symbolic computer programs (e.g. Lisp
                 code). Concepts from genetic algorithms are used to
                 evolve a population of initially random programs so
                 that they are able to solve the problem at hand. This
                 paper describes genetic programming and discuss the
                 usefulness of the method for building cognitive models.
                 Although it appears that an arbitrary fit to the
                 training examples will be evolved, it is shown that GP
                 can be constrained to produce small, general
                 programs.",
  notes =        "symbolic regression of 2.719x^2 + 3.14161x from 20
                 random points, parsimony pressure used",
}

@InProceedings{das:GPVR,
  author =       "Sumit Das and Terry Franguidakis and Michael Papka and
                 Thomas A. DeFanti and Daniel J. Sandin",
  title =        "A genetic programming application in virtual reality",
  booktitle =    "Proceedings of the first IEEE Conference on
                 Evolutionary Computation",
  year =         "1994",
  publisher =    "IEEE Press",
  volume =       "1",
  note =         "Part of 1994 IEEE World Congress on Computational
                 Intelligence, Orlando, Florida",
  pages =        "480--484",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  organisation = "IEEE",
  size =         "5 pages",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.mcs.anl.gov/home/papka/PAPERS/IEEE/gpvr.html",
  abstract =     "Genetic programming techniques have been applied to a
                 variety of different problems. In this paper, the
                 authors discuss the use of these techniques in a
                 virtual environment. The use of genetic programming
                 allows the authors a quick method of searching shape
                 and sound spaces. The basic design of the system,
                 problems encountered, and future plans are all
                 discussed.",
  notes =        "Displays 4 simple geometric 3dee items in virtual
                 reality CAVE. User breeds from those he likes.",
}

@InProceedings{dasgupta:1999:AIASR,
  author =       "Dipankar Dasgupta and Yuehua Cao and Congjun Yang",
  title =        "An Immunogenetic Approach to Spectra Recognition",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "149--155",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{daSilva:2000:ewbss,
  author =       "Adelino R. Ferreira {da Silva}",
  title =        "Evolutionary Wavelet Bases in Signal Spaces",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and George D. Smith
                 and David Corne and Martin Oates and Emma Hart and Pier
                 Luca Lanzi and Egbert Jan Willem and Yun Li and Ben
                 Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "44--53",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67353-9",
  notes =        "Evolution of wavlet trees.

                 EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM,
                 EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April
                 17, 2000
                 Proceedings

                 http://evonet.dcs.napier.ac.uk/evoworkshops/

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67353-9",
}

@InProceedings{daSilva:2000:GECCO,
  author =       "Adelino R. Ferreira {da Silva}",
  title =        "Genetic Algorithms for Component Analysis",
  pages =        "243--250",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{dastani:2001:gecco,
  title =        "Finding Perceived Pattern Structures using Genetic
                 Programming",
  author =       "Mehdi Dastani and Elena Marchiori and Robert Voorn",
  pages =        "3--10",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, visual
                 perception, gestalt, simplicity principle, structural
                 information theory (SIT), perceptual regularity",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@Article{dautenhahn:2002:GPEM,
  author =       "Kerstin Dautenhahn",
  title =        "Book Review: Swarm Intelligence",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "1",
  pages =        "93--97",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISSN =         "1389-2576",
  abstract =     "Review of Kennedy+Eberhart's {"}Swarm Intelligence{"}
                 http://www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-595-9
                 James Kennedy and Russell C. Eberhart, with Yuhui Shi,
                 2001, MKP ISBN 1-55860-595-9",
  notes =        "Article ID: 395992",
}

@InProceedings{davenport:1999:RIURPR,
  author =       "G. F. Davenport and M. D. Ryan and V. J.
                 Rayward-Smith",
  title =        "Rule Induction Using a Reverse Polish Representation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "990--995",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Davidge:1993:rr,
  author =       "Robert Davidge",
  title =        "Looping as a Means of Survival: Playing Russian
                 Roulette in a Harsh Environment",
  booktitle =    "ECAL-93 Self organisation and life: from simple rules
                 to global complexity",
  year =         "1993",
  pages =        "259--273",
  address =      "CP 231, Universite Libre de Bruxelles, Bld. du
                 Triomphe, 1050 Brussels, Belgium, Fax 32-2-659.5767
                 Phone 32-2-650.5776 Email sgross@ulb.ac.be",
  month =        "24--26 " # may,
  organisation = "Centre for Non-Linear Phenomena and Complex Systems",
  email =        "robertd@cogs.susx.ac.uk",
  keywords =     "genetic algorithms",
  size =         "15 pages",
  abstract =     "Cline 4bit processor runs across 2dee memory array.
                 Controlled by 16 chromosome of micro-instruction
                 sequences of fixed length.",
  notes =        "There seems to be some doubt as to wether ECAL-93 was
                 published. This copy from attendee.",
}

@InProceedings{davidson:1999:snr:htpa,
  author =       "J. W. Davidson and D. A. Savic and G. A. Walters",
  title =        "Symbolic and numerical regression: a hybrid technique
                 for polynomial approximators",
  booktitle =    "Proceedings of Recent Advances in Soft Computing'99",
  year =         "1999",
  editor =       "Robert John and Ralph Birkenhead",
  pages =        "111--116",
  address =      "De Montfort University, Leicester, UK",
  month =        "1-2 " # jul,
  publisher =    "Physica Verlag",
  keywords =     "genetic algorithms, genetic programming, least
                 squares, polynomial expressions, symbolic algebra,
                 symbolic regression",
}

@Article{davidson:1999:miepfftpf,
  author =       "J. W. Davidson and D. A. Savic and G. A. Walters",
  title =        "Method for the identification of explicit polynomial
                 formulae for the friction in turbulent pipe flow",
  journal =      "Journal of Hydroinformatics",
  year =         "1999",
  volume =       "1",
  number =       "2",
  pages =        "115--126",
  keywords =     "genetic algorithms, genetic programming, least
                 squares, polynomial expressions, symbolic algebra,
                 symbolic regression",
}

@InProceedings{davidson:1999:ac-wfohrm,
  author =       "J. W. Davidson and D. A. Savic and G. A. Walters",
  title =        "Approximators for the Colebrook-White Formula Obtained
                 through a Hybrid Regression Method",
  booktitle =    "Proceedings of XIII International Conference on
                 Computational Methods in Water Resources",
  year =         "2000",
  address =      "Calgary, Canada",
  month =        "25-29 " # jun,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{davidson:2000:rrmunprm,
  author =       "J. W. Davidson and D. A. Savic and G. A. Walters",
  title =        "Rainfall Runoff Modeling Using a New Polynomial
                 Regression Method",
  booktitle =    "Proceedings of the 4th International Conference on
                 Hydroinformatics",
  year =         "2000",
  address =      "Iowa City, Iowa, USA",
  month =        "23-27 " # jul,
  organisation = "Iowa Institute of Hydraulic Research",
  note =         "CD-ROM only",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "none",
  notes =        "Kirkton, Scotland. Mallows Cp to avoid overfitting. GP
                 limited to just polynomials (actually produced by post
                 processing) constants fitted by least-squares.
                 Comparison with previously published GP and ANN.
                 Overfitting (consistency) v. model instability.
                 Population size 40. (l,m) = (100,40) ?",
}

@InProceedings{davidson:2000:snrea,
  author =       "J. W. Davidson and D. A. Savic and G. A. Walters",
  title =        "Symbolic and numerical regression: experiments and
                 applications",
  booktitle =    "Developments in Soft Computing",
  year =         "2001",
  editor =       "Robert John and Ralph Birkenhead",
  pages =        "175--182",
  address =      "De Montfort University, Leicester, UK",
  month =        "29-30 " # jun # " 2000.",
  publisher =    "Physica Verlag",
  keywords =     "genetic algorithms, genetic programming,
                 least-squares, rule-based programming, stepwise
                 regression, symbolic regression",
  ISBN =         "3-7908-1361-3",
  abstract =     "This paper describes a new method for creating
                 polynomial regression models. The new method is
                 compared with stepwise regression and symbolic
                 regression using three example problems. The first
                 example is a polynomial equation. The two examples that
                 follow are real-world problems, approximating the
                 Colebrook-White equation and rainfall-runoff
                 modelling",
}

@InCollection{davis:1994:spec,
  author =       "James Davis",
  title =        "Single Populations v. Co-Evolution",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "20--27",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-182105-2",
  notes =        "Steady state GP model. Tank control strategies
                 co-evolved competeitievely against each other.

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{deakin:1996:GPtaw1,
  author =       "Anthony G. Deakin and Derek F. Yates",
  title =        "Genetic Programming Tools Available on the Web: {A}
                 First Encounter",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "420",
  address =      "Stanford University, CA, USA",
  publisher_address = "Cambridge, MA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.csc.liv.ac.uk/~anthony/gp9632.ps",
  size =         "1 page",
  notes =        "GP-96 10 page version at
                 http://www.csc.liv.ac.uk/~anthony/gp961.ps",
}

@InProceedings{Deakin:1997:esGP,
  author =       "Anthony G. Deakin and Derek F. Yates",
  title =        "Economical Solutions with Genetic Programming: the
                 Non-Hamstrung Squadcar Problem, Fv{M} and {EHP}",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "71--76",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  size =         "pages",
  notes =        "GP-97",
}

@InProceedings{deakin:1997:PTN,
  author =       "Anthony G. Deakin and Derek F. Yates",
  title =        "Phase Transition Networks: {A} Modelling technique
                 supporting the Evolution of Autonomous Agents' Tactical
                 and Operational Activities",
  booktitle =    "Evolutionary Computing",
  year =         "1997",
  editor =       "David Corne and Jonathan L. Shapiro",
  volume =       "1305",
  series =       "Lecture Notes in Computer Science",
  pages =        "263--273",
  address =      "Manchester, UK",
  month =        "11-13 " # apr,
  organisation = "AISB",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, agents,
                 MPHaSys",
  ISBN =         "3-540-63476-2",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-63476-2",
  notes =        "Proceedings of the Workshop on Artificial Intelligence
                 and Simulation of Behaviour (AISB) International
                 Workshop on Evolutionary Computing. Workshop in
                 Manchester, UK, April 7-8, 1997

                 Phase Transfer Networks PTN, egs traffic lights, blood
                 glucose regulation,",
}

@InProceedings{deakin:1998:eoaasGP,
  author =       "Anthony G. Deakin and Derek F. Yates",
  title =        "Evolving and Optimizing Autonomous Agents' Strategies
                 with Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "42--47",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{deaton:1996:gsreDNA,
  author =       "R. Deaton and M. Garzon and R. C. Murphy and J. A.
                 Rose and D. R. Franceschetti and S. E. {Stevens, Jr.}",
  title =        "Genetic Search of Reliable Encodings for {DNA}-Based
                 Computation",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "9--15",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{deaton:1997:ithr,
  author =       "R. Deaton and M. Garzon and R. C. Murphy and D. R.
                 Franceschetti and J. A. Rose and S. E. {Stevens, Jr.}",
  title =        "Information Transfer through Hybridization Reactions
                 in {DNA} based Computing",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "DNA Computing",
  pages =        "463--471",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{deaton:1999:RTCDC,
  author =       "Russell Deaton",
  title =        "Reaction Temperature Constraints in {DNA} Computing",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1803--1804",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "dna and molecular computing",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{deb:1998:otsRGA,
  author =       "Kalyanmoy Deb and Surendra Gulati and Sekhar
                 Chakrabarti",
  title =        "Optimal Truss-Structure Design using Real-Coded
                 Genetic Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "479--486",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{deb:1999:CTPMO,
  author =       "Kalyanmoy Deb",
  title =        "Construction of Test Problems for Multi-Objective
                 Optimization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "164--171",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{deb:1999:SRGASBC,
  author =       "Kalyanmoy Deb and Hans-Georg Beyer",
  title =        "Self-Adaptation in Real-Parameter Genetic Algorithms
                 with Simulated Binary Crossover",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "172--179",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@MastersThesis{decaux:2001:masters,
  author =       "Robert {De Caux}",
  title =        "Using Genetic Programming to Evolve Strategies for the
                 Iterated Prisoner's Dilemma",
  school =       "University College, London",
  year =         "2001",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, java, gpsys,
                 ipd, Coevolution, Pareto scoring, strongly typed",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/decaux.masters.zip",
  size =         "112 pages",
  abstract =     "The technique of Genetic Programming (GP) uses
                 Darwinian principles of natural selection to evolve
                 simple programs with the aim of finding better or
                 fitter solutions to a problem. Based on the technique
                 of Genetic Algorithms (GA), a population of potential
                 solutions stored in tree form are evaluated against a
                 fitness function. The fittest ones are then modified by
                 a genetic operation, and used to form the next
                 generation. This process is repeated until certain
                 criteria have been met. This could be an ultimate
                 solution, or a certain number of generations having
                 been evolved. Genetic Programming is a fast developing
                 field with potential uses in medicine, finance and
                 artificial intelligence. This project attempts to use
                 the technique to evolve strategies for the game of
                 Prisoner's Dilemma. Although a simple game, the range
                 of possible strategies when the game is iterated is
                 vast, but what makes it particularly interesting is the
                 absence of an ultimate strategy and the possibility of
                 mutual benefit by cooperation. A system was created to
                 allow strategies to be evolved by either playing
                 against fixed opponents or against each other
                 (coevolution). The strategies are stored as trees, with
                 GP used to form the next generation. The main advantage
                 of GP over GA is that the trees do not need to be of a
                 fixed size, so strategies can be developed which
                 utilise the entire game history as opposed to just the
                 last few moves. This implementation has advantages over
                 previous investigations, as information about which go
                 is being played can be used, thus allowing cleverer
                 strategies. Work has also been conducted into a hunting
                 phase, where strategies roam a two dimensional grid to
                 find a suitable opponent. By studying the history of
                 potential opponents and using GA, evidence emerged of
                 an increase in cooperative behaviour as strategies
                 sought out suitable opponents, demonstrating parallels
                 with biological models of population dynamics. The
                 system has been developed to allow a user to alter
                 important parameters, select the evolution method and
                 seed the population with pre-defined strategies by
                 means of a graphical user interface.",
  notes =        "Awarded a distinction. Supervised by Robin Hirsch. Zip
                 archive contains msword document",
}

@InProceedings{DeFalco:1997:GPekc,
  author =       "M. Conte and G. Tautteur and I. {De Falco} and A.
                 Della Cioppa and E. Tarantino",
  title =        "Genetic Programming Estimates of Kolmogorov
                 Complexity",
  booktitle =    "Genetic Algorithms: Proceedings of the Seventh
                 International Conference",
  year =         "1997",
  editor =       "Thomas Back",
  pages =        "743--750",
  address =      "Michigan State University, East Lansing, MI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "19-23 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-487-1",
  URL =          "http://www.irsip.na.cnr.it/~hotg/papers/kc.ps",
  size =         "8 pages",
  abstract =     "In this paper the problem of the Kolmogorov complexity
                 related to binary strings is faced. We propose a
                 Genetic Programming approach which consists in evolving
                 a population of Lisp programs looking for the optimal
                 program that generates a given string. This
                 evolutionary approach has permited to overcome the
                 intractable space and time difficulties occurring in
                 methods which perform an approximation of the
                 Kolmogorov complexity function. The experimental
                 results are quite significant and also show interesting
                 computational strategies so proving the effectiveness
                 of the implemented technique.",
  notes =        "ICGA-97",
}

@InProceedings{falco:1999:TSNM,
  author =       "I. {De Falco} and A. Iazzetta and E. Tarantino and A.
                 Della Cioppa and A. Iacuelli",
  title =        "Towards a Simulation of Natural Mutation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "156--163",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{DeFalco:2000:GECCO,
  author =       "I. {De Falco} and A. Iazzetta and E. Tarantino and A.
                 Della Cioppa and G. Trautteur",
  title =        "A Kolmogorov Complexity-based Genetic Programming tool
                 for string compression",
  pages =        "427--434",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@Article{DeFalco:ASC,
  author =       "I. {De Falco} and A. {Della Cioppa} and E. Tarantino",
  title =        "Discovering interesting classification rules with
                 genetic programming",
  journal =      "Applied Soft Computing",
  year =         "2001",
  volume =       "1",
  number =       "4F",
  pages =        "257--269",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 Classification",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-44KWJTS-1/1/8f98e1cb13b739a68dad80864389ca51",
  URL =          "http://www.elsevier.com/gej-ng/10/10/65/45/43/28/article.pdf",
  abstract =     "Data mining deals with the problem of discovering
                 novel and interesting knowledge from large amount of
                 data. This problem is often performed heuristically
                 when the extraction of patterns is difficult using
                 standard query mechanisms or classical statistical
                 methods. In this paper a genetic programming framework,
                 capable of performing an automatic discovery of
                 classification rules easily comprehensible by humans,
                 is presented. A comparison with the results achieved by
                 other techniques on a classical benchmark set is
                 carried out. Furthermore, some of the obtained rules
                 are shown and the most discriminating variables are
                 evidenced.",
}

@InProceedings{deGaris:1992:dcGPssg,
  author =       "Hugo {de Garis} and Hitoshi Iba and Tatsumi Furuya",
  title =        "Differentiable Chromosomes: The Genetic Programming of
                 switchable Shape-Genes",
  booktitle =    "Parallel Problem Solving from Nature 2",
  month =        "28-30 " # sep,
  year =         "1992",
  editor =       "R Manner and B Manderick",
  pages =        "489--498",
  address =      "Brussels, Belgium",
  publisher =    "Elsevier Science",
  keywords =     "genetic algorithms, genetic programming",
  size =         "10 pages",
  notes =        "Wants to build machines with billions of components,
                 proposes these grow themselves in an embryonic fashion.
                 Does some experiments with two stage, hence
                 differentiable, chromosomes which control the states of
                 a cellular automata. Stages are switched on by psuedo
                 chemical gradient. Can grow convex shapes but pretty
                 poor at using GA to evolve concave shapes.

                 PPSN2",
}

@InProceedings{degaris:1993:erGPsrca,
  author =       "Hugo {de Garis}",
  title =        "Evolving a Replicator The Genetic Programming of Self
                 Reproduction in Cellular Automata",
  booktitle =    "ECAL-93 Self organisation and life: from simple rules
                 to global complexity",
  year =         "1993",
  pages =        "274--284",
  address =      "CP 231, Universite Libre de Bruxelles, Bld. du
                 Triomphe, 1050 Brussels, Belgium, Fax 32-2-659.5767
                 Phone 32-2-650.5776 Email sgross@ulb.ac.be",
  month =        "24--26 " # may,
  organisation = "Centre for Non-Linear Phenomena and Complex Systems",
  email =        "degaris@hip.att.co.jp",
  keywords =     "genetic algorithms, genetic programming,
                 nonotechnology, nanots, artificial life,
                 Qantum-electronic computers, Darwin machines",
  size =         "11 pages",
  abstract =     "Presents results from the evolution of cellular
                 automata replicators using GP (ie using GAs to
                 build/evolve systems. 1: How difficult is the evolution
                 of CA replicators (intersity to Artificial Life), 2:
                 Evolving CAs may provide tools for quantum-electronic
                 computers (eg quantum dot arrays)",
  notes =        "There seems to be some doubt as to wether ECAL-93 was
                 published. This copy from attendee.

                 GA chromosome is fixed (1024 * 4 CA state values)
                 encoding the CA state transition rules.

                 {"}Evolving CA replicators is much harder than
                 initially thought{"}

                 Now working on CA networks cf Von Neuman, Codd,
                 Burks.",
}

@InProceedings{deGaris:1994:CAM-BRAIN,
  author =       "Hugo {de Garis}",
  title =        "{CAM}-{BRAIN} The Genetic Programming of an Artificial
                 Brain Which Grows/Evolves at Electronic Speeds in a
                 Cellular Automata Machine",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "337--339b",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, cellular automata, neural
                 networks",
  size =         "6 pages",
  notes =        "It appears growth of cellular automata are controlled
                 by linear fixed length chromosome, ie does not use Koza
                 style tree. The CA grow in channels which convey
                 signals that are isolated from each other except at
                 junctions (synapses). Artificial brain by 2000AD.",
}

@Misc{degaris:1996:alifeV,
  author =       "Hugo {de Garis}",
  title =        "Alife-{V} 1996 Conference Report",
  year =         "1996",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, artificial
                 life",
  URL =          "http://www.hip.atr.co.jp/~degaris/AlifeV.txt",
  size =         "7 pages",
  abstract =     "Personal account of the 5th World Artificial Life
                 Conference, 16-18 May 1996, Nara, Japan",
}

@InProceedings{garis:1999:AABPASWIMENNMCDDAI,
  author =       "H. {de Garis} and A. Buller and M. Korkin and F. Gers
                 and N. E. Nawa and M. Hough",
  title =        "{ATR}'s Artificial Brain (``{CAM}-Brain'') Project:
                 {A} Sample of What Individual ``CoDi-1Bit'' Model
                 Evolved Neural Net Modules Can Do with Digital and
                 Analog {I}/{O}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1233",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{degaris:2002:gecco:lbp,
  title =        "A Reversible Evolvable Network Architecture and
                 Methodology to Overcome the Heat Generation Problem in
                 Molecular Scale Brain Building",
  author =       "Hugo {de Garis} and Jonathan Dinerstein and
                 Ravichandra Sriram",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "83--90",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

@InProceedings{eddejong:1999:gssc,
  author =       "Edwin D. {de Jong} and Luc Steels",
  title =        "Generation and Selection of Sensory Channels",
  booktitle =    "Evolutionary Image Analysis, Signal Processing and
                 Telecommunications: First European Workshop, EvoIASP'99
                 and EuroEcTel'99",
  year =         "1999",
  editor =       "Riccardo Poli and Hans-Michael Voigt and Stefano
                 Cagnoni and Dave Corne and George D. Smith and Terence
                 C. Fogarty",
  volume =       "1596",
  series =       "LNCS",
  pages =        "90--100",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "28-29 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65837-8",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-65837-8",
  abstract =     "Sensory channels determine the way an agent views the
                 world. We investigate the question of how sensory
                 channels may be autonomously constructed using
                 generation and selection. The context is the
                 discrimination of geometric shapes. In a first
                 experiment, elements of a solution were attributed
                 fitness based on the part of the problem they solved.
                 In two subsequent experiments, cooperation between
                 elements was respectively required and encouraged by
                 means of a fitness function which only rewards complete
                 solutions. Differences between the approaches are
                 discussed, and generation and selection is concluded to
                 provide a successful mechanism for the autonomous
                 construction of sensory channels.",
  notes =        "EvoIASP99'99",
}

@InProceedings{icga87:deJong,
  author =       "Kenneth {De Jong}",
  title =        "On Using Genetic Algorithms to Search Program Spaces",
  booktitle =    "Genetic Algorithms and their Applications: Proceedings
                 of the second international conference on Genetic
                 Algorithms",
  year =         "1987",
  editor =       "John J. Grefenstette",
  pages =        "210--216",
  month =        "28-31 " # jul,
  organisation = "AAAI",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "Hillsdale, NJ, USA",
  publisher =    "Lawrence Erlbaum Associates",
  keywords =     "genetic algorithms, genetic programming",
  size =         "7 pages",
  ISBN =         "0-8058-0158-8",
  notes =        "Agues against using LISP (but reference to LISP in
                 ICGA-87) as too order depenant and fragile. Suggests
                 instead production languages as in Holland and others
                 classifiers. Warns new representations and crossover
                 operators must obey schema theorrem, so crossover is
                 not disruptive and building blocks can be formed",
}

@InProceedings{delgado:1999:MHEDFS,
  author =       "Myriam Delgado and Fernando Von Zuben and Fernando
                 Gomide",
  title =        "Modular and Hierarchial Evolutionary Design of Fuzzy
                 Systems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "180--187",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{delgado:2002:FUZZIEEE,
  author =       "Myriam Regattieri Delgado and Fernando {Von Zuben} and
                 Fernando Gomide",
  title =        "Multi-Objective Decision Making: Towards Improvement
                 of Accuracy, Interpretability and Design Autonomy in
                 Hierarchical Genetic Fuzzy Systems",
  booktitle =    "Proceedings of the 2002 IEEE International Conference
                 on Fuzzy Systems, FUZZ-IEEE-02",
  pages =        "1222--1227",
  year =         "2002",
  month =        "12-17 " # may,
  address =      "Hilton Hawaiian Village Hotel, Honolulu, Hawaii",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE",
  ISBN =         "0-7803-7280-8",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper presents fuzzy modeling as a
                 multi-objective decision making problem considering
                 accuracy, interpretability and autonomy as goals. The
                 proposed approach assumes that these goals can be
                 handled via corresponding single-objective
                 e-constrained decision making problems whose solution
                 is produced by a hierarchical evolutionary process. The
                 fitting, generalization, and interpretation
                 characteristics of the resulting fuzzy models are
                 discussed using a classification problem.",
  notes =        "IJCNN 2002 Held in connection with the World Congress
                 on Computational Intelligence (WCCI 2002)

                 The length of the chromosome, fixed by the constraint
                 e2, determines the maximum number of fuzzy rules but
                 smaller rule-bases are always aimed at first.",
}

@InCollection{dembo:2002:EMSGA,
  author =       "Adar Dembo",
  title =        "Evolving Musical Scores using the Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "65--72",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@Article{Dempster:2000:QF,
  author =       "M. A. H. Dempster and C. M. Jones",
  title =        "A real-time adaptive trading system using genetic
                 programming",
  journal =      "Quantitative Finance",
  year =         "2000",
  volume =       "1",
  pages =        "397--413",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cfr.jims.cam.ac.uk/archive/PAPERS/2000/geneticprogramming.pdf",
  size =         "17 pages",
  abstract =     "Technical analysis indicators are widely used by
                 traders in financial and commodity markets to predict
                 future price levels and enhance trading profitability.
                 We have previously shown a number of popular
                 indicator-based trading rules to be loss-making when
                 applied individually in a systematic manner. However,
                 technical traders typically use combinations of a broad
                 range of technical indicators. Moreover, successful
                 traders tend to adapt to market conditions by dropping
                 trading rules as soon as they become loss-making or
                 when more profitable rules are found. In this paper we
                 try to emulate such traders by developing a trading
                 system consisting of rules based on combinations of
                 different indicators at different frequencies and lags.
                 An initial portfolio of such rules is selected by a
                 genetic algorithm applied to a number of indicators
                 calculated on a set of US Dollar/British Pound spot
                 foreign exchange tick data from 1994 to 1997 aggregated
                 to various intraday frequencies. The genetic algorithm
                 is subsequently used at regular intervals on
                 out-of-sample data to provide new rules and a feedback
                 system is used to rebalance the rule portfolio, thus
                 creating two levels of adaptivity. Despite the
                 individual indicators being generally loss-making over
                 the data period, the best rule found by the developed
                 system is found to be modestly, but significantly,
                 profitable in the presence of realistic transaction
                 costs.",
  notes =        "INSTITUTE OF PHYSICS PUBLISHING quant.iop.org",
}

@InProceedings{derrig:1998:hecagcs,
  author =       "Daniel Derrig and James D. Johannes",
  title =        "Hierarchical Exemplar Based Credit Allocation for
                 Genetic Classifier Systems",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "622--628",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{derrig:1998:deosc,
  author =       "Daniel Derrig and James Johannes",
  title =        "Deleting End-of-Sequence Classifiers",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{deschain:2000:ASTC,
  author =       "Larry M. Deschain and Fred A. Zafran and Janardan J.
                 Patel and David Amick and Robert Pettit and Frank D.
                 Francone and Peter Nordin and Edward Dilkes and Laurene
                 V. Fausett",
  title =        "Solving the Unsolved Using Machine Learning, Data
                 Mining and Knowledge Discovery to Model a Complex
                 Production Process",
  booktitle =    "Advanced Technology Simulation Conference",
  year =         "2000",
  editor =       "M. Ades",
  address =      "Wasington, DC, USA",
  organisation = "Society for Computer Simulations",
  month =        "22-26 " # apr,
  keywords =     "genetic algorithms, genetic programming, discipulus",
  URL =          "http://pw2.netcom.com/%7elmdmit84/SoilStabilization2000.pdf",
  size =         "6 pages",
  notes =        "http://www.scs.org/confernc/astc00/final-program/BIS-details.htm

                 Soil Stabilization via Evolutionary Computation -
                 Linear Genetic Programming, Simulated Annealing, and
                 ANN. Predict hydraulic condictivity, strangth, leach,
                 of stabilised waste given grain size and grout
                 composition. {"}The speed savings alone made GP the
                 technology of Choice.{"}",
}

@InProceedings{Deschain:2001:ASTC,
  author =       "Larry M. Deschain and Janardan J. Patel and Ronald D.
                 Guthrie and Joseph T. Grimski and M. J. Ades",
  title =        "Using Linear Genetic Programming to Develop a {C/C++}
                 Simulation Model of a Waste Incinerator",
  booktitle =    "Advanced Technology Simulation Conference",
  year =         "2001",
  editor =       "M. Ades",
  address =      "Seattle",
  month =        "22-26 " # apr,
  organisation = "Society for Computer Simulations",
  keywords =     "genetic algorithms, genetic programming, discipulus,
                 DSS, 10 demes",
  URL =          "http://pw2.netcom.com/~lmdmit84/ASTC2001-LGP-INCINERATOR.pdf",
  notes =        "ASTC 2001
                 http://www.scs.org/confernc/astc01/prelim-program/astc01prelim.html
                 Science Applications International Corporation

                 Model of C02 concentration from 1 weeks live running
                 hourly logs. Interactive Evaluation (Unclear what this
                 means). Print out of PDF poor",
}

@Article{deschain:2000:PCAI,
  author =       "Larry M. Deschain",
  title =        "Tackling Real-World Environmental Challenges with
                 Linear Genetic Programming",
  journal =      "PCAI",
  year =         "2000",
  volume =       "15",
  number =       "5",
  pages =        "35--37",
  month =        sep # "/" # oct,
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  notes =        "advocates a unique approach to the challenges of
                 engineering and scientific data mining, control, and
                 process optimization by using fast linear genetic
                 programming technique.",
}

@InProceedings{deschaine:2002:FEA,
  author =       "L. M. Deschaine and F. D. Francone",
  title =        "Extending the Boundaries of Design Optimization by
                 Integrating Fast Optimization Techniques with
                 Machine-Code-Based, Linear Genetic Programming",
  booktitle =    "The Fourth International Workshop on Frontiers in
                 Evolutionary Algorithms (FEA 2002)",
  year =         "2002",
  editor =       "Manuel Grana Romay and Richard Duro",
  address =      "Research Triangle Park, North Carolina, USA",
  month =        mar # " 8-13",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-9707890-1-7",
  notes =        "FEA2002 In conjunction with Sixth Joint Conference on
                 Information Sciences

                 My printer refuses to deal with this as PDF",
}

@InCollection{deshpande:2002:CJSGASBS,
  author =       "Nishant Deshpande",
  title =        "Comparison of a Job-Shop Scheduler using Genetic
                 Algorithms with a {SLACK} Based Scheduler",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "73--82",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{desjarlais:1999:CSOUGAST,
  author =       "Lisa M. Desjarlais and Mohammad-R. Akbarzadeh-T. and
                 Craig W. Wright",
  title =        "Control System Optimization Using Genetic Algorithms
                 within the SoftLab Toolkit",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1774",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{dessi:1999:AAASDGP,
  author =       "Antonello Dessi and Antonella Giani and Antonia
                 Starita",
  title =        "An Analysis of Automatic Subroutine Discovery in
                 Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "996--1001",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{devaney:1995:mpimake,
  author =       "Judith E. Devaney",
  title =        "Converting pvmmake to mpimake under {LAM}, and {MPI}
                 and Parallel Genetic Programming",
  booktitle =    "MPI Developers Conference",
  year =         "1995",
  editor =       "Andrew Lumsdaine",
  address =      "University of Notre Dame",
  month =        "22-23 " # jun,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cse.nd.edu/mpidc95/proceedings/papers/postscript/devaney.ps",
  abstract =     "This looks at the issues which arose in porting the
                 pvmmake utility from pvm to mpi. Pvmmake is a pvm
                 application which allows a user to send files, execute
                 commands, and receive results from a single machine on
                 any machine in the virtual machine. It's actions are
                 controlled by the contents of an agenda file. It's most
                 common use is to enable management of the development
                 of a parallel program in a heterogeneous environment. A
                 utility with the same features, mpimake, was coded up
                 to run under LAM.

                 Genetic programming is an algorithm which evolves a
                 program to solve your input problem. The implementation
                 under MPI requires the transfer of data structures such
                 as lists and trees. The match between the requirements
                 of this algorithm and the datatype feature in mpi will
                 be discussed.",
  notes =        "Data from
                 http://www.cse.nd.edu/mpidc95/proceedings/abstracts/html/devaney/
                 4 Nov 1997",
}

@InProceedings{devaney:2001:gpe,
  author =       "Judith Devaney and John Hagedorn and Olivier Nicolas
                 and Gagan Garg and Aurelien Samson and Martial Michel",
  title =        "A Genetic Programming Ecosystem",
  booktitle =    "Proceedings 15th International Parallel and
                 Distributed Processing Symposium, Abstracts and CDROM",
  year =         "2001",
  pages =        "1323--1330",
  xaddress =     "Los Alamitos, CA, USA",
  howpublished = "Abstracts and CD-ROM",
  month =        "23-27 " # apr,
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-0990-8",
  URL =          "http://math.nist.gov/mcsd/savg/papers/bio.pp.gz",
  note =         "IPDPS2001:WS",
}

@InProceedings{devaney:2002:gecco:lbp,
  title =        "The Role of Genetic Programming in Describing the
                 Microscopic Structure of Hydrating Plaster",
  author =       "Judith E. Devaney and John G. Hagedorn",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "91--98",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 pruning anti-bloat, fitness based on correlation. Maple
                 algebraic simplification. sensitivity, true-positives.
                 C5. {"}clear and concise decision algorithm that
                 accurately predicts{"} p96",
}

@InCollection{kinnear:DHaeseleer,
  title =        "Effects of Locality in Individual and Population
                 Evolution",
  author =       "Patrik D'haeseleer and Jason Bluming",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "8",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "177--198",
  size =         "22 pages",
}

@InProceedings{Dhaeseleer:1994:cpcGP,
  author =       "Patrik D'haeseleer",
  title =        "Context preserving crossover in genetic programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "256--261",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/WCCI94_CPC.ps.Z",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  abstract =     "Two new crossover operators for GP (Strong Context
                 preserving (SCPC) and Weak context preserving(WCPC)).
                 These attempt to preserve the context of swapped
                 subtrees. SCPC best used 50% with koza crossover. 100%
                 WCPC not performing as well.",
  notes =        "

                 ",
}

@InCollection{Dharma:1997:amctsa,
  author =       "Prisdha Dharma",
  title =        "Automatic Model Construction for Time Series Analysis
                 via Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "28--35",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InCollection{dhingra:2002:ESIDAO,
  author =       "Philip Dhingra",
  title =        "Evolution of Simple Intelligence Distribution in
                 Artificial Organisms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "83--92",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp GP to evolve group of ants to
                 push a box from the centre of a room to a wall. Trade
                 off between {"}intelligence{"} of individual ants and
                 number of ants in the group. LISP",
}

@InCollection{dickinson:1994:d-i,
  author =       "Andrew Dickinson",
  title =        "Evolution of Damage-Immune Programs using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "21--30",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InCollection{dickson:1999:EOGASS,
  author =       "Andrew Dickson",
  title =        "Evolution of Optimum Genetic Algorithms for Specific
                 Spaces",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "41--48",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{digby:1999:EAABGC,
  author =       "David Digby and William Seffens",
  title =        "Evolutionary Algorithm Analysis of the Biological
                 Genetic Codes",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1440",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{dijk:1999:OTDGAGA,
  author =       "S. van Dijk and D. Thierens and M. de Berg",
  title =        "On The Design of Genetic Algorithms for Geographical
                 Applications",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "188--195",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{dill:1997:grmGA,
  author =       "Karen M. Dill and Marek A. Perkowski",
  title =        "Minimization of {GRM} Forms with a Genetic Algorithm",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "362",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{Dill:1997:PACRIM,
  author =       "Karen M. Dill and James H. Herzog and Marek A.
                 Perkowski",
  title =        "Genetic programming and its applications to the
                 synthesis of digital logic",
  booktitle =    "Communications, Computers and Signal Processing,
                 PACRIM 1997",
  year =         "1997",
  volume =       "2",
  pages =        "823--826",
  address =      "Victoria, BC, Canada",
  month =        "20-22 " # aug,
  keywords =     "genetic algorithms, genetic programming, logic
                 circuits, logic CAD, digital logic synthesis, arbitrary
                 logic expressions, logic synthesis, problem
                 applicability, optimization criterion, logic gates,
                 population sizes, complete function coverage,
                 experimental test results, randomly designed functions,
                 input variables, logic equations, function coverage,
                 training set size, small training sets, function
                 recognition",
  ISBN =         "0-7803-3905-3",
  abstract =     "Genetic programming is applied to the synthesis of
                 arbitrary logic expressions. As a new method of logic
                 synthesis, this technique is uniquely advantageous in
                 its flexibility for both problem applicability and
                 optimization criterion. A number of experiments were
                 conducted exploring this method with different types of
                 logic gates and population sizes. While complete
                 function coverage is not guaranteed, the best
                 experimental test results over eight randomly designed
                 functions, of four to seven input variables, have
                 produced logic equations with a 98.4% function
                 coverage. In addition, the relation between the
                 training set size for the genetic program and function
                 coverage was also empirically explored. These
                 experiments showed that only small training sets were
                 necessary for function recognition.",
}

@InCollection{dillon:1995:EGASSSTP,
  author =       "Thomas Dillon",
  title =        "Evolution of General Algorithmic Solutions for Simple
                 Sliding Tile Puzzles",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "65--75",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{dimopoulos:1999:ESPGPF,
  author =       "Christos Dimopoulos and Ali M. S. Zalzala",
  title =        "Evolving Scheduling Policies through a Genetic
                 Programming Framework",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1231",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{dimopoulos:1999:AGPHOTTP,
  author =       "Christos Dimopoulos and Ali M. S. Zalzala",
  title =        "A Genetic Programming Heuristic for the One-Machine
                 Total Tardiness Problem",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "3",
  pages =        "2207--2214",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, manufacturing
                 optimization",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@Article{Dimopoulos:2001:AES,
  author =       "C. Dimopoulos and A. M. S. Zalzala",
  title =        "Investigating the use of genetic programming for a
                 classic one-machine scheduling problem",
  journal =      "Advances in Engineering Software",
  volume =       "32",
  pages =        "489--498",
  year =         "2001",
  month =        "6",
  email =        "cop97cd@sheffield.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V1P-42YFC02-7/1/6be8f2e3206dccb17801b7a7833a6299",
  abstract =     "Genetic programming has rarely been applied to
                 manufacturing optimisation problems. In this paper the
                 potential use of genetic programming for the solution
                 of the one-machine total tardiness problem is
                 investigated. Genetic programming is utilised for the
                 evolution of scheduling policies in the form of
                 dispatching rules. These rules are trained to cope with
                 different levels of tardiness and tightness of due
                 dates.",
}

@PhdThesis{diplock:thesis,
  author =       "Gary Diplock",
  title =        "The application of evolutionary computing techniques
                 to spatial interaction modelling",
  school =       "Leeds University, UK",
  year =         "1996",
  month =        Sep,
  email =        "garyd@gmap.leeds.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://gam.leeds.ac.uk/pub/gary/thesis/thesis.zip",
  size =         "pages",
  notes =        "

                 The research involved using both GAs and GP to build
                 new forms of spatial models which predict the flows of
                 products and services, population, etc between spatial
                 areas. GAs were also used to calibrate existing spatial
                 interaction models. The GP was implemented on the
                 512-processor T3D facility in Edinburgh (Scotland)
                 using a MPI shell

                 {"}Please note that{"}
                 ftp://gam.leeds.ac.uk/pub/gary/thesis/thesis.zip (word
                 for windows) is {"}a draft version which has a few
                 typing errors, etc. but this should not be a problem{"}
                 5-0ct-1997

                 ",
}

@InProceedings{dittrich:1998:lmrrm,
  author =       "Peter Dittrich and Andreas Burgel and Wolfgang
                 Banzhaf",
  title =        "Learning to Move a Robot with Random Morphology",
  booktitle =    "Proceedings of the First European Workshop on
                 Evolutionary Robotics",
  year =         "1998",
  editor =       "Phil Husbands and Jean-Arcady Meyer",
  volume =       "1468",
  series =       "LNCS",
  pages =        "165--178",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "16-17 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64957-3",
  notes =        "EvoRobot'98 See also dittrich:1998:rmr",
}

@Article{dittrich:1998:rmr,
  author =       "Peter Dittrich and Andreas Burgel and Wolfgang
                 Banzhaf",
  title =        "Random Morphology Robot - {A} Test Platform for Online
                 Evolution",
  journal =      "Robots and Autonomous Systems",
  year =         "1998",
  note =         "To appear",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "See also dittrich:1998:lmrrm",
}

@InProceedings{dittrich:1999:DPFLGCRMR,
  author =       "Peter Dittrich and Andre Skusa and Wolfgang Kantschik
                 and Wolfgang Banzhaf",
  title =        "Dynamical Properties of the Fitness Landscape of a
                 {GP} Controlled Random Morphology Robot",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1002--1008",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{divina:2001:gecco,
  title =        "Knowledge Based Evolutionary Programming for Inductive
                 Learning in First-Order Logic",
  author =       "Federico Divina and Elena Marchiori",
  pages =        "173",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{dolado:1998:GPNNlrspe,
  author =       "J. J. Dolado and L. Fernández",
  title =        "Genetic Programming, Neural Networks and Linear
                 Regression in Software Project Estimation",
  booktitle =    "International Conference on Software Process
                 Improvement, Research, Education and Training",
  year =         "1998",
  editor =       "C. Hawkins and M. Ross and G. Staples and J. B.
                 Thompson",
  pages =        "157--171",
  address =      "London",
  month =        "10-11 " # sep,
  publisher =    "British Computer Society",
  keywords =     "genetic algorithms, genetic programming, neural
                 networks, linear regression",
  ISBN =         "1 902505 03 4",
  URL =          "http://www.sc.ehu.es/jiwdocoj/docs/inspir98.pdf",
  size =         "1 Mb",
  notes =        "INSPIRE 98
                 http://www2.unl.ac.uk/~11georgiadou/inspire98/",
}

@InProceedings{dolado:1999:lmsce,
  author =       "J. Javier Dolado",
  title =        "Limits to the Methods in Software Cost Estimation",
  booktitle =    "Proceedings of the 1st International Workshop on Soft
                 Computing Applied to Software Engineering",
  year =         "1999",
  editor =       "Conor Ryan and Jim Buckley",
  pages =        "63--68",
  address =      "University of Limerick, Ireland",
  month =        "12-14 " # apr,
  organisation = "SCARE",
  publisher =    "Limerick University Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-874653-52-6",
  URL =          "http://www.sc.ehu.es/jiwdocoj/docs/dolado-scase99.ps",
  size =         "330 Kb",
  abstract =     "We present some conclusions related to the use of
                 classical regression, neural networks (NN) and genetic
                 programming (GP) for software cost estimation. Although
                 the estimates of classical regression can be improved
                 by NN and GP, the results are not impressive. We
                 conclude that either data points limit the usefulness
                 of the methods, or that better ways have to be found
                 for applying soft-computing techniques for software
                 cost estimation.",
  notes =        "http://scare.csis.ul.ie/scase99/ SCASE'99",
}

@Article{Dolado:2000:vcmsse,
  author =       "Jose Javier Dolado",
  title =        "A validation of the component-based method for
                 software size estimation",
  journal =      "IEEE Transactions on Software Engineering",
  year =         "2000",
  volume =       "26",
  number =       "10",
  pages =        "1006--1021",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, software
                 reusability, software component-based method, software
                 size estimation, software management, work planning,
                 lines of code, fourth-generation language, Mark II
                 function points, software size prediction, neural
                 networks,",
  ISSN =         "0098-5589",
  URL =          "http://ieeexplore.ieee.org/iel5/32/19037/00879821.pdf",
  size =         "16 pages",
  abstract =     "Estimation of software size is a crucial activity
                 among the tasks of software management. Work planning
                 and subsequent estimations of the effort required are
                 made based on the estimate of the size of the software
                 product. Software size can be measured in several ways:
                 lines of code (LOC) is a common measure and is usually
                 one of the independent variables in equations for
                 estimating several methods for estimating the final LOC
                 count of a software system in the early stages. We
                 report the results of the validation of the
                 component-based method (initially proposed by Verner
                 and Tate, 1988) for software sizing. This was done
                 through the analysis of 46 projects involving more than
                 100,000 LOC of a fourth-generation language. We present
                 several conclusions concerning the predictive
                 capabilities of the method. We observed that the
                 component-based method behaves reasonably, although not
                 as well as expected for {"}global{"} methods such as
                 Mark II function points for software size prediction.
                 The main factor observed that affects the performance
                 is the type of component.",
}

@Article{Dolado:2001:SCF,
  author =       "Jose J. Dolado",
  title =        "On the Problem of the Software Cost Function",
  journal =      "Information and Software Technology",
  year =         "2001",
  volume =       "43",
  number =       "1",
  pages =        "61--72",
  month =        "1 " # jan,
  keywords =     "genetic algorithms, genetic programming, software cost
                 function, Cost estimation, Empirical research",
  URL =          "http://www.elsevier.com/locate/issn/09505849",
  size =         "12 pages",
  abstract =     "The question of finding a function for software cost
                 estimation is a long-standing issue in the software
                 engineering field. The results of other works have
                 shown different patterns for the unknown function,
                 which relates software size to project cost (effort).
                 In this work, the research about this problem has been
                 made by using the technique of Genetic Programming (GP)
                 for exploring the possible cost functions. Both
                 standard regression analysis and GP have been applied
                 and compared on several data sets. However, regardless
                 of the method, the basic size-effort relationship does
                 not show satisfactory results, from the predictive
                 point of view, across all data sets. One of the results
                 of this work is that we have not found significant
                 deviations from the linear model in the software cost
                 functions. This result comes from the marginal cost
                 analysis of the equations with best predictive
                 values.",
}

@InCollection{dolin:2000:CPCESIITDLC,
  author =       "Brad Dolin",
  title =        "Co-Evolution of Populations of Chasers and Evaders
                 that use Sonic Intensity and Interaural Time Difference
                 as Localization Cues",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "117--124",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{dolin:2001:eh,
  author =       "Brad Dolin and Forrest H {Bennett III} and Eleanor G.
                 Rieffel",
  title =        "Methods for evolving robust distributed robot control
                 software: coevolutionary and single population
                 techniques",
  booktitle =    "The Third NASA/DoD workshop on Evolvable Hardware",
  year =         "2001",
  editor =       "Didier Keymeulen and Adrian Stoica and Jason Lohn and
                 Ricardo S. Zebulum",
  pages =        "21--29",
  address =      "Long Beach, California",
  publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC,
                 20036-1992, USA",
  month =        "12-14 " # jul,
  organisation = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-1180-5",
  notes =        "EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/
                 Note misspeling of Brad Dolin as {"}Dofin, B.{"}.",
}

@Article{dolin:2002:GPEM,
  author =       "Brad Dolin and J. J. Merelo",
  title =        "Resource Review: {A} Web-Based Tour of Genetic
                 Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "3",
  pages =        "311--313",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1389-2576",
  abstract =     "Summary of some introductions to GP, tutorials and
                 demos, implementations and useful links for GP
                 research",
  notes =        "Article ID: 5091793",
}

@InProceedings{dolin:ppsn2002:pp142,
  author =       "Brad Dolin and M. G. Arenas and J. J. Merelo",
  title =        "Opposites Attract: Complementary Phenotype Selection
                 for Crossover in Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "142 ff.",
  year =         "2002",
  editor =       "J.-J. Merelo Guerv\'os and P. Adamidis and H.-G. Beyer
                 and J.-L. Fern\'andez-Villaca\~nas and H.-P. Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithsm, genetic programming",
  note =         "Keywords: Technique::Advanced techniques -
                 miscellaneous, Technique::Evolutionary computing -
                 general, Technique::Evolutionary computing -
                 miscellaneous, Technique::Genetic programming -
                 general, Technique::Selection",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900142.pdf",
}

@PhdThesis{domingos:thesis,
  author =       "Roberto Pinheiro Domingos",
  title =        "Non-Linear Nuclear Engineering Models as an
                 Application of Genetic Programming",
  school =       "Universidade Federal Rio de Janeiro",
  year =         "1997",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  notes =        "details from
                 http://www.genetic-programming.org/gpphdtheses.html",
}

@InCollection{donald:1995:AEACFI,
  author =       "Keith Mac Donald",
  title =        "An Evolutionary Approach to {CPU} Fault Isolation",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "199--208",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{Dorado:2002:EvoWorkshops,
  author =       "Julian Dorado and Juan R. Rabu$\tilde{n}$al and
                 Jer\'onimo Puertas and Antonino Santos and Daniel
                 Rivero",
  title =        "Prediction and Modelling of the Flow of a Typical
                 Urban Basin through Genetic Programming",
  booktitle =    "Applications of Evolutionary Computing, Proceedings of
                 EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN",
  year =         "2002",
  editor =       "Stefano Cagnoni and Jens Gottlieb and Emma Hart and
                 Martin Middendorf and G{"}unther Raidl",
  volume =       "2279",
  series =       "LNCS",
  pages =        "190--201",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-4 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, applications, hydrology, rain-fall
                 run-off, sewage, flooding alarm, transference function,
                 hydraulic enginnering, kinematic wave, unit hydographs,
                 STGP",
  ISBN =         "3-540-43432-1",
  size =         "12 pages",
  abstract =     "Genetic Programming (GP) is an evolutionary method
                 that creates computer programs that represent
                 approximate or exact solutions to a problem. This paper
                 proposes an application of GP in hydrology, namely for
                 modelling the effect of rain on the run-off flow in a
                 typical urban basin. The ultimate goal of this research
                 is to design a real time alarm system to warn of floods
                 or subsidence in various types of urban basin. Results
                 look promising and appear to offer some improvement
                 over stochastic methods for analysing river basin
                 systems such as unitary radiographs.",
  notes =        "EvoWorkshops2002, part of cagnoni:2002:ews

                 Vitoria, Spain, 5 minute pluviometer samples = 288
                 samples per day. Data for rainless days??? Replicated
                 -288...575 three cycles {"}to avoid this
                 discontinuity{"} p193. Sine and Cosine but no IF? No
                 details of mutation, no fine constant adjustment, no
                 anti-bloat measures?

                 Fitting average day and rainy day are
                 separated.

                 Complex arithmetic, mutlti-typed system. {"}This
                 execution does not return any value, it only stores the
                 system's poles, zeros and constants{"} p197. Poles
                 outside unit circle lead to immediate death of tree.
                 Tested on 20 hours and 45 minutes of variable rainfall.
                 Average error on GP model less than that of {"}SCS Unit
                 Hydrograph{"}, Table 1.",
}

@InProceedings{dorado:2002:IJCNN,
  author =       "Julian Dorado and Juan R. Rabunal and Daniel Rivero
                 and Antonino Santos and Alejandro Pazos",
  title =        "Automatic Recurrent {ANN} Rule Extraction with Genetic
                 Programming",
  booktitle =    "Proceedings of the 2002 International Joint Conference
                 on Neural Networks IJCNN'02",
  pages =        "1552--1557",
  year =         "2002",
  month =        "12-17 " # may,
  address =      "Hilton Hawaiian Village Hotel, Honolulu, Hawaii",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE",
  ISBN =         "0-7803-7278-6",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Various rule-extraction techniques using ANNs have
                 been used so far, most of them being applied on
                 multi-layer ANNs, since they are more easily handled.
                 In many cases, extraction methods focusing on different
                 types of networks and training have been implemented,
                 however, there are virtually no methods that view the
                 extraction of rules from ANNs as systems which are
                 independent from their architecture, training and
                 internal distribution of weights, connections and
                 activation functions. This paper proposes a
                 rule-extraction system of ANNs regardless of their
                 architecture (multi-layer or recurrent), using Genetic
                 programming as a rule-exploration technique.",
  notes =        "IJCNN 2002 Held in connection with the World Congress
                 on Computational Intelligence (WCCI 2002)",
}

@InProceedings{dorado:ppsn2002:pp485,
  author =       "Julian Dorado and Juan R. Rabu\~nal and Antonino
                 Santos and Alejandro Pazos and Daniel Rivero",
  title =        "Automatic Recurrent {ANN} Rule Extraction with Genetic
                 Programming",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "485 ff.",
  year =         "2002",
  editor =       "J.-J. Merelo Guerv\'os and P. Adamidis and H.-G. Beyer
                 and J.-L. Fern\'andez-Villaca\~nas and H.-P. Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Keywords: Related::Neural Networks, Technique::Genetic
                 programming - general",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900485.pdf",
}

@Book{dorigo.97,
  author =       "Marco Dorigo and Marco Colombetti",
  title =        "Robot Shaping: An Experiment in Behavior Engineering",
  publisher =    "MIT Press/Bradford Books",
  year =         "1997",
  notes =        "

                 ",
}

@TechReport{dorin:1994:GPr,
  author =       "Alan Dorin",
  title =        "Koza, {J}. {"}Genetic Programming{"} (review)",
  institution =  "School of Computer Science and Software Engineering,
                 Monash University",
  address =      "Clayton, Australia 3168",
  year =         "1994",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.monash.edu.au/~aland/reviews/koza.rev.html",
  notes =        "www only",
  size =         "0.5 pages",
}

@Article{dosi:1999:nepal:er,
  author =       "Giovanni Dosi and Luigi Marengo and Andrea Bassanini
                 and Marco Valente",
  title =        "Norms as emergent properties of adaptive learning: The
                 case of economic routines",
  journal =      "Journal of Evolutionary Economics",
  year =         "1999",
  volume =       "9",
  number =       "1",
  pages =        "5--26",
  keywords =     "genetic algorithms, genetic programming,
                 computability, oligopoly",
  ISSN =         "0936-9937",
}

@InProceedings{downing:1998:GPGAes,
  author =       "Keith Downing",
  title =        "Combining Genetic Programming and Genetic Algorithms
                 for Ecological Simulation",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "48--53",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{downing:2001:gecco,
  title =        "Adaptive Genetic Programs via Reinforcement Learning",
  author =       "Keith L. Downing",
  pages =        "19--26",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, Reinforcement
                 Learning, Baldwin Effect, Lamarckianism, Hybrid
                 Adaptive Systems",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@Article{downing:2001:GPEM,
  author =       "Keith L. Downing",
  title =        "Reinforced Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "3",
  pages =        "259--288",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, reinforcement
                 learning, the Baldwin Effect, Lamarckism",
  ISSN =         "1389-2576",
  abstract =     "This paper introduces the Reinforced Genetic
                 Programming (RGP) system, which enhances standard
                 tree-based genetic programming (GP) with reinforcement
                 learning (RL). RGP adds a new element to the GP
                 function set: monitored action-selection points that
                 provide hooks to a reinforcement-learning system. Using
                 strong typing, RGP can restrict these choice points to
                 leaf nodes, thereby turning GP trees into
                 classify-and-act procedures. Then, environmental
                 reinforcements channeled back through the choice points
                 provide the basis for both lifetime learning and
                 general GP fitness assessment. This paves the way for
                 evolutionary acceleration via both Baldwinian and
                 Lamarckian mechanisms. In addition, the hybrid hints of
                 potential improvements to RL by exploiting evolution to
                 design proper abstraction spaces, via the problem-state
                 classifications of the internal tree nodes. This paper
                 details the basic mechanisms of RGP and demonstrates
                 its application on a series of static and dynamic
                 maze-search problems.",
}

@InProceedings{dracopoulos:1996:sGPpBSP,
  author =       "Dimitris C. Dracopoulos and Simon Kent",
  title =        "Speeding up Genetic Programming: {A} Parallel {BSP}
                 Implementation",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "421",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@InProceedings{Dracopoulos:1997:es,
  author =       "Dimitris C. Dracopoulos",
  title =        "Evolutionary Control of a Satellite",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "77--81",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  size =         "pages",
  notes =        "GP-97",
}

@Book{dracopoulos:1997:elanac,
  author =       "Dimitris C. Dracopoulos",
  title =        "Evolutionary Learning Algorithms for Neural Adaptive
                 Control",
  publisher =    "Springer Verlag",
  year =         "1997",
  series =       "Perspectives in Neural Computing",
  address =      "P.O. Box 31 13 40, D-10643 Berlin, Germany",
  month =        aug,
  email =        "orders@springer.de",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-76161-6",
  URL =          "http://www.springer.de/catalog/html-files/deutsch/comp/3540761616.html",
  abstract =     "Neural networks and evolutionary algorithms are
                 constantly expanding their field of application to a
                 variety of new domains. One area of particular interest
                 is their applicability to control and adaptive control
                 systems: the limitations of the classical control
                 theory combined with the need for greater robustness,
                 adaptivity and ``intelligence'' make neurocontrol and
                 evolutionary control algorithms an attractive (and in
                 some cases, the only) alternative.

                 After an introduction to neural networks and genetic
                 algorithms, this volume describes in detail how neural
                 networks and evolutionary techniques (specifically
                 genetic algorithms and genetic programming) can be
                 applied to the adaptive control of complex dynamic
                 systems (including chaotic ones). A number of examples
                 are presented and useful tips are given for the
                 application of the techniques described. The
                 fundamentals of dynamic systems theory and classical
                 adaptive control are also given.",
  notes =        "Chapter 7 deals with genetic algorithms, including 8
                 pages on genetic programming. These include solving the
                 problem described in Dracopoulos:1997:es",
  size =         "212 pages",
}

@InCollection{dracopoulos:1997:GAGPc,
  author =       "Dimitris C. Dracopoulos",
  title =        "Genetic Algorithms and Genetic Programming for
                 Control",
  booktitle =    "Evolutionary Algorithms in Engineering Applications",
  publisher =    "Springer-Verlag",
  year =         "1997",
  editor =       "Dipankar Dasupta and Zbigniew Michalewicz",
  pages =        "329--343",
  address =      "Berlin",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-62021-4",
  notes =        "brief survey of GA and GP in control. Principly
                 concentrates upon using GP to control a tumbling
                 satellite",
  size =         "pages",
}

@InProceedings{drechsler:1996:GAshtOKFDD,
  author =       "Rold Drechsler and Bernd Becker and Nicole Gockel",
  title =        "A Genetic Algorithm for the Construction of Small and
                 Highly Testable {OKFDD} Circuits",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "473--478",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 GA paper",
}

@InProceedings{drechsler:2001:EuroGP,
  author =       "Nicole Drechsler and Frank Schmiedle and Daniel Grosse
                 and Rolf Drechsler",
  title =        "Heuristic Learning based on Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "1--10",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Heuristic
                 Learning, VLSI CAD, BDD, Binary Decision Diagrams",
  ISBN =         "3-540-41899-7",
  size =         "10 pages",
  abstract =     "In this paper we present an approach to learning
                 heuristics based on Genetic Programming (GP). Instead
                 of directly solving the problem by application of GP,
                 GP is used to develop a heuristic that is applied to
                 the problem instance. By this, the typical large
                 runtimes of evolutionary methods have to be invested
                 only once in the learning phase. The resulting
                 heuristic is very fast. The technique is applied to a
                 field from the area of VLSI CAD, i.e. minimization of
                 Binary Decision Diagrams (BDDs). We chose this topic
                 due to its high practical relevance and since it
                 matches the criteria where our algorithm works best,
                 i.e. large problem instances where standard
                 evolutionary techniques cannot be applied due to their
                 large runtimes. Our experiments show that we obtain
                 high quality results that outperform previous methods,
                 while keeping the advantage of low runtimes.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@TechReport{drecourt:1999uANNGPrrmTR,
  author =       "Jean-Philippe Drecourt",
  title =        "Application Of Neural Networks And Genetic Programming
                 To Rainfall Runoff modeling",
  institution =  "Danish Hydraulic Institute (Hydro-Informatics
                 Technologies HIT)",
  year =         "1999",
  type =         "D2K Technical Report",
  number =       "D2K-0699-1",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://projects.dhi.dk/d2k/Publications/D2K-TR-0699-01.pdf",
  abstract =     "The main problem in rainfall/runoff modeling is to
                 obtain data about the catchment with sufficient
                 accuracy. Since self-learning tools only need knowledge
                 about rainfall and runoff, they can offer a good
                 alternative to classical model. The present study
                 focuses on Lindenborg, a Danish catchment situated in
                 the northern part of Jutland, between Hobro and Ålborg.
                 It is characterized by high groundwater contribution
                 and thus a very persistent flow regime. The tools used
                 were artificial neural networks (ANN) and genetic
                 programming (GP). The purpose was to compare the
                 efficiency of these tools with a classic lumped model
                 (NAM) and a naïve prediction (i.e. the runoff does not
                 change between one day and the next one). The study
                 with GP was oriented in two directions: the prediction
                 of the runoff, and the prediction of the variation in
                 the runoff. In both cases GP was given the rainfall and
                 runoff of the past days, and it was assumed that the
                 rainfall was predicted without any error for the target
                 day. Each strategy has its own advantages. Predicting
                 the variation is considered to be closer to the
                 relationships given by physics, whereas predicting the
                 runoff takes in account the large auto-correlation of
                 the runoff time series. Since it is difficult to
                 predict the upper boundary of runoff, the ANN worked
                 exclusively with the time variation. The variation in
                 runoff is less likely to saturate the network than the
                 runoff itself, especially in this catchment where the
                 dynamics are relatively slow. Therefore, the
                 sensitivity of the prediction is increased. Time lag
                 recurrent network (TLRN) were used for this study as
                 they allow to take in account smoothed version of the
                 past time series, both in the input and the hidden
                 layers. The comparison of the different models was
                 based on the Pearson coefficient of correlation, which
                 gives a good overview of the performance of the
                 prediction.",
  notes =        "See also drecourt:1999uANNGPrrm",
  size =         "38 pages",
}

@InProceedings{drecourt:1999uANNGPrrm,
  author =       "J-P. Drecourt",
  title =        "Using Artificial Neural Networks and Genetic
                 Programming in rainfall/runoff modeling",
  booktitle =    "3rd DHI Software Conference \& DHI Software Courses",
  year =         "1999",
  address =      "Helsingør, Denmark",
  month =        "7-11 " # jun,
  organisation = "Danish Hydraulic Institute",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dhi.dk/softcon/abstract/102.doc",
  abstract =     "The main problem in rainfall/runoff modeling is to
                 obtain data about the catchment with sufficient
                 accuracy. Since self-learning tools only need knowledge
                 about rainfall and runoff, they can offer a good
                 alternative to classical model. The present study
                 focuses on Lindenborg, a Danish catchment situated in
                 the northern part of Jutland, between Hobro and Ålborg.
                 It is characterized by high groundwater contribution
                 and thus a very persistent flow regime. The tools used
                 were artificial neural networks (ANN) and genetic
                 programming (GP). The purpose was to compare the
                 efficiency of these tools with a classic lumped model
                 (NAM) and a naïve prediction (i.e. the runoff does not
                 change between one day and the next one). The study
                 with GP was oriented in two directions : the prediction
                 of the runoff, and the prediction of the variation in
                 the runoff. In both cases GP was given the rainfall and
                 runoff of the past days, and it was assumed that the
                 rainfall was predicted without any error for the target
                 day. Each strategy has its own advantages. Predicting
                 the variation is considered to be closer to the
                 relationships given by physics, whereas predicting the
                 runoff takes in account the large auto-correlation of
                 the runoff time series. Since it is difficult to
                 predict the upper boundary of runoff, the ANN worked
                 exclusively with the time variation. The variation in
                 runoff is less likely to saturate the network than the
                 runoff itself, especially in this catchment where the
                 dynamics are relatively slow. Therefore, the
                 sensitivity of the prediction is increased. Time lag
                 recurrent network (TLRN) were used for this study as
                 they allow to take in account smoothed version of the
                 past time series, both in the input and the hidden
                 layers. The comparison of the different models was
                 based on the Pearson coefficient of correlation, which
                 gives a good overview of the performance of the
                 prediction. This study was realized in relationship
                 with the Department of Hydrodynamics and Water
                 Resources of DTU as a special course for the Master of
                 Science in Environmental Engineering.",
  notes =        "http://www.dhi.dk/softcon/index.htm See also
                 drecourt:1999uANNGPrrmTR",
}

@InProceedings{Dreschler:1997:BEA,
  author =       "Rolf Dreschler and Nicole Gockel and Elke Mackensen
                 and Bernd Becker",
  title =        "{BEA}: Specialized Hardware for Implementation of
                 Evolutionary Algorithms",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Evolvable Hardware",
  pages =        "491",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{drost:2000:mbea,
  author =       "Stefan Droste and Dirk Wiesmann",
  title =        "Metric Based Evolutionary Algorithms",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "29--43",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "In this article a set of guidelines for the design of
                 genetic operators and the representation of the
                 phenotype space is proposed. These guidelines should
                 help to systematize the design of problem-specific
                 evolutionary algorithms. Hence, they should be
                 particularly beneficial for the design of genetic
                 programming systems. The applicability of this concept
                 is shown by the systematic design of a genetic
                 programming system for finding Boolean functions. This
                 system is the first GP-system, that reportedly found
                 the 12 parity function.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{Droste:1997:eGPbf,
  author =       "Stefan Droste",
  title =        "Efficient Genetic Programming for Finding Good
                 Generalizing Boolean Functions",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "82--87",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  size =         "pages",
  notes =        "GP-97",
}

@InProceedings{droste:1998:GPgq,
  author =       "Stefan Droste",
  title =        "Genetic Programming with Guaranteed Quality",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "54--59",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{droste:1999:PNFLBALFA,
  author =       "Stefan Droste and Thomas Jansen and Ingo Wegener",
  title =        "Perhaps Not a Free Lunch But At Least a Free
                 Appetizer",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "833--839",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{DrostePPSN2000,
  author =       "Stefan Droste and Dominic Heutelbeck and Ingo
                 Wegener",
  title =        "Distributed Hybrid Genetic Programming for Learning
                 Boolean Functions",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VI 6th
                 International Conference",
  editor =       "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
                 Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
                 Merelo and Hans-Paul Schwefel",
  year =         "2000",
  publisher =    "Springer Verlag",
  address =      "Paris, France",
  month =        "16-20 " # sep,
  note =         "LNCS 1917",
  pages =        "181--190",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{duan:2001:gecco,
  title =        "Estimating Stock Price Predictability Using Genetic
                 Programming",
  author =       "Minglei Duan and Richard J. Povinelli",
  pages =        "174",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster, time
                 series, data mining, prediction",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{duffy:1999:CEF,
  author =       "John Duffy and Jim Engle-Warnick",
  title =        "Using Symbolic Regression to Infer Strategies from
                 Experimental Data",
  booktitle =    "Fifth International Conference: Computing in Economics
                 and Finance",
  year =         "1999",
  editor =       "David A. Belsley and Christopher F. Baum",
  pages =        "150",
  address =      "Boston College, MA, USA",
  month =        "24-26 " # jun,
  note =         "Book of Abstracts",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.pitt.edu/~jduffy/docs/Usr.ps",
  abstract =     "We propose the use of a new technique -- symbolic
                 regression -- as a method for inferring the strategies
                 that are being played by subjects in economic decision
                 making experiments. We begin by describing symbolic
                 regression and our implementation of this technique
                 using genetic programming. We provide a brief overview
                 of how our algorithm works and how it can be used to
                 uncover simple data generating functions that have the
                 flavor of strategic rules. We then apply symbolic
                 regression using genetic programming to experimental
                 data from the ultimatum game. We discuss and analyze
                 the strategies that we uncover using symbolic
                 regression and we conclude by arguing that symbolic
                 regression techniques should at least complement
                 standard regression analyses of experimental data.",
  notes =        "CEF'99 See also duffy:1999:srised
                 http://fmwww.bc.edu/cef99/sess/chen.cfp.html",
  size =         "21 pages",
}

@InCollection{duffy:1999:srised,
  author =       "John Duffy and Jim Engle-Warnick",
  title =        "Using Symbolic Regression to Infer Strategies from
                 Experimental Data",
  booktitle =    "Evolutionary Computation in Economics and Finance",
  publisher =    "Springer-Verlag",
  year =         "1999",
  editor =       "Shu-Heng Chen",
  note =         "to appear",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.pitt.edu/~jduffy/docs/Usr.ps",
  abstract =     "We propose the use of a new technique -- symbolic
                 regression -- as a method for inferring the strategies
                 that are being played by subjects in economic decision
                 making experiments. We begin by describing symbolic
                 regression and our implementation of this technique
                 using genetic programming. We provide a brief overview
                 of how our algorithm works and how it can be used to
                 uncover simple data generating functions that have the
                 flavor of strategic rules. We then apply symbolic
                 regression using genetic programming to experimental
                 data from the ultimatum game. We discuss and analyze
                 the strategies that we uncover using symbolic
                 regression and we conclude by arguing that symbolic
                 regression techniques should at least complement
                 standard regression analyses of experimental data.",
  notes =        "Presented at CEF'99 (see duffy:1999:CEF)
                 http://fmwww.bc.edu/cef99/sess/chen.cfp.html

                 ",
  size =         "21 pages",
}

@InProceedings{dulewicz:2001:HIS,
  title =        "Evolving Natural Language Parser with Genetic
                 Programming",
  author =       "Grzegorz Dulewicz and Olgierd Unold",
  editor =       "Ajith Abraham and Mario Koppen",
  booktitle =    "2001 International Workshop on Hybrid Intelligent
                 Systems",
  series =       "LNCS",
  pages =        "361--378",
  publisher =    "Springer-Verlag",
  address =      "Adelaide, Australia",
  publisher_address = "Berlin",
  month =        "11-12 " # dec,
  year =         "2001",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6",
  ISBN =         "3-7908-1480-6",
  keywords =     "genetic algorithms, genetic programming, natural
                 language processing, edge encoding",
  abstract =     "1 Introduction When we try to deal with natural
                 language processing (NLP) we have to start with a
                 grammar of a natural language. But the grammars
                 described in linguistic literature have an informal
                 form and many exceptions. Thus, they are not useful to
                 create final formal models of grammars, which make
                 machine processing of sentences possible. These
                 grammars can be a starting point for the attempts to
                 create basic models of natural language grammar at the
                 most. However, it requires expert knowledge. Machine
                 learning based on a set of sample sentences can be the
                 better way to find the grammar rules. This kind of
                 learning allows to avoid the preparation of knowledge
                 about the language for the NLP system. The examples of
                 correct and incorrect sentences allow the NLP systems
                 with the self-evolutionary parser to try to find the
                 right grammar. This self-evolutionary parser can be
                 improved on basis of new examples. Thus, the knowledge
                 acquired in this way is flexible and easyly
                 modifiable.",
  notes =        "HIS01",
}

@InProceedings{Dunay:1994:rliGP,
  author =       "B. D. Dunay and F. E. Petry and W. P Buckles",
  title =        "Regular language induction with genetic programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "396--400",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "5 pages",
  notes =        "Considers two classes of regular language (NB series
                 and Tomita) which can be recognised or accpeted by
                 deterministic finite automata (Finite state machines).
                 Can translate from DFA to tree structure. Trees are not
                 executable programs but represent languages. crossover
                 on trees defined. GP able to define a language given
                 examples of it. Works on simplier examples but has
                 difficulties with 8b, 9b, 10b and TL5.",
}

@InProceedings{dunay:1995:scpga,
  author =       "Bertrand Daniel Dunay and Frederic E. Petry",
  title =        "Solving Complex Problems with Genetic Algorithms",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "264--270",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-370-0",
  size =         "7 pages",
  abstract =     "Using GA to evolve Turing machines which recognise
                 languages from the Chomsky heirarchy. Example for
                 regular languages (awb), context free languages
                 (a**nb**n) and context sensitive languages
                 (a**nb**na**n).",
}

@InProceedings{dunning:1996:eanlp,
  author =       "Ted E. Dunning and Mark W. Davis",
  title =        "Evolutionary Algorithms for Natural Language
                 Processing",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "16--23",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming, NLP",
  ISBN =         "0-18-201031-7",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{dupas:1999:RAO,
  author =       "R. Dupas and G. Cavory and G. Goncalves",
  title =        "Real-World Applications. Optimising the throughput of
                 a manufacturing production line using a genetic
                 algortihm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1775",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{durand:1998:gxpsf,
  author =       "Nicolas Durand and Jean-Marc Alliot",
  title =        "Genetic crossover operator for partially separable
                 functions",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "487--494",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{dworman:1995:b3acg,
  author =       "Garett Dworman and Steven O. Kimbrough and James D.
                 Laing",
  title =        "Bargaining in a Three-Agent Coalition Game: An
                 Application of Genetic Programming",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "9--16",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@TechReport{dworman:1995:iGPSgt,
  author =       "Garett Dworman and Steve O. Kimbrough and James D.
                 Laing",
  title =        "Implementation of a Genetic Programming System in a
                 Game-Theoretic Context",
  institution =  "University of Pennsylvania, Department of Operations
                 and Information Management",
  year =         "1995",
  type =         "working paper",
  number =       "95-01-02",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://opim.wharton.upenn.edu/users/sok/comprats/GPWP01.ps",
  size =         "14 pages",
}

@InProceedings{dworman:1996:admGPgtc,
  author =       "Garett Dworman and Steve O. Kimbrough and James D.
                 Laing",
  title =        "On Automated Discovery of Models Using Genetic
                 Programming in Game-Theoretic Contexts",
  booktitle =    "Proceedings of the 28th Hawaii International
                 Conference on System Sciences, Volume III: Information
                 Systems: Decision Support and Knowledge-based Systems",
  year =         "1995",
  editor =       "Jay F. {Nunamaker Jr.} and Ralph H. {Sprague Jr.}",
  pages =        "428--438",
  publisher_address = "Los Alamitos, CA",
  month =        jan,
  publisher =    "IEEE Computer Society Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://opim.wharton.upenn.edu/users/sok/comprats/HICSSGP6.ps",
  url_2 =        "http://opim.wharton.upenn.edu/users/sok/comprats/HICSSGP6-figures.eps",
  size =         "13 pages",
}

@InProceedings{dworman:1996:baa2cg,
  author =       "Garett Dworman and Steven O. Kimbrough and James D.
                 Laing",
  title =        "Bargaining by Artificial Agents in Two Coalition
                 Games: {A} Study in Genetic Programming for Electronic
                 Commerce",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "54--62",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{dzeroski:1995:dsiml,
  author =       "Saso Dzeroski and Ljupeo Todorovski and Igor
                 Petrovski",
  title =        "Dynamical System Identification with Machine
                 Learning",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "50--63",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  size =         "14 pages",
  abstract =     "LAGRANGE algorithm described, brusselator,
                 volterra-lotka model of population dynamics, monod
                 equations, pole balancing, system identification.",
  notes =        "part of rosca:1995:ml",
}

@InProceedings{east:1999:IWOPUGA,
  author =       "E. William East",
  title =        "Infrastructure Work Order Planning Using Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1510--1516",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 Seeding initial population Order-based chromosome",
}

@InProceedings{Eberbach:1997:eGPdc,
  author =       "Eugene Eberbach",
  title =        "Enhancing Genetic Programming by \$-calculus",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "88",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  size =         "pages",
  notes =        "GP-97",
}

@InProceedings{eberbach:1998:xECGPALAADNAClc,
  author =       "Eugene Eberbach",
  title =        "Expressing Evolutionary Computation, Genetic
                 Programming, Artificial Life, Autonomous Agents and
                 {DNA}-Based Computing in l-Calculus",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{eberbach:2000:eecgpalaadc,
  author =       "Eugene Eberbach",
  title =        "Expressing Evolutionary Computation, Genetic
                 Programming, Artificial Life, Autonomous Agents, and
                 {DNA}-Based Computing in \$-Calculus",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1361--1368",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, new
                 paradigms",
  ISBN =         "0-7803-6375-2",
  abstract =     "Genetic programming, autonomous agents, artificial
                 life and evolutionary computation share many common
                 ideas. They generally investigate distributed complex
                 processes, perhaps with the ability to interact. It
                 seems to be natural to study their behavior using
                 process algebras, which were designed to handle
                 distributed interactive systems. \$-calculus is a
                 higher-order polyadic process algebra for resource
                 bounded computation. It has been designed to handle
                 autonomous agents, evolutionary computing, neural nets,
                 expert systems, machine learning, and distributed
                 interactive AI systems, in general. \$-calculus has
                 built-in cost-optimization mechanism allowing to deal
                 with nondeterminism, incomplete and uncertain
                 information. In this paper, we express in \$-calculus
                 several subareas of evolutionary computation, including
                 genetic programming, artif icial life, autonomous
                 agents and DNA-based comput-ing.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@InProceedings{Ebner:1997a,
  author =       "Marc Ebner",
  title =        "Evolution of Hierarchical Translation-Invariant
                 Feature Detectors with an Application to Character
                 Recognition",
  booktitle =    "Mustererkennung 1997, 19. DAGM-Symposium
                 Braunschweig",
  year =         "1997",
  editor =       "Erwin Paulus and Friedrich M. Wahl",
  pages =        "456--463",
  publisher_address = "Berlin",
  month =        "15-17 " # sep,
  publisher =    "Springer-Verlag",
  address =      "Berlin",
  keywords =     "evolution strategies, structure evolution, feature
                 detection",
  ISBN =         "3-540-63426-6",
  notes =        "

                 ",
}

@InProceedings{Ebner:1997b,
  author =       "Marc Ebner",
  title =        "On the Evolution of Edge Detectors for Robot Vision
                 using Genetic Programming",
  booktitle =    "Workshop SOAVE '97 - Selbstorganisation von Adaptivem
                 Verhalten, VDI Reihe 8 Nr. 663",
  year =         "1997",
  pages =        "127--134",
  editor =       "Horst-Michael Gro{\ss}",
  address =      "D{\"u}sseldorf",
  publisher =    "VDI Verlag",
  keywords =     "genetic algorithms, genetic programming, edge
                 detection",
  ISBN =         "3-18-366308-2",
  notes =        "

                 ",
}

@InProceedings{ebner:1998:eioGP,
  author =       "Marc Ebner",
  title =        "On the Evolution of Interest Operators using Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at EuroGP'98: the First European
                 Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and Marc Schoenauer
                 and Terry Fogarty and Wolfgang Banzhaf",
  pages =        "6--10",
  address =      "Paris, France",
  publisher_address = "School of Computer Science",
  month =        "14-15 " # apr,
  publisher =    "CSRP-98-10, The University of Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-ra.informatik.uni-tuebingen.de/mitarb/ebner/documents/gpmoravec.ps.gz",
  size =         "5 pages",
  notes =        "EuroGP'98LB part of Poli:1998:egplb",
}

@InProceedings{Ebner:1998c,
  author =       "Marc Ebner",
  title =        "Evolution of a control architecture for a mobile
                 robot",
  booktitle =    "Proceedings of the Second International Conference on
                 Evolvable Systems: From Biology to Hardware (ICES 98)",
  year =         "1998",
  editor =       "Moshe Sipper and Daniel Mange and Andrés Pérez-Uribe",
  volume =       "1478",
  series =       "LNCS",
  pages =        "303--310",
  address =      "Lausanne, Switzerland",
  publisher_address = "Berlin",
  month =        "23-25 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64954-9",
  URL =          "http://www-ra.informatik.uni-tuebingen.de/mitarb/ebner/documents/gprealrob.ps.gz",
  abstract =     "Programming a robot to perform a desired task in an
                 unknown environment is a difficult task. Due to
                 unexpected interactions between the environment and the
                 robot many iterations of program development are often
                 required. Using genetic programming the robots
                 themselves may search the space of possible programs.
                 In an experiment which was conducted over a period of
                 two months we evolved a behavior-based control
                 architecture for a large sized mobile robot, a Real
                 World Interface B21. This is the first time that a
                 large mobile robot was used in evolutionary robotics
                 using tree-based genetic programming. In addition, our
                 architecture uses conditional statements to build up a
                 hierarchical reactive control structure. Sonar sensors
                 are used to sense the environment. Because the robot is
                 able to exert a considerable force if it crashes into
                 an object, safety measures had to be taken to
                 automatically monitor the run.",
  notes =        "ICES98",
}

@InProceedings{ebner:1999:eemrl,
  author =       "Marc Ebner",
  title =        "Evolving an Environment Model for Robot Localization",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "184--192",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@InProceedings{ebner:1999:etsio,
  author =       "Marc Ebner and Andreas Zell",
  title =        "Evolving a Task Specific Image Operator",
  booktitle =    "Evolutionary Image Analysis, Signal Processing and
                 Telecommunications: First European Workshop, EvoIASP'99
                 and EuroEcTel'99",
  year =         "1999",
  editor =       "Riccardo Poli and Hans-Michael Voigt and Stefano
                 Cagnoni and Dave Corne and George D. Smith and Terence
                 C. Fogarty",
  volume =       "1596",
  series =       "LNCS",
  pages =        "74--89",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "28-29 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65837-8",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-65837-8",
  notes =        "EvoIASP99'99",
}

@InProceedings{ebner:1999:EF,
  author =       "Marc Ebner and Andreas Zell",
  title =        "Evolving a behavior-based control architecture- From
                 simulations to the real world",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1009--1014",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ebner:1999:OSSGPIRNSS,
  author =       "Marc Ebner",
  title =        "On the Search Space of Genetic Programming and Its
                 Relation to Nature's Search Space",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1357--1361",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, models of
                 evolutionary computation",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{ebner:2001:EuroGP,
  author =       "Marc Ebner",
  title =        "Evolving Color Constancy for an Artificial Retina",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "11--22",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Color
                 Constancy, Artificial Retina, Image Processing",
  ISBN =         "3-540-41899-7",
  size =         "12 pages",
  abstract =     "Objects retain their color in spite of changes in the
                 wavelength and energy composition of the light they
                 reflect. This phenomenon is called color constancy and
                 plays an important role in computer vision research. We
                 have used genetic programming to automatically search
                 the space of programs to solve the problem of color
                 constancy for an artificial retina. This retina
                 consists of a two dimensional array of elements each
                 capable of exchanging information with its adjacent
                 neighbors. The task of the program is to compute the
                 intensities of the light illuminating the scene. These
                 intensities are then used to calculate the reflectances
                 of the object. Randomly generated color Mondrians were
                 used as fitness cases. The evolved program was tested
                 on artificial Mondrians and natural images.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{ebner:2002:EuroGP,
  title =        "Coevolution Produces an Arms Race Among Virtual
                 Plants",
  author =       "Marc Ebner and Adrian Grigore and Alexander Heffner
                 and J{\"u}rgen Albert",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "316--325",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "Creating interesting virtual worlds is a difficult
                 task. We are using a variant of genetic programming to
                 automatically create plants for a virtual environment.
                 The plants are represented as context-free Lindenmayer
                 systems. OpenGL is used to visualize and evaluate the
                 plants. Our plants have to collect virtual sunlight
                 through their leaves in order to reproduce
                 successfully. Thus we have realized an interaction
                 between the plant and its environment. Plants are
                 either evaluated separately or all individuals of a
                 population at the same time. The experiments show that
                 during coevolution plants grow much higher compared to
                 rather bushy plants when plants are evaluated in
                 isolation.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InCollection{Ebstyne:1997:msm,
  author =       "Michael J. Ebstyne",
  title =        "Musings on Syncopation and Machines",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "36--46",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "music",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{edelson:1999:ECCFIGCUASIFPM,
  author =       "William Edelson and Michael L. Gargano",
  title =        "Efficient Calculation of Compute-Intensive Fitness In
                 Genetic Computations Using {A} Survival Indicator For
                 Population Members",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "784",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{edmonds:1995:fuzzy,
  author =       "Andrew N. Edmonds and Diana Burkhardt and Osei Adjei",
  title =        "Genetic Programming of Fuzzy Logic Production Rules",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "765",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.scifi.co.uk/pub/docs/ICECPS.z",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html",
}

@InCollection{edmonds:1997:mbrea,
  author =       "Bruce Edmonds and Scott Moss",
  title =        "Modelling of Boundedly Rational Agents using
                 Evolutionary Programming Techniques",
  booktitle =    "Evolutionary Computing",
  publisher =    "Springer-Verlag",
  year =         "1997",
  editor =       "David Corne and Jonathan L. Shapiro",
  volume =       "1305",
  series =       "LNCS",
  pages =        "31--42",
  address =      "University of Manchester, UK",
  month =        "7-8 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-63476-2",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-63476-2",
  notes =        "Papers in AISB-97 Evolutionaru computation workshop
                 proceedings may be revised before final publication.
                 http://www.cs.man.ac.uk/ai/AISB97/text.html#evolut

                 Former soviet union. Strictly declarative modelling
                 language SDML. 3 sets of runs, agents have memory of
                 different sizes, space for 10, 20 or 30 models.
                 http://www.fmb.mmu.ac.uk",
  size =         "12 pages",
}

@TechReport{edmonds:1998:mGPcov,
  author =       "Bruce Edmonds",
  title =        "Meta-Genetic Programming: Co-evolving the Operators of
                 Variation",
  institution =  "Centre for Policy Modelling, Manchester Metropolitan
                 University, UK",
  year =         "1998",
  type =         "CPM Report",
  number =       "98-32",
  address =      "Aytoun St., Manchester, M1 3GH. UK",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, automatic
                 programming, genetic operators, co-evolution",
  URL =          "http://www.cpm.mmu.ac.uk/cpmrep32.html",
  abstract =     "The standard Genetic Programming approach is augmented
                 by co-evolving the genetic operators. To do this the
                 operators are coded as trees of indefinite length. In
                 order for this technique to work, the language that the
                 operators are defined in must be such that it preserves
                 the variation in the base population. This technique
                 can varied by adding further populations of operators
                 and changing which populations act as operators for
                 others, including itself, thus to provide a framework
                 for a whole set of augmented GP techniques. The
                 technique is tested on the parity problem. The pros and
                 cons of the technique are discussed.",
  notes =        "see edmonds:2001:mGPcov",
}

@InProceedings{edmonds:1998:gsrefb,
  author =       "Bruce Edmonds",
  title =        "Gossip, Sexual Recombination and the {El Farol Bar:}
                 modelling the emergence of heterogeneity",
  booktitle =    "Proceedings of the 1998 Conference on Computation in
                 Economics, Finance and Engineering",
  year =         "1998",
  address =      "Cambridge",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "agents also learn and communicate. Each gene is
                 composed of two tree-structures: one to control its
                 actions and one to determine communication.",
  notes =        "coevolution, bounded rationality. Communicate (talk)
                 one branch first. Then action (go to bar OR not go).
                 STGP. page 3 {"}total population was 5 in this
                 example{"}. SDML. problem specific terminal and
                 function sets (different for two branches)",
}

@Article{edmonds:1999:r5GP,
  author =       "Bruce Edmonds",
  title =        "The Uses of Genetic Programming in Social Simulation:
                 {A} Review of Five Books",
  journal =      "The Journal of Artificial Societies and Social
                 Simulation",
  year =         "1999",
  volume =       "2",
  number =       "1",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.soc.surrey.ac.uk/JASSS/2/1/review1.html",
  size =         "40957 bytes",
  abstract =     "Moderately extensive introduction to GP followed by
                 review of the following five books from the perspective
                 of Social Simulation:

                 Genetic Programming: On the Programming of Computers by
                 Natural Selection John R. Koza Cambridge, MA: The
                 M.I.T. Press 1992 koza:book

                 Genetic Programming II: Automatic Discovery of Reusable
                 Programs John R. Koza Cambridge, MA: The M.I.T. Press,
                 A Bradford Book 1994 koza:gp2

                 Advances in Genetic Programming Edited by Kenneth E.
                 Kinnear Jr. Cambridge, MA: The M.I.T. Press, A Bradford
                 Book 1994 kinnear:book

                 Advance in Genetic Programming Volume 2 Edited by Peter
                 J. Angeline and Kenneth E. Kinnear Jr. Cambridge, MA:
                 The M.I.T. Press, A Bradford Book 1996
                 book:1996:aigp2

                 Genetic Programming and Data Structures William B.
                 Langdon Dordrecht: Kluwer Academic Publishers 1998
                 langdon:book",
  notes =        "JASSS",
}

@Article{edmonds:2000:aigp,
  author =       "Bruce Edmonds",
  title =        "A Review of the {"}Advances in Genetic Programming{"}
                 Series (Volumes 1, 2 and 3)",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "3",
  pages =        "289--296",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1389-2576",
  notes =        "kinnear:book book:1996:aigp2 book:1999:aigp3",
}

@InCollection{edmonds:2001:MUC,
  author =       "Bruce Edmonds",
  title =        "Learning Appropriate Contexts",
  booktitle =    "Modelling and Using Context",
  publisher =    "Springer-Verlag",
  year =         "2001",
  editor =       "Varol Akman and Paolo Bouquet and Richard Thomason and
                 Roger Young",
  volume =       "2116",
  series =       "LNAI",
  pages =        "143--155",
  month =        "27-30 " # jul,
  email =        "b.edmonds@mmu.ac.uk",
  keywords =     "genetic algorithms, genetic programming, learning,
                 conditions of application, context, evolutionary
                 computing, error",
  ISBN =         "3-540-42379-6",
  URL =          "http://www.cpm.mmu.ac.uk/cpmrep78.html",
  size =         "13 pages",
  abstract =     "Genetic Programming is extended so that the solutions
                 being evolved do so in the context of local domains
                 within the total problem domain. This produces a
                 situation where different {"}species{"} of solution
                 develop to exploit different {"}niches{"} of the
                 problem indicating exploitable solutions. It is argued
                 that for context to be fully learnable a further step
                 of abstraction is necessary. Such contexts abstracted
                 from clusters of solution/model domains make sense of
                 the problem of how to identify when it is the content
                 of a model is wrong and when it is the context. Some
                 principles of learning to identify useful contexts are
                 proposed.",
  notes =        "Volume in the proceedings of the 3rd International and
                 interdisciplinary conference, CONTEXT 2001, Dundee, UK,
                 July 2001
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42379-6",
}

@Article{edmonds:2001:mGPcov,
  author =       "Bruce Edmonds",
  title =        "Meta-Genetic Programming: Co-evolving the Operators of
                 Variation",
  journal =      "Elektrik",
  year =         "2001",
  volume =       "9",
  number =       "1",
  pages =        "13--29",
  month =        may,
  note =         "Turkish Journal Electrical Engineering and Computer
                 Sciences",
  keywords =     "genetic algorithms, genetic programming, automatic
                 programming, genetic operators, co-evolution",
  ISSN =         "1300-0632",
  URL =          "http://journals.tubitak.gov.tr/elektrik/issues/elk-01-9-1/elk-9-1-2-0008-5.pdf",
  URL =          "http://cogprints.ecs.soton.ac.uk/archive/00001776/00/mgp.pdf",
  abstract =     "The standard Genetic Programming approach is augmented
                 by co-evolving the genetic operators. To do this the
                 operators are coded as trees of indefinite length. In
                 order for this technique to work, the language that the
                 operators are defined in must be such that it preserves
                 the variation in the base population. This technique
                 can varied by adding further populations of operators
                 and changing which populations act as operators for
                 others, including itself, thus to provide a framework
                 for a whole set of augmented GP techniques. The
                 technique is tested on the parity problem. The pros and
                 cons of the technique are discussed.",
  notes =        "Elektrik http://www.tubitak.gov.tr/journals/elektrik/
                 see edmonds:1998:mGPcov",
}

@Article{edwards:1995:nature,
  author =       "A. W. F. Edwards",
  title =        "Forced Evolution",
  journal =      "Nature",
  year =         "1995",
  volume =       "375",
  pages =        "11",
  month =        "6 " # jul,
  notes =        "Notes {"}Professor of speculative learning{"} at the
                 {"}Grand academy of Lagado{"} visited by Captain Lemuel
                 Gulliver in his travels. Cites work claiming this
                 travelogue was read by Charles Darwin in 1840.",
}

@InProceedings{eggermont:1999:affGPdm,
  author =       "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
  title =        "Adapting the Fitness Function in {GP} for Data
                 Mining",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "193--202",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  URL =          "http://www.liacs.nl/~jeggermo/publications/eurogp99.ps.gz",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@InProceedings{EEH99b,
  author =       "Jeroen Eggermont and Agoston E. Eiben and Jano I. {van
                 Hemert}",
  title =        "A comparison of genetic programming variants for data
                 classification",
  booktitle =    "Advances in Intelligent Data Analysis, Third
                 International Symposium, IDA-99",
  year =         "1999",
  editor =       "David J. Hand and Joost N. Kok and Michael R.
                 Berthold",
  volume =       "1642",
  series =       "LNCS",
  email =        "jvhemert@cs.leidenuniv.nl",
  pages =        "281--290",
  address =      "Amsterdam, The Netherlands",
  publisher_address = "Berlin",
  month =        "9--11 " # aug,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming,
                 classification",
  URL =          "http://www.liacs.nl/~jeggermo/publications/ida99.ps.gz",
  ISBN =         "3-540-66332-0",
  notes =        "IDA-99, Booleanization of inputs, ML: Australian
                 credit, German Credit, Heart Disease, Pima. steady
                 state. SAW-ing",
}

@InProceedings{EEH99bnaic,
  author =       "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
  title =        "A comparison of genetic programming variants for data
                 classification",
  booktitle =    "Proceedings of the Eleventh Belgium/Netherlands
                 Conference on Artificial Intelligence (BNAIC'99)",
  year =         "1999",
  editor =       "Eric Postma and Marc Gyssens",
  pages =        "253--254",
  address =      "Kasteel Vaeshartelt, Maastricht, Holland",
  month =        "3-4 " # nov,
  organisation = "BNVKI, Dutch and the Belgian AI Association",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz",
  size =         "2 pages",
  notes =        "resubmission of EEH99b
                 http://www.cs.unimaas.nl/~bnvki/bnaic99/",
}

@InProceedings{eggermon:2000:bnaic,
  author =       "J. Eggermont and J. I. {van Hemert}",
  title =        "Stepwise Adaptation of Weights for Symbolic Regression
                 with Genetic Programming",
  booktitle =    "Proceedings of the Twelveth Belgium/Netherlands
                 Conference on Artificial Intelligence (BNAIC'00)",
  year =         "2000",
  organisation = "BNVKI, Dutch and the Belgian AI Association",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz",
}

@InProceedings{eggermont_adaptive:2001:EuroGP,
  author =       "Jeroen Eggermont and Jano I. {van Hemert}",
  title =        "Adaptive Genetic Programming Applied to New and
                 Existing Simple Regression Problems",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "23--35",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Adaptation,
                 Symbolic Regression, Problem Generator, Program Trees",
  ISBN =         "3-540-41899-7",
  URL =          "http://www.liacs.nl/~jeggermo/publications/eurogp2001-symreg.ps.gz",
  size =         "13 pages",
  abstract =     "In this paper we continue our study on adaptive
                 genetic programming. We use Stepwise Adaptation of
                 Weights (SAW) to boost performance of a genetic
                 programming algorithm on simple symbolic regression
                 problems. We measure the performance of a standard GP
                 and two variants of SAW extensions on two different
                 symbolic regression problems from literature. Also, we
                 propose a model for randomly generating polynomials
                 which we then use to further test all three GP
                 variants.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{eggermont:2001:EuroGP_dead,
  author =       "Jeroen Eggermont and Tom Lenaerts and Sanna Poyhonen
                 and Alexandre Termier",
  title =        "Raising the Dead; Extending Evolutionary Algorithms
                 with a Case-based Memory",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "280--290",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Dynamic
                 Fitness, Global Memory",
  ISBN =         "3-540-41899-7",
  URL =          "http://www.liacs.nl/~jeggermo/publications/eurogp2001-dynamic.ps.gz",
  size =         "11 pages",
  abstract =     "In dynamically changing environments, the performance
                 of a standard evolutionary algorithm deteriorates. This
                 is due to the fact that the population, which is
                 considered to contain the history of the evolutionary
                 process, does not contain enough information to allow
                 the algorithm to react adequately to changes in the
                 fitness landscape. Therefore, we added a simple, global
                 case-based memory to the process to keep track of
                 interesting historical events. Through the introduction
                 of this memory and a storing and replacement scheme we
                 were able to improve the reaction capabilities of an
                 evolutionary algorithm with a periodically changing
                 fitness function.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{eggermont:2002:EuroGP,
  title =        "Evolving Fuzzy Decision Trees with Genetic Programming
                 and Clustering",
  author =       "Jeroen Eggermont",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "71--82",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "In this paper we present a new fuzzy decision tree
                 representation for n-category data classification using
                 genetic programming. The new fuzzy representation uses
                 fuzzy clusters for handling continuous attributes. To
                 make optimal use of the fuzzy classifications of this
                 representation an extra fitness measure is used. The
                 new fuzzy representation will be compared, using
                 several machine learning data sets, to a similar
                 non-fuzzy representation as well as to some other
                 evolutionary and non-evolutionary algorithms from
                 literature.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InCollection{eglit:1994:tpfts,
  author =       "Jason T. Eglit",
  title =        "Trend Prediction in Financial Time Series",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "31--40",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{eguchi:2002:gecco:lbp,
  title =        "Multiagent Systems with Symbiotic Learning and
                 Evolution Using Genetic Network Programming",
  author =       "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and
                 Junichi Murata",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "130--137",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

@InCollection{ehlis:2000:EITPDRUE,
  author =       "Tobin Ehlis",
  title =        "Evolution of Intelligent Task Prioritization in a
                 Dynamic Randomly Updated Environment",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "125--134",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@TechReport{ehrenburg:1995:fls,
  author =       "H. H. Ehrenburg and H. A. N. {van Maanen}",
  title =        "A Finite Automaton Learning System Using Genetic
                 Programming",
  institution =  "Department of Computer Science, CWI, Centrum voor
                 Wiskunde en Informmatica",
  year =         "1994",
  type =         "NeuroColt Tech Rep",
  number =       "CS-R9458",
  address =      "CWI, P.O. Box 94079, 1090 GB Amsterdam, The
                 Netherlands",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Computing, finite automata",
  URL =          "ftp://ftp.cwi.nl/pub/CWIreports/AA/CS-R9458.ps.Z",
  abstract =     "FALS is an evolutionary system that is designed to
                 find small digital circuits that duplicate the
                 behaviour of a given finite automaton. The system is
                 based upon the GP approach.

                 ",
  notes =        "

                 ",
  size =         "40 pages",
}

@InProceedings{ehrenburg:1996:iDAGcGP,
  author =       "Herman Ehrenburg",
  title =        "Improved Direct Acyclic Graph Handling and the Combine
                 Operator in Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "285--291",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96

                 {"}combine{"} genetic operator, ancestor
                 information.

                 p289 Does not {"}make use of the 32-fold speedup be
                 evaluating 32 fitness cases in parallel{"}. p290
                 {"}Martin C. Martin{"} CMU 32-parallelization
                 trick{"}.",
}

@InProceedings{Eetal96,
  author =       "A. E. Eiben and T. J. Euverman and W. Kowalczyk and E.
                 Peelen and F. Slisser and J. A. M. Wesseling",
  title =        "Comparing Adaptive and Traditional Techniques for
                 Direct Marketing",
  booktitle =    "Proceedings of the 4th European Congress on
                 Intelligent Techniq ues and Soft Computing",
  year =         "1996",
  editor =       "H.-J. Zimmermann",
  pages =        "434--437",
  publisher_address = "Aachen, Germany",
  publisher =    "Verlag Mainz",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@Unpublished{eiben:email:10-Nov-1997,
  author =       "Gusz Eiben",
  title =        "{GP} in Leiden",
  note =         "electronic communication",
  month =        "10 " # nov,
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 Organization: Leiden University, Dept. of Mathematics &
                 Computer Science, The Netherlands

                 Dear GP-ers,

                 Following Michele's example on

                 > everybody could just give a list of keywords, >
                 describing > - the main technical focus of the person
                 or the team > - the applications. here is the item on
                 the Leiden group.

                 Mainly applications and application oriented research
                 in the filed of marketing and financial services. Alas,
                 this implies that many of our projects are
                 confidential. Even if we are allowed to submit, we need
                 to leave out so many technical details that the
                 reviewers find it unacceptable. I hope you can use some
                 of this info.

                 <snip>

                 Cheers, Gusz
                 --------------------------------------------------------------------
                 Topics, resp. applications:

                 - Direct marketing application for a big multinational
                 computer manufacturer (see the publication below) -
                 Credit-Score-Card application for a medium size Dutch
                 bank

                 - Creditability evaluation application for a major
                 Dutch bank

                 - Data mining feature selection application for a small
                 Dutch software house

                 - Customer retention modelling for a major Dutch
                 investment fund

                 see EEKS98

                 Master Theses co-supervised by our group members (not
                 published)

                 M. Keijzer, Representing Computer Programs in Genetic
                 Programming, 1995 (in English). Supervised by A.E.
                 Eiben and M. Gerrets.

                 S. da Silva, Go and Genetic Programming: Playing Go
                 with Filter Functions, 1996 (in English). Supervised by
                 A.E. Eiben and H.J.M. Goeman.

                 H.D. Sneep, A Genetic Algorithms for the Development of
                 a Credit-Score-Card, 1994. Supervised by A.E. Eiben and
                 H.J. Gaaikema.

                 C. van Straten, Predictive Power of Genetic
                 Programming, 1995. Supervised by A.E. Eiben and J.A.M.
                 Wesseling.

                 C.J. Veenman, Positional Genetic Programming, 1996 (in
                 English). Supervised by A.E. Eiben and W.J. Kowalczyk
                 (see veennan:mastersthesis )

                 D. de Vries, Seeking for the Reliable Custumer with
                 Darwin, 1994. Supervised by A.E. Eiben and B.
                 Kersten.

                 D.L.T. Zwietering, Genetic Selection of Relevant
                 Features, 1995. Supervised by A.E. Eiben, E. Lebert and
                 D. Thierens.

                 Cheers

                 ",
}

@InCollection{EEKS98,
  author =       "A. E. Eiben and T. J. Euverman and W. Kowalczyk and F.
                 Slisser",
  title =        "Modelling Customer Retention with Statistical
                 Techniques, Rough Data Models and Genetic Programming",
  booktitle =    "Fuzzy Sets, Rough Sets and Decision Making Processes",
  publisher =    "Springer-Verlag",
  year =         "1998",
  editor =       "A. Skowron and S. K. Pal",
  address =      "Berlin",
  note =         "in press",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{eiben:1998:gmcr,
  author =       "A. E. Eiben and A. E. Koudijs and F. Slisser",
  title =        "Genetic Modelling of Customer Retention",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "178--186",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "This paper contains results of a research project
                 aiming at the application and evaluation of modern data
                 analysis techniques in the field of marketing. The
                 investigated techniques are: genetic programm ing,
                 rough data analysis, CHAID and logistic regression
                 analysis. All four techniques are applied independently
                 to the problem of customer retention modelling, using a
                 database of a financial company. Models created by
                 these techniques are used to gain insights into factors
                 influencing customer behaviour and to make predictions
                 on ending the relationship with the company in
                 question. Comparing the predictive power of the
                 obtained models shows that the genetic technology
                 offers the highest performance.",
  notes =        "EuroGP'98",
}

@InProceedings{eiben:1999:PA,
  author =       "A. E. Eiben and D. Elia and J. I. van Hemert",
  title =        "Population dynamics and emerging mental features in
                 {AEGIS}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1257--1264",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{eiben:1999:pcea,
  author =       "Agoston Endre Eiben and Robert Hintering and Zbigniew
                 Michalewicz",
  title =        "Parameter Control in Evolutionary Algorithms",
  journal =      "IEEE Transations on Evolutionary Computation",
  year =         "1999",
  volume =       "3",
  number =       "2",
  pages =        "124--141",
  keywords =     "evolutionary strategies, genetic algorithms",
  notes =        "Some mention of GP, particularly peter angeline's
                 work",
}

@InCollection{Eisenstein:1997:GAil,
  author =       "Jacob Eisenstein",
  title =        "Genetic Algorithms and Incremental Learning",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "47--56",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming, seeding",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{ekart:1998:gcd4bl,
  author =       "Aniko Ekart",
  title =        "Generating Class Descriptions of Four Bar Linkages",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB See also ekart:1999:ASI",
}

@InProceedings{ekart:1999:ccgGPm,
  author =       "Aniko Ekart",
  title =        "Controlling Code Growth in Genetic Programming by
                 Mutation",
  booktitle =    "Late-Breaking Papers of EuroGP-99",
  year =         "1999",
  editor =       "W. B. Langdon and Riccardo Poli and Peter Nordin and
                 Terry Fogarty",
  pages =        "3--12",
  address =      "Goteborg, Sweden",
  month =        "26-27 " # may,
  organisation = "EvoGP",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In the paper a method that moderate code growth in
                 genetic programming is presented. The addressed problem
                 is symbolic regression. A special mutation operator is
                 used for the simplification of programs. If every
                 individual program in each generation is simplified,
                 then performance of the genetic programming system is
                 worsened. But if simplification is applied as a
                 mutation operator, more compact solutions of the same
                 or better accuracy can be obtained",
  notes =        "EuroGP'99LB part of langdon:1999:egplb",
}

@InProceedings{ekart:1999:ASI,
  author =       "Aniko Ekart and Andras Markus",
  title =        "Decision Trees and Genetic Programming in Synthesis of
                 Four Bar Mechanisms",
  booktitle =    "Life Cycle Approaches to Production Systems,
                 Proceedings of the Advanced Summer Institute-ASI'99",
  year =         "1999",
  pages =        "210--208",
  address =      "Leuven",
  month =        "22-24 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "960-530-040-0",
  notes =        "http://www.lar.ee.upatras.gr/icims/asi/asi99/asi99.htm
                 See also ekart:1998:gcd4bl Nice fusion of C4.5 and
                 GP.",
}

@InProceedings{ekart:1999:EA,
  author =       "Aniko Ekart",
  title =        "Shorter Fitness Preserving Genetic Programs",
  booktitle =    "Artificial Evolution. 4th European Conference, AE'99,
                 Selected Papers",
  year =         "2000",
  editor =       "C. Fonlupt and J.-K. Hao and E. Lutton and E. Ronald
                 and M. Schoenauer",
  volume =       "1829",
  series =       "LNCS",
  pages =        "73--83",
  address =      "Dunkerque, France",
  month =        "3-5 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67846-8",
  notes =        "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67846-8

                 {"}Simplification is implemented in Prolog and consists
                 of approximately 250 clauses.{"} Fig 4. plots of
                 fitness (RMS error) times program size",
}

@InProceedings{ekart:2000:mGPfs,
  author =       "Aniko Ekart and S. Z. Nemeth",
  title =        "A metric for genetic programs and fitness sharing",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "259--270",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "In the paper a metric for genetic programs is
                 constructed. This metric reflects the structural
                 difference of the genetic programs. It is used then for
                 applying fitness sharing to genetic programs, in
                 analogy with fitness sharing applied to genetic
                 algorithms. The experimental results for several
                 parameter settings are discussed. We observe that by
                 applying fitness sharing the code growth of genetic
                 programs could be limited.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@Article{ekart:2001:genp,
  author =       "Aniko Ekart and S. Z. Nemeth",
  title =        "Selection Based on the Pareto Nondomination Criterion
                 for Controlling Code Growth in Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "1",
  pages =        "61--73",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, code growth,
                 selection scheme, multiobjective optimization",
  ISSN =         "1389-2576",
  abstract =     "The rapid growth of program code is an important
                 problem in genetic programming systems. In the present
                 paper we investigate a selection scheme based on
                 multiobjective optimization. Since we want to obtain
                 accurate and small solutions, we reformulate this
                 problem as multiobjective optimization. We show that
                 selection based on the Pareto nondomination criterion
                 reduces code growth and processing time without
                 significant loss of solution accuracy.",
}

@InProceedings{ekart:2001:ESI,
  author =       "Aniko Ekart and S. Z. Nemeth",
  title =        "Stability of Tree Based Decision Principles",
  booktitle =    "EURO Summer Institute (ESI) XIX, Decision Analysis and
                 Artificial Intellience",
  year =         "2001",
  address =      "Toulouse, France",
  month =        "9-22 " # sep,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{ekart:2002:EuroGP,
  title =        "Maintaining the Diversity of Genetic Programs",
  author =       "Anik\'o Ek\'art and Sandor Zoltan N\'emeth",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "162--171",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "An important problem of evolutionary algorithms is
                 that throughout evolution they loose genetic diversity.
                 Many techniques have been developed for maintaining
                 diversity in genetic algorithms, but few investigations
                 have been done for genetic programs. We define here a
                 diversity measure for genetic programs based on our
                 metric for genetic trees. We use this distance measure
                 for studying the effects of fitness sharing. We then
                 propose a method for adaptively maintaining the
                 diversity of a population during evolution.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{eklund:2002:ampgeiv,
  author =       "Sven E. Eklund",
  title =        "A Massively Parallel {GP} Engine in {VLSI}",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "629--633",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstrct =      "In this paper we propose the implementation of a
                 massively parallel GP model in hardware in order to
                 speed up the genetic algorithm. This fine-grained
                 diffusion architecture consists of a large amount of
                 independent processing nodes that evolve a large number
                 of small, overlapping subpopulations. Every node has an
                 embedded CPU that executes a linear machine code GP
                 representation at a rate of up to 20,000 generations
                 per second.",
}

@InProceedings{ekman:2001:ehs,
  author =       "Magnus Ekman and Peter Nordin",
  title =        "Evolvable Hardware using State-machines",
  booktitle =    "Graduate Student Workshop",
  year =         "2001",
  editor =       "Conor Ryan",
  pages =        "409--412",
  address =      "San Francisco, California, USA",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}

@InProceedings{el-beltagy:1999:MTFEOCEPPL,
  author =       "Mohammed A. El-Beltagy and Prasanth B. Nair and Andy
                 J. Keane",
  title =        "Metamodeling Techniques For Evolutionary Optimization
                 of Computationally Expensive Problems: Promises and
                 Limitations",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "196--203",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{eldershaw:1999:RMG,
  author =       "Craig Eldershaw and Stephen Cameron",
  title =        "Real-world applications: Motion planning using {GA}s",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1776",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{elhaggaz:1999:E,
  author =       "Salah Elhaggaz and Brian Turton and John Brown",
  title =        "Evolutionary algorithm for phased network topology
                 design",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "80--87",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@Article{ellis:2002:AEM,
  author =       "David I. Ellis and David Broadhurst and Douglas B.
                 Kell and Jem J. Rowland and Royston Goodacre",
  title =        "Rapid and Quantitative Detection of the Microbial
                 Spoilage of Meat by Fourier Transform Infrared
                 Spectroscopy and Machine Learning",
  journal =      "Applied and Environmental Microbiology",
  year =         "2002",
  volume =       "68",
  number =       "6",
  pages =        "2822--2828",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Fourier transform infrared (FT-IR) spectroscopy is a
                 rapid, noninvasive technique with considerable
                 potential for application in the food and related
                 industries. We show here that this technique can be
                 used directly on the surface of food to produce
                 biochemically interpretable ?fingerprints.? Spoilage in
                 meat is the result of decomposition and the formation
                 of metabolites caused by the growth and enzymatic
                 activity of microorganisms. FT-IR was exploited to
                 measure biochemical changes within the meat substrate,
                 enhancing and accelerating the detection of microbial
                 spoilage. Chicken breasts were purchased from a
                 national retailer, comminuted for 10 s, and left to
                 spoil at room temperature for 24 h. Every hour, FT-IR
                 measurements were taken directly from the meat surface
                 using attenuated total reflectance, and the total
                 viable counts were obtained by classical plating
                 methods. Quantitative interpretation of FT-IR spectra
                 was possible using partial least-squares regression and
                 allowed accurate estimates of bacterial loads to be
                 calculated directly from the meat surface in 60 s.
                 Genetic programming was used to derive rules showing
                 that at levels of 10 7 bacteria·g �1 the main
                 biochemical indicator of spoilage was the onset of
                 proteolysis. Thus, using FT-IR we were able to acquire
                 a metabolic snapshot and quantify, noninvasively, the
                 microbial loads of food samples accurately and rapidly
                 in 60 s, directly from the sample surface. We believe
                 this approach will aid in the Hazard Analysis Critical
                 Control Point process for the assessment of the
                 microbiological safety of food at the production,
                 processing, manufacturing, packaging, and storage
                 levels.",
  notes =        "DOI: 10.1128/AEM.68.6.2822?2828.2002 American Society
                 for Microbiology",
}

@InCollection{engel:1995:EESEAT,
  author =       "David Engel",
  title =        "Evolving Effective Solutions in Effective Amounts of
                 Time",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "76--85",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InCollection{engelhardt:1998:LBNDSUGP,
  author =       "Barbara Engelhardt",
  title =        "Learning a Bayesian Network from Data Samples Using
                 Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "1--10",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{erba:2001:EuroGP,
  author =       "Massimiliano Erba and Roberto Rossi and Valentino
                 Liberali and Andrea Tettamanzi",
  title =        "An Evolutionary Approach to Automatic Generation of
                 {VHDL} Code for Low-Power Digital Filters",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "36--50",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Evolvable
                 Hardware, Evolutionary Algorithms, Electronic Design,
                 Digital Filters, VHDL",
  ISBN =         "3-540-41899-7",
  size =         "15 pages",
  abstract =     "An evolutionary algorithm is used to design a finite
                 impulse response digital filter with reduced power
                 consumption. The proposed design approach combines
                 genetic optimization and simulation methodology, to
                 evaluate a multi-objective fitness function which
                 includes both the suitability of the filter transfer
                 function and the transition activity of digital blocks.
                 The proper choice of fitness function and selection
                 criteria allows the genetic algorithm to perform a
                 better search within the design space, thus exploring
                 possible solutions which are not considered in the
                 conventional structured design methodology. Although
                 the evolutionary process is not guaranteed to generate
                 a filter fully compliant to specifications in every
                 run, experimental evidence shows that, when
                 specifications are met, evolved filters are much better
                 than classical designs both in terms of power
                 consumption and in terms of area, while maintaining the
                 same performance.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{eriksson97,
  author =       "Roger Eriksson and Bj{\"{o}}rn Olsson",
  title =        "Cooperative Coevolution in Inventory Control
                 Optimisation",
  year =         "1997",
  booktitle =    "Proceedings of the Third International Conference on
                 Artificial Neural Networks and Genetic Algorithms",
  editor =       "G. D. Smith and N. C. Steele and R. F. Albrecht",
  publisher =    "Springer-Verlag",
  address =      "University of East Anglia, Norwich, UK",
  keywords =     "genetic algorithms",
  notes =        "ICANNGA97",
}

@InProceedings{erkan:2002:gecco:lbp,
  title =        "Controlled Genetic Programming Search for Solving
                 Deceptive Problems",
  author =       "Emin Erkan Korkmaz and G{\"o}kt{\"u}rk
                 {\"U}{\c{c}}oluk",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "295--300",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 Context free grammar induction. N-parity (N=5). Uses
                 global information (ie outside population).",
}

@InProceedings{Escazut:1997:ccscts,
  author =       "Cathy Escazut and Terence C. Fogarty",
  title =        "Coevolving Classifier Systems to Control Traffic
                 Signals",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{eskin:1999:Othello,
  author =       "E. Eskin and Eric V. Siegel",
  title =        "Genetic Programming Applied to Othello: Introducing
                 Students to Machine Learning Research",
  booktitle =    "30th Technical Symposium of the ACM Special Interest
                 Group in Computer Science Education",
  year =         "1999",
  address =      "New Orleans, LA, USA",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.columbia.edu/~evs/papers/sigcse-paper.ps",
  abstract =     "In this paper we describe and analyze a three week
                 assignment that was given in a Machine Learning course
                 at Columbia University. The assignment presented
                 students with an introduction to machine learning
                 research. The assignment required students to apply
                 Genetic Programming to evolve algorithms that play the
                 board game Othello. The students were provided with an
                 implemented experimental approach as a starting point.
                 The students were required to perform their own
                 experimental modifications corresponding to research
                 issues in machine learning. The results of student
                 experiments were good both in terms of research and in
                 terms of student learning. All relevant code,
                 documentation and information about GPOthello is
                 available at the following url:
                 http://www.cs.columbia.edu/~evs/ml/othello.html .",
}

@TechReport{esparcia:1995:95012,
  author =       "K. C. Sharman and A. I. Esparcia-Alcazar and Y. Li",
  title =        "Evolving Digital Signal Processing Algorithms by
                 Genetic Programming",
  institution =  "Faculty of Engineering",
  year =         "1995",
  type =         "Technical Report",
  number =       "CSC-95012",
  address =      "Glasgow G12 8QQ, Scotland",
  month =        "31 " # mar,
  keywords =     "genetic algorithms, genetic programming, simulated
                 annealing, digital signal processing, neural networks",
  URL =          "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs95012.html",
  abstract =     "We introduce a novel genetic programming (GP)
                 technique to evolve both the structure and parameters
                 of adaptive digital signal processing algorithms. This
                 is accomplished by defining a set of node functions and
                 terminals to implement the basic operations commonly
                 used in a large class of DSP algorithms. In addition,
                 we show how simulated annealing may be employed to
                 assist the GP in optimising the numerical parameters of
                 expression trees. The concepts are illustrated by using
                 GP to evolve high performance algorithms for detecting
                 binary data sequences at the output of a noisy,
                 non-linear communications channel.",
  notes =        "Also submitted to: Proc. First IEE/IEEE Int. Conf. on
                 GA in Eng. Syst.: Innovations and Appl., Sheffield,
                 Sept. 1995, pp.473-480.",
  size =         "pages",
}

@TechReport{esparcia:1996:96009,
  author =       "Anna I. Esparcia-Alcázar and Ken C. Sharman",
  title =        "Evolving Recurrent Neural Network Architectures by
                 Genetic Programming",
  institution =  "Faculty of Engineering",
  year =         "1996",
  type =         "Technical Report",
  number =       "CSC-96009",
  address =      "Glasgow G12 8QQ, Scotland",
  keywords =     "genetic algorithms, genetic programming, Recurrent
                 Neural Networks, Simulated annealing, Digital Signal
                 Processing",
  URL =          "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs96009.html",
  abstract =     "We propose a novel design paradigm for recurrent
                 neural networks. This employs a two-stage Genetic
                 Programming / Simulated Annealing hybrid algorithm to
                 produce a neural network which satisfies a set of
                 design constraints. The Genetic Programming part of the
                 algorithm is used to evolve the general topology of the
                 network, along with specifications for the neuronal
                 transfer functions, while the Simulated Annealing
                 component of the algorithm adapts the network's
                 connection weights in response to a set of training
                 data. Our approach offers important advantages over
                 existing methods for automated network design. Firstly,
                 it allows us to develop recurrent network connections;
                 secondly, we are able to employ neurons with arbitrary
                 transfer functions, and thirdly, the approach yields an
                 efficient and easy to implement on-line training
                 algorithm. The procedures involved in using the GP/SA
                 hybrid algorithm are illustrated by using it to design
                 a neural network for adaptive filtering in a signal
                 processing application.",
  size =         "pages",
}

@TechReport{esparcia:1996:96010,
  author =       "Anna I. Esparcia-Alcázar and Ken C. Sharman",
  title =        "Application of Genetic Programming to Signal
                 Processing Problems",
  institution =  "Faculty of Engineering",
  year =         "1996",
  type =         "Technical Report",
  number =       "CSC-96010",
  address =      "Glasgow G12 8QQ, Scotland",
  keywords =     "genetic algorithms, genetic programming, Digital
                 Signal Processing Simulated Annealing, Adaptive
                 Filtering",
  URL =          "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs96010.html",
  abstract =     "The field of Digital Signal Processing (DSP) is
                 concerned with the restoration of signals which have
                 undergone distortion and interference or noise
                 corruption as a result of being transmitted. The usual
                 way to recover such a signal is by adaptive filtering.
                 Designing adaptive filters is not an easy task. It
                 usually involves complicated algorithms whose
                 performance depends on the skill of the designer as
                 well as the power of the computer used. The aim of the
                 present work is to provide a way of automating such
                 process by means of a black box technique. In this
                 approach, both the structure and the parameters of
                 adaptive filters are evolved. The former is done by
                 Genetic Programming (GP) and the latter is done by
                 Simulated Annealing (SA). The power of the hybrid GP/SA
                 is demonstrated with some results on three interesting
                 DSP applications: channel equalisation, noise
                 cancellation and interference removal.",
  notes =        "Also submitted to: Late-breaking papers at the Genetic
                 Programming 96 Conference, Stanford, USA, July 1996
                 esparcia:1996:GPdsp",
  size =         "pages",
}

@InProceedings{esparcia:1996:GPdsp,
  author =       "Anna I. {Esparcia Alcazar} and Ken C. Sharman",
  title =        "Some Applications of Genetic Programming in Digital
                 Signal Processing",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "24--31",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming, DSP",
  URL =          "http://www.iti.upv.es/~anna/papers/someappsgp96.ps",
  notes =        "GP-96LB, recursive, memory The email address for the
                 bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670

                 accepted for GP'96 but, due to a number of
                 circumstances, never appeared in the proceedings. It
                 was presented at the conference

                 See esparcia:1996:96010",
}

@InProceedings{esparcia:1996:GPerNNasp,
  author =       "Anna I. Esparcia-Alcazar and Kenneth C. Sharman",
  title =        "Genetic Programming Techniques that Evolve Recurrent
                 Neural Networks Architectures for Signal Processing",
  booktitle =    "IEEE Workshop on Neural Networks for Signal
                 Processing",
  year =         "1996",
  month =        sep,
  keywords =     "Genetic Programming, Genetic Algorithms",
  address =      "Seiko, Kyoto, Japan",
  size =         "9 pages",
}

@InProceedings{esparcia:1997:GPdsp,
  author =       "Anna I. Esparcia-Alcazar and Ken Sharman",
  title =        "Evolving Recurrent Neural Network Architectures by
                 Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "89--94",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www.iti.upv.es/~anna/papers/gp-rnn97.ps",
  notes =        "GP-97",
}

@InProceedings{Esparcia-Alcazar:1997:lsGP,
  author =       "Anna I. Esparcia-Alcazar and Ken Sharman",
  title =        "Learning Schemes for Genetic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "57--65",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  URL =          "http://www.iti.upv.es/~anna/papers/learningGP97.ps",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{Esparcia-Alcazar:1997:iGPtasp,
  author =       "Anna I Esparcia-Alcazar",
  title =        "An investigation into a Genetic Programming Technique
                 for Adaptive Signal Processing",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "290",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@PhdThesis{Esparcia-Alcazar:1998:thesis,
  author =       "Anna I. Esparcia-Alcazar",
  title =        "Genetic Programming for Adaptive Signal Processing",
  school =       "Electronics and Electrical Engineering, University of
                 Glasgow",
  year =         "1998",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/esparcia-alcazar/thesis.ps.gz",
  URL =          "http://www.iti.upv.es/~anna/papers/Thesis.zip",
  size =         "142 pages",
  abstract =     "This thesis is devoted to presenting the application
                 of the Genetic Programming (GP) paradigm to a class of
                 Digital Signal Processing (DSP) problems. Its main
                 contributions are

                 a new methodology for representing Discrete-Time
                 Dynamic Systems (DDS) as expression trees. The
                 objective is the state space specification of DDSs: the
                 behaviour of a system for a time instant t_0 is
                 completely accounted for given the inputs to the system
                 and also a set of quantities which specify the state of
                 the system. This means that the proposed method must
                 incorporate a form of memory that will handle this
                 information.

                 For this purpose a number of node types and associated
                 data structures are defined. These will allow for the
                 implementation of local and time recursion and also
                 other specific functions, such as the sigmoid commonly
                 encountered in neural networks. An example is given by
                 representing a recurrent NN as an expression tree.

                 a new approach to the channel equalisation problem. A
                 survey of existing methods for channel equalisation
                 reveals that the main shortcoming of these techniques
                 is that they rely on the assumption of a particular
                 structure or model for the system addressed. This
                 implies that knowledge about the system is available;
                 otherwise the solution obtained will have a poor
                 performance because it was not well matched to the
                 problem.

                 This gives a main motivation for applying GP to channel
                 equalisation, which is done in this work for the first
                 time. Firstly, to provide a unified technique for a
                 wide class of problems, including those which are
                 poorly understood; and secondly, to find alternative
                 solutions to those problems which have been
                 successfully addressed by existing techniques.

                 In particular, in the equalisation of nonlinear
                 channels, which have been mainly addressed with Neural
                 Networks and various adaptation algorithms, the
                 proposed GP approach presents itself as an interesting
                 alternative.",
  abstract =     "a new way of handling numerical parameters in GP, node
                 gains. A node gain is a numerical parameter associated
                 to a node that multiplies its output value. This
                 concept was introduced by Sharman and Esparcia-Alcázar
                 (1993) and is fully developed here.

                 The motivation for a parameterised GP is addressed,
                 together with an overview of how it has been addressed
                 by other authors. The drawbacks of these methods are
                 highlighted: there is no established way of determining
                 the number of parameters to use and their placement;
                 further, unused parameters can be unnecessarily adapted
                 while, on the other hand, useful ones might be
                 eliminated. The way in which node gains overcome these
                 problems is explained. An extra advantage is the
                 possibility of expressing complex systems in a compact
                 way, which is labelled {"}compacting effect{"} of node
                 gains.

                 The costs of node gains are also pointed out: increase
                 in the degrees of freedom and increased complexity.
                 This, in theory, results in an increase of
                 computational expense, due to the handling of more
                 complex nodes and to the fact that an extra
                 multiplication is needed per node. These costs,
                 however, are expected to be of, at most, the same order
                 of magnitude as those of the alternatives.

                 Experimental analysis shows that random node gains may
                 not be able to achieve all the potential benefits
                 expected. It is conjectured that optimisation of the
                 values is needed in order to attain the full benefits
                 of node gains, which brings along the next
                 contribution.

                 a mathematical model is given for an adaptive GP. As
                 concluded from the previous point, adaptation of the
                 values of the node gains is needed in order to take
                 full advantage of them. A Simulated Annealing (SA)
                 algorithm is introduced as the adaptation algorithm.
                 This is put in the context of an analogy: the
                 adaptation of the gains by SA is equivalent to the
                 learning process of an individual during its
                 lifetime.

                 This analogy gives way to the introduction of two
                 learning schemes, labelled Lamarckian and Darwinian,
                 which refer to the possibility of inheriting the
                 learned gains.

                 The Darwinian and Lamarckian learning schemes for GP
                 are compared to the standard GP technique and also to
                 GP with random node gains. Statistical analysis, done
                 for both fixed and time-varying environments, shows the
                 superiority of both learning methods over the
                 non-learning ones, although it is not possible at this
                 stage to determine which of the two provides a better
                 performance.

                 a number of interesting results in the channel
                 equalisation problem. These are compared to those
                 obtained by other techniques and it is concluded that
                 the proposed method obtains better or similar
                 performance when extreme values (maximum fitness or
                 minimum error) are considered.",
}

@InProceedings{esparcia­alcazar:1999:ppGPcdlis,
  author =       "Anna Esparcia-Alcazar and Ken Sharman",
  title =        "Phenotype Plasticity in Genetic Programming: {A}
                 Comparison of {Darwinian} and {Lamarckian} Inheritance
                 Schemes",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "49--64",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  URL =          "http://www.iti.upv.es/~anna/papers/eurogp99.ps",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Combination of GP and Simulated Annealing. Performs
                 experiments were SA produced changes (ie new constants)
                 are incorporated into genes (Lamarckian inheritance,
                 also known as {"}repair{"} in GA circles) compared to
                 not writing back. SA+GP claimed to be good (often).",
}

@InProceedings{esparcia­alcazar:1999:GPce,
  author =       "Anna Esparcia-Alcazar and Ken Sharman",
  title =        "Genetic Programming for Channel Equalisation",
  booktitle =    "Evolutionary Image Analysis, Signal Processing and
                 Telecommunications: First European Workshop, EvoIASP'99
                 and EuroEcTel'99",
  year =         "1999",
  editor =       "Riccardo Poli and Hans-Michael Voigt and Stefano
                 Cagnoni and Dave Corne and George D. Smith and Terence
                 C. Fogarty",
  volume =       "1596",
  series =       "LNCS",
  pages =        "126--137",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "28-29 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65837-8",
  URL =          "http://www.iti.upv.es/~anna/papers/evoiasp99.ps",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-65837-8",
  abstract =     "This paper is devoted to providing a comparison
                 between classical and neural channel equalisation
                 techniques and node gain Genetic Programming enhanced
                 with Simulated Annealing (or GP+SA). Firstly, the
                 shortcomings of existing techniques are exposed and the
                 main requirements to demand of a new method enumerated.
                 A description of the problem is followed by an account
                 of particular cases of equalisation, exemplified by
                 three channels, both linear and nonlinear. Results are
                 obtained for these channels both with the proposed
                 method and a classical technique, the Recursive Least
                 Squares (RLS) algorithm, and they are further compared
                 to those existing in the literature. The comparison
                 shows the great potential of GP+SA, especially in the
                 case of nonlinear channels. The main disadvantage of
                 the proposed method, the computational effort involved,
                 is also pointed out and it is concluded that, upon the
                 whole, the method deserves further investigation.",
  notes =        "EvoIASP99'99",
}

@InProceedings{essam:2001:acpfgp,
  author =       "Daryl Essam and R. I. Bob McKay",
  title =        "Adaptive Control of Partial Functions in Genetic
                 Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "895--901",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Partial
                 Functions, Fitness Evaluation",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 Convergence of populations of partial functions.
                 recursion list membership, 6-multiplexor, 11-mux.
                 undef, DCTG-GP cf. ross:1999:LGPDCTG fitness sharing
                 mitigated by non-undef",
}

@Article{essam:2002:GPEM,
  author =       "Daryl Essam",
  title =        "Book Review: Blondie24: Playing at the Edge of {AI}",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "4",
  pages =        "389--390",
  month =        dec,
  ISSN =         "1389-2576",
  notes =        "Article ID: 5103876",
}

@Article{Evans:2001:CEP,
  author =       "C. Evans and P. J. Fleming and D. C. Hill and J. P.
                 Norton and I. Pratt and D. Rees and K.
                 Rodriguez-Vazquez",
  title =        "Application of system identification techniques to
                 aircraft gas turbine engines",
  journal =      "Control Engineering Practice",
  volume =       "9",
  pages =        "135--148",
  year =         "2001",
  number =       "2",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V2H-4280YP2-3/1/24d44180070f91dea854032d98f9187a",
  abstract =     "A variety of system identification techniques are
                 applied to the modelling of aircraft gas turbine
                 dynamics. The motivation behind the study is to improve
                 the efficiency and cost-effectiveness of system
                 identification techniques currently used in the
                 industry. Three system identification approaches are
                 outlined in this paper. They are based upon: multisine
                 testing and frequency-domain identification,
                 time-varying models estimated using extended least
                 squares with optimal smoothing, and multiobjective
                 genetic programming to select model structure.",
}

@InProceedings{evett:1987:rifs,
  author =       "Ian W. Evett and E. J. Spiehler",
  title =        "Rule Induction in Forensic Science",
  booktitle =    "KBS in Goverment",
  year =         "1987",
  pages =        "107--118",
  publisher_address = "Pinner, UK",
  publisher =    "Online Publications",
  keywords =     "genetic algorithms, genetic programming, BEAGLE",
  notes =        "British Library shelfmark 5088.238300

                 BEAGLE trial by UK Home Office forensic scientist to
                 give binary and three way classification of class
                 samples based on its refractive index and its
                 composition (8 elements obtianed by scanning electron
                 microscope). In blind trial (ten cases) {"}The results
                 reported from BEAGLE rules gave the lowest error
                 rate.{"} [page 116] when compared to two standard
                 techniques, neighest 3 neighbours (cartestian distance)
                 and Statistical Package for the Social Sciences
                 (SPSS).",
}

@InProceedings{evett:1999:MG,
  author =       "Matthew Evett and Taghi Khoshgoftaar and Pei-der Chien
                 and Edward Allen",
  title =        "Modelling software quality with {GP}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1232",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{Evett97-agps-tr,
  author =       "M. Evett and T. Fernandez",
  title =        "A Distributed System for Genetic Programming that
                 Dynamically Allocates Processors",
  institution =  "Dept. Computer Science and Engineering, Florida
                 Atlantic Univ ersity",
  year =         "1997",
  address =      "Boca Raton, FL, USA",
  annote =       "AGPS",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "parallel GP system, AGPS, is based on MPI, not PVM",
}

@InProceedings{evett:1998:GPsqp,
  author =       "Matthew Evett and Taghi Khoshgoftar and Pei-der Chien
                 and Edward Allen",
  title =        "{GP}-based software quality prediction",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "60--65",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{evett:1998:nmidncGP,
  author =       "Matthew Evett and Thomas Fernandez",
  title =        "Numeric Mutation Improves the Discovery of Numeric
                 Constants in Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "66--71",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{evonews:1999:mole,
  key =          "evonews",
  title =        "{MOLE} at City University",
  journal =      "EvoNEWS",
  year =         "1999",
  volume =       "11",
  pages =        "2--3",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming",
  size =         "http://www.dcs.napier.ac.uk/evonet/Coordinator/evonews/evonews11.pdf
                 ?",
  abstract =     "Profile of research group. Introns Peter Smith
                 application of GP to MRI brain tumors+Principal
                 Component Analysis, NMR Helen Gray and Peter W. H.
                 Smith (NMR in Biomedicine, 11)",
}

@Article{evonews:1999:art,
  key =          "evonews",
  title =        "Evol-artists - a new breed entirely",
  journal =      "EvoNEWS",
  year =         "1999",
  volume =       "11",
  pages =        "7--10",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming",
  size =         "http://www.dcs.napier.ac.uk/evonet/Coordinator/evonews/evonews11.pdf
                 ?",
  notes =        "Steven Rooke. Richard Dawkins Biomorphs. Jeffrey
                 Ventrella. Mattias Fagerlund
                 http://www.acacia.se/Mattias/WebGP/ Ken Musgrave. Karl
                 Sims. Dr. Mutatis an evolutionary art tool.

                 Jano I. van Hemert --- Pieter Mondriaan. Pensousal
                 Machado -- NEvAr",
}

@InProceedings{fabrega:1999:GANNCSG,
  author =       "Francesc Xavier Llora i Fabrega and Josep Maria
                 Garrell i Guiu",
  title =        "{GENIFER}: {A} Nearest Neighbour based Classifier
                 System using {GA}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "797",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{faglia:1996:mpdCAMGA,
  author =       "Rodolfo Faglia and David Vetturi",
  title =        "Motion Planning and Design of {CAM} Mechanisms by
                 Means of a Genetic Algorithm",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "479--484",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 GA paper",
}

@InProceedings{falco:2002:usprfmibmoagpa,
  author =       "Ivanoe {De Falco} and Antonio Della Cioppa and Ernesto
                 Tarantino",
  title =        "Unsupervised Spectral Pattern Recognition for
                 Multispectral Images by means of a Genetic Programming
                 approach",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "231--236",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "An innovative approach to spectral pattern recognition
                 for multispectral images based on Genetic Programming
                 is introduced. The problem is faced in terms of
                 unsupervised pixel classification. The system is tested
                 on a multispectral image with 31 spectral bands and 256
                 by 256 pixels. A good quality clustered output image is
                 obtained.",
}

@InCollection{fan:1998:DADDCGP,
  author =       "John L. Fan",
  title =        "Design of an Adaptive Detector for Digital
                 Communications using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "11--19",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{WeigueFan:1999:agmfGPeir,
  author =       "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
  title =        "Automatic generation of matching functions by genetic
                 programming for effective information retrieval",
  booktitle =    "Proceedings of the 1999 Americas Conference on
                 Information Systems",
  year =         "1999",
  editor =       "W. David Haseman and Derek L. Nazareth",
  pages =        "49--51",
  address =      "Milwaukee, WI, USA",
  month =        "13-15 " # aug,
  organisation = "Association for Information Systems",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-personal.umich.edu/~wfan/paper/Amcis_final.pdf",
  size =         "3 pages",
  abstract =     "With the advent of the Internet, online resources are
                 increasingly available. Many users choose popular
                 search engines to perform an online search to satisfy
                 their information need. However, these search engines
                 tend to turn up many non-relevant documents, which make
                 their retrieval precision very low. How to find
                 appropriate ranking metrics to retrieve more relevant
                 documents and fewer non-relevant documents for users
                 remains a big challenge to the information retrieval
                 community. In this paper, we propose a new framework
                 that combines the merits of genetic programming and
                 relevance feedback techniques to automatically generate
                 and refine the matching functions used for document
                 ranking. This approach overcomes the shortcoming of
                 traditional ranking algorithms using a fixed ranking
                 strategy. It also gives some new ideas and hints for
                 information retrieval professionals.",
  notes =        "AMCIS99
                 https://commerce.mindspring.com/www.icisnet.org/proc.html

                 Prototype implemented in C. Fitness based on user
                 feedback",
}

@InProceedings{WeiguoFan:2000:icis,
  author =       "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
  title =        "Personalization of Search Engine Services for
                 Effective Retrieval and Knowledge Management",
  booktitle =    "The Proceedings of the International Conference on
                 Information Systems 2000",
  year =         "2000",
  pages =        "20--34",
  email =        "wfan@umich.edu",
  keywords =     "genetic algorithms, genetic programming, information
                 retrieval",
  URL =          "http://www-personal.umich.edu/~wfan/paper/icis_final.pdf",
  abstract =     "The Internet and corporate intranets provide far more
                 information than anybody can absorb. People use search
                 engines to find the information they require. However,
                 these systems tend to use only one fixed term weighting
                 strategy regardless of the context to which it applies,
                 posing serious performance problems when
                 characteristics of different users, queries, and text
                 collections are taken into consideration. In this
                 paper, we argue that the term weighting strategy should
                 be context specific, that is, different term weighting
                 strategies should be applied to different contexts, and
                 we propose a new systematic approach that can
                 automatically generate term weighting strategies for
                 different contexts based on genetic programming (GP).
                 The new proposed framework was tested on TREC data and
                 the results are very promising.",
}

@InProceedings{Fan:1999:AMCIS,
  author =       "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
  title =        "Automatic generation of matching functions by genetic
                 programming for effective information retrieval",
  booktitle =    "Proceedings of the 1999 Americas Conference on
                 Information Systems",
  year =         "1999",
  editor =       "W. David Haseman and Derek L. Nazareth",
  pages =        "49--51",
  organization = "Association for Information Systems",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://filebox.vt.edu/users/wfan/Amcis_final.pdf",
  abstract =     "With the advent of the Internet, online resources are
                 increasingly available. Many users choose popular
                 search engines to perform an online search to satisfy
                 their information need. However, these search engines
                 tend to turn up many non-relevant documents, which make
                 their retrieval precision very low. How to find
                 appropriate ranking metrics to retrieve more relevant
                 documents and fewer non-relevant documents for users
                 remains a big challenge to the information retrieval
                 community. In this paper, we propose a new framework
                 that combines the merits of genetic programming and
                 relevance feedback techniques to automatically generate
                 and refine the matching functions used for document
                 ranking. This approach overcomes the shortcoming of
                 traditional ranking algorithms using a fixed ranking
                 strategy. It also gives some new ideas and hints for
                 information retrieval professionals.",
}

@InProceedings{fan:2001:bgrgaafd,
  author =       "Zhun Fan and Jianjun Hu and Kisung Seo and Erik D.
                 Goodman and Ronald C. Rosenberg and Baihai Zhang",
  title =        "Bond Graph Representation and {GP} for Automated
                 Analog Filter Design",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "81--86",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, STGP",
  notes =        "GECCO-2001LB, lilgp",
}

@InProceedings{fan:2002:gecco,
  author =       "Zhun Fan and Kisung Seo and Ronald C. Rosenberg and
                 Jianjun Hu and Erik D. Goodman",
  title =        "Exploring Multiple Design Topologies Using Genetic
                 Programming And Bond Graphs",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1073--1080",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, bond graphs, design automation,
                 mechatronic system, topology",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@TechReport{farringdon:1996:in05,
  author =       "J Farringdon",
  title =        "Random Effects in Genetic Algorithms and Programming
                 (\& Other Genetic Algorithm Issues)",
  institution =  "University College London",
  year =         "1996",
  type =         "Internal Note",
  number =       "IN/96/05",
  address =      "Computer Science, Gower Street, London WC1E 6BT, UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/j.farringdon/GP/in-1996-05.html",
  abstract =     "Phenomena known to mathematicians and psychologists
                 seem to be as yet unexploited by genetic algorithms and
                 genetic programming techniques. A number of genetic
                 techniques are briefly considered here from a maths and
                 psychology perspective, the most immediately applicable
                 of which is the use of statistical distributions. The
                 statistical distributions technique may be implemented
                 by a programmer and produce returns for a user within
                 an hour.",
}

@InProceedings{federman:1998:clps,
  author =       "Francine Federman and Susan Fife Dorchak",
  title =        "A Study of Classifier Length and Population Size",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "629--634",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{federman:1999:RMLCSUBC,
  author =       "Francine Federman and Gayle Sparkman and Stephanie
                 Watt",
  title =        "Representation of Music in a Learning Classifier
                 System Utilizing Bach Chorales",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "785",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{fehr:1994:semo,
  author =       "Garry Fehr",
  title =        "Spontaneous Emergence of Multicellular Organisms From
                 Unicellular Ancestors",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "28--34",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@TechReport{feldt:1998:eGPmsv,
  author =       "Robert Feldt",
  title =        "An experiment on using genetic programming to develop
                 multiple diverse software variants",
  institution =  "Department of Computer Engineering, Chalmers
                 University of Technology",
  year =         "1998",
  type =         "Technical Report",
  number =       "98-13",
  address =      "Gothenburg, Sweden",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Included also in feldt:1998:midthesis",
  size =         "pages",
}

@TechReport{feldt:1998:scdGPsft,
  author =       "Robert Feldt",
  title =        "A survey of the concept of diversity in genetic
                 programming and software fault tolerance",
  institution =  "Department of Computer Engineering, Chalmers
                 University of Technology",
  year =         "1998",
  type =         "Technical Report",
  number =       "98-15",
  address =      "Gothenburg, Sweden",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Included also in feldt:1998:midthesis",
  size =         "pages",
}

@InProceedings{feldt:1998:gmdsvGP,
  author =       "Robert Feldt",
  title =        "Generating Multiple Diverse Software Versions with
                 Genetic Programming",
  booktitle =    "Proceedings of the 24th EUROMICRO Conference, Workshop
                 on Dependable Computing Systems",
  year =         "1998",
  pages =        "387--396",
  address =      "Vaesteraas, Sweden",
  month =        "25-27th " # aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.amp.york.ac.uk/external/sweden/sweden.htm",
  notes =        "described in feldt:1998:midthesis",
}

@Article{feldt:1998:gdsvGPes,
  author =       "Robert Feldt",
  title =        "Generating Diverse Software Versions with Genetic
                 Programming: an Experimental Study",
  journal =      "IEE Proceedings - Software Engineering",
  year =         "1998",
  volume =       "145",
  number =       "6",
  pages =        "228--236",
  month =        dec,
  note =         "Special issue on Dependable Computing Systems",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.iee.org.uk/publish/journals/profjrnl/cntnsen.html#SENDecember1998",
  notes =        "described in feldt:1998:midthesis",
}

@TechReport{feldt:1998:midthesis,
  author =       "Robert Feldt",
  title =        "Using Genetic Programming to Systematically Force
                 Software Diversity",
  institution =  "Department of Computer Engineering, Chalmers
                 University of Technology",
  year =         "1998",
  type =         "Technical Report",
  number =       "296L",
  address =      "Goteborg, Sweden",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "91-7197-740-6",
  notes =        "licentiate of Engineering thesis",
  size =         "133 pages",
}

@InProceedings{feldt:1999:GPxtxsdp,
  author =       "Robert Feldt",
  title =        "Genetic Programming as an Explorative Tool in Early
                 Software Development Phases",
  booktitle =    "Proceedings of the 1st International Workshop on Soft
                 Computing Applied to Software Engineering",
  year =         "1999",
  editor =       "Conor Ryan and Jim Buckley",
  pages =        "11--20",
  address =      "University of Limerick, Ireland",
  month =        "12-14 " # apr,
  organisation = "SCARE",
  publisher =    "Limerick University Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-874653-52-6",
  notes =        "http://scare.csis.ul.ie/scase99/ SCASE'99 USAF
                 aircraft arresting system (landing on carriers) used as
                 example. Java GPsys.",
}

@InProceedings{feldt:2000:feeeGP,
  author =       "Robert Feldt and Peter Nordin",
  title =        "Using Factorial Experiments to Evaluate the Effect of
                 Genetic Programming Parameters",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "271--282",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "Statistical techniques for designing and analyzing
                 experiments are used to evaluate the individual and
                 combined effects of genetic programming parameters.
                 Three binary classification problems are investigated
                 in a total of seven experiments consisting of 1108 runs
                 of a machine code genetic programming system. The
                 parameters having the largest effect in these
                 experiments are the population size and the number of
                 generations. A large number of parameters have
                 negligible effects. The experiments indicate that the
                 investigated genetic programming system is robust to
                 parameter variations, with the exception of a few
                 important parameters.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{feldt:2000:gp-beagle,
  author =       "Robert Feldt and Michael O'Neill and Conor Ryan and
                 Peter Nordin and William B. Langdon",
  title =        "{GP-Beagle:} {A} Benchmarking Problem Repository for
                 the Genetic Programming Community",
  pages =        "90--97",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ce.chalmers.se/~feldt/gpbeagle/faq_and_info/gpbeagle_papers/gecco2000lb/feldt_et_al_gecco2000lb_gpbeagle.ps",
  notes =        "Part of whitley:2000:GECCOlb",
}

@InProceedings{fernandes:1999:EAST,
  author =       "Carlos Fernandes and Joao Paulo Caldeira and Fernando
                 Melicio and Agostinho Rosa",
  title =        "Evolutionary Algorithm for School Timetabling",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1777",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{FSTG99,
  author =       "F. Fernandez and J. M. Sanchez and M. Tomassini and J.
                 A. Gomez",
  title =        "A Parallel Genetic Programming Tool based on {PVM}",
  booktitle =    "Recent Advances in Parallel Virtual Machine and
                 Message Passing Interface, Proceedings of the 6th
                 European PVM/MPI Users' Group Meeting",
  series =       "Lecture Notes in Computer Science",
  editor =       "J. Dongarra and E. Luque and T. Margalef",
  volume =       "1697",
  pages =        "241--248",
  publisher =    "Springer-Verlag",
  ISBN =         "3-540-66549-8",
  year =         "1999",
  month =        sep,
  address =      "Barcelona, Spain",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{FTVB00,
  author =       "F. Fernandez and M. Tomassini and L. Vanneschi and L.
                 Bucher",
  title =        "A Distributed Computing Environment for Genetic
                 Programming using {MPI}",
  editor =       "J. J. Dongarra and Peter Kacsuk and Norbert
                 Podhorszki",
  booktitle =    "Recent advances in parallel virtual machine and
                 message passing interface: 7th European {PVM\slash MPI}
                 Users' Group Meeting",
  volume =       "1908",
  publisher =    "Springer-Verlag",
  address =      "Balatonfured, Hungary",
  pages =        "322--329",
  year =         "2000",
  ISBN =         "3-540-41010-4 (softcover)",
  ISSN =         "0302-9743",
  bibdate =      "Mon Oct 16 18:31:56 MDT 2000",
  series =       "Lecture Notes in Computer Science",
  month =        "10-13 " # sep,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{fernandez:1996:wrGPmsrp,
  author =       "Jaime J. Fernandez and Kristin A. Farry and John B.
                 Cheatham",
  title =        "Waveform Recognition Using Genetic Programming: The
                 Myoelectric Signal Recognition Problem",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "63--71",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://hobbes.jsc.nasa.gov/pub/jjf/gp96.gz",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{Fernandez:1997:tpsets,
  author =       "Thomas Fernandez and Matthew Evett",
  title =        "Training Period Size and Evolved Trading Systems",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "95",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{fernandez:1998:nmisrGP,
  author =       "Thomas Fernandez and Matthew Evett",
  title =        "Numeric Mutation as an Improvement to Symbolic
                 Regression in Genetic Programming",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "251--260",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 Florida Atlantic University, Boca Raton, FL",
}

@InProceedings{fernandez:1999:ABIFFRG,
  author =       "J. Jaime Fernandez Jr. and Ian D. Walker",
  title =        "A Biologically Inspired Fitness Function for Robotic
                 Grasping",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1517--1522",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{fernandez:1999:SAEP,
  author =       "Francisco Fernandez and Marco Tomassini and J. M.
                 Sanchez",
  title =        "Solving the Ant and the Even Parity-5 problems by
                 means of parallel genetic programming",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "88--92",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, Genetic Programming",
  notes =        "GECCO-99LB",
}

@InProceedings{fernandez:2000:esmpGP,
  author =       "F. Fernandez and M. Tomassini and W. F. {Punch III}
                 and J. M. Sanchez",
  title =        "Experimental Study of Multipopulation Parallel Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "283--293",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "The parallel execution of several populations in
                 evolutionary algorithms has usually given good results.
                 Nevertheless, researchers have to date drawn
                 conflicting conclusions when using some of the parallel
                 genetic programming models. One aspect of the conflict
                 is population size, since published GP works do not
                 agree about whether to use large or small populations.
                 This paper presents an experimental study of a number
                 of common GP test problems. Via our experiments, we
                 discovered that an optimal range of values exists. This
                 assists us in our choice of population size and in the
                 selection of an appropriate parallel genetic
                 programming model. Finding efficient parameters helps
                 us to speed up our search for solutions. At the same
                 time, it allows us to locate features that are common
                 to parallel genetic programming and the classic genetic
                 programming technique.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{fernandez:2000:GA,
  author =       "Francisco Fernandez and Marco Tomassini",
  title =        "Genetic programming and reconfigurable hardware: {A}
                 proposal for solving the problem of placement and
                 routing",
  booktitle =    "Graduate Student Workshop",
  year =         "2000",
  editor =       "Conor Ryan and Una-May O'Reilly and William B.
                 Langdon",
  pages =        "265--268",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{Fernandez:2000:GECCO,
  author =       "Francisco Fernandez and Marco Tomassini and William
                 Punch and J. M. Sanchez",
  title =        "Experimental Study of Isolated Multipopulation Genetic
                 Programming",
  pages =        "536",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{fernandez:2000:esimgp,
  author =       "F. Fernandez and M. Tomassini and J. M. Sanchez",
  title =        "Experimental Study of Isolated Multipopulation Genetic
                 Programming",
  booktitle =    "Proceedings of the 26th Annual Conference of the IEEE
                 Industrial Electronics Society",
  volume =       "1697",
  pages =        "2672--2677",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-6456-2",
  year =         "2000",
  month =        oct,
  address =      "Nagoya, Japan",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://fp.ieeexplore.ieee.org/iel5/7662/20956/00972420.pdf?isNumber=20956&prod=CNF&arnumber=00972420",
  abstract =     "In this paper we present results obtained when
                 comparing classic genetic programming (GP) with the
                 isolated multipopulation version. Our first discovery
                 was that sometimes, given a certain number of
                 individuals, it is useful to distribute them among
                 several populations even when no communication is
                 allowed. This consequently lead to research
                 concentrating on three main questions: firstly, how to
                 distribute individuals according to the problem in
                 hand; secondly, how many populations must be employed
                 in proportion to the effort and fitness involved when
                 solving a problem; and finally, how to use isolated
                 multipopulation GP in the classification of problems.",
}

@InProceedings{fernandez:2001:EuroGP,
  author =       "Francisco Fernandez and Marco Tomassini and Leonardo
                 Vanneschi",
  title =        "Studying the Influence of Communication Topology and
                 Migration on Distributed Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "51--63",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Distributed
                 Genetic Programming, Parallelism, Multipopulation
                 structures, Parallel evolutionary algorithms",
  ISBN =         "3-540-41899-7",
  size =         "13 pages",
  abstract =     "In this paper we present a systematic experimental
                 study of some of the parameters influencing parallel
                 and distributed genetic programming (PADGP) by using
                 three benchmark problems. We first present results on
                 the system's communication topology and then we study
                 the parameters governing individual migration between
                 subpopulations: the number of individuals sent and the
                 frequency of exchange. Our results suggest that fitness
                 evolution is more sensitive to the migration factor
                 than the communication topology.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{fernandez:2001:soprvpmrp,
  author =       "F. Fernandez and M. Tomassini",
  title =        "Studying the Optimal Parameter Range of Values in
                 {PADGP} by Means of Real-life Problems",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "436--441",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Parallel
                 Genetic Programming, FPGA",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@InProceedings{fernandez:2001:gecco,
  title =        "A new methodology for the Placement and Routing
                 problem based on {PADGP}",
  author =       "F. Fernandez and J. M. Sanchez and M. Tomassini",
  pages =        "175",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 Parallel Evolutionary Algorithms, Evolvable Hardware",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{fernandez:2001:,
  author =       "F. Fernandez and J. M. Sanchez and M. Tomassini",
  title =        "Placing and Routing Circuits on {FPGA}s by Means of
                 Parallel and Distributed Genetic Programming",
  booktitle =    "Evolvable Systems: From Biology to Hardware,
                 Proceedings of the 4th International Conference, ICES
                 2001",
  series =       "Lecture Notes in Computer Science",
  editor =       "Y. Liu and K. Tanaka and M. Iwata and T. Higuchi and
                 M. Yasunaga",
  volume =       "2210",
  pages =        "204--214",
  publisher =    "Springer-Verlag",
  ISBN =         "3-540-42671-X",
  ISSN =         "0302-9743",
  year =         "2001",
  month =        oct,
  address =      "Tokyo, Japan",
  keywords =     "evolvable hardware, genetic programming",
}

@InProceedings{fernandez:2002:EuroGP,
  title =        "Comparing Synchronous and Asynchronous Parallel and
                 Distributed {GP} Models",
  author =       "Francisco Fernandez and G. Galeano and J. A. Gomez",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "326--335",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "In this paper we present a study that analyses the
                 respective advantages and disadvantages of the
                 synchronous and asynchronous versions of island-based
                 genetic programming. We also look at different
                 measuring systems for comparing parallel genetic
                 programming with panmitic model. At the same time we
                 show an interesting relationship between the bloat
                 phenomenon and the number of individuals we use.
                 Finally, we study a relationship between the number of
                 subpopulations in parallel GP and the advantages of the
                 asynchronous model.",
  notes =        "EuroGP'2002, part of lutton:2002:GP.

                 Santa Fe Ant, even-5-parity. padgp",
}

@Unpublished{Ferreira:2000:GEP,
  author =       "Candida Ferreira",
  title =        "Gene Expression Programming: a New Adaptive Algorithm
                 for Solving Problems",
  note =         "rejected for publication",
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.gene-expression-programming.com/webpapers/GEP.pdf",
  abstract =     "Gene expression programming, a genome/phenome genetic
                 algorithm (linear and non-linear), is presented here
                 for the first time as a new technique for creation of
                 computer programs. Gene expression programming uses
                 character linear chromosomes composed of genes
                 structurally organised in a head and a tail. The
                 chromosomes function as a genome and are subjected to
                 modification by means of mutation, transposition, root
                 transposition, gene transposition, gene recombination,
                 1-point and 2-point recombination. The chromosomes
                 encode expression trees which are the object of
                 selection. The creation of these separate entities
                 (genome and expression tree) with distinct functions
                 allows the algorithm to perform with high efficiency:
                 in the symbolic regression, sequence induction and
                 block stacking problems it surpasses genetic
                 programming in more than two orders of magnitude,
                 whereas in the density-classification problem it
                 surpasses genetic programming in more than four orders
                 of magnitude. The suite of problems chosen to
                 illustrate the power and versatility of gene expression
                 programming includes, besides the above mentioned
                 problems, two problems of Boolean concept learning: the
                 11-multiplexer and the GP rule problem.",
  notes =        "Date: Tue, 14 Nov 2000 21:04:44 -0100 To:
                 genetic-programming
                 <genetic-programming@cs.stanford.edu>

                 From: Candida Ferreira <candida.f@mail.telepac.pt>
                 Subject: GP: Paper on gene expression programming Hi
                 all,

                 My paper on gene expression programming is now
                 available as a pdf for download at my site:
                 http://www.gene-expression-programming.com

                 Be advised that different versions of this paper were
                 submitted and rejected by Nature and Genetic
                 Programming and Evolvable Machines. One of the reasons
                 one anonymous reviewer from GPEM gave was that The
                 performance of the GEP algorithm compared to GP seems
                 too good to be true to me. As I really want to see
                 other scientists using GEP in other applications, I
                 decided to publish my paper on the web in order to make
                 this powerful algorithm available to all. Remember,
                 though, that there is a patent pending and GEP can not
                 be used commercially. Best regards, Candida Ferreira",
  size =         "pages",
}

@Misc{ferreira:2001:WSC6,
  author =       "Candida Ferreira",
  title =        "{GEP} tutorial",
  howpublished = "WSC6 tutorial",
  year =         "2001",
  month =        sep,
  email =        "candidaf@gene-expression-programming.com",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  URL =          "http://www.gene-expression-programming.com/webpapers/GEPtutorial.pdf",
  notes =        "WSC6, 6th World Conference on Soft Computing in
                 Industrial Applications

                 presentation:
                 http://www.gene-expression-programming.com/webpapers/slideShow.pdf

                 See discussion eg
                 http://groups.yahoo.com/group/genetic_programming/message/68",
}

@InProceedings{ferreira:2001:wsc6Aa,
  author =       "Candida Ferreira",
  title =        "Gene Expression Programming in Problem Solving",
  booktitle =    "Soft Computing and Industry Recent Applications",
  year =         "2001",
  editor =       "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and
                 Takeshi Furuhashi and Frank Hoffmann",
  pages =        "635--654",
  month =        "10--24 " # sep,
  publisher =    "Springer-Verlag",
  note =         "Published 2002",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  ISBN =         "1-85233-539-4",
  notes =        "WSC6
                 http://www.springer.de/cgi/svcat/search_book.pl?isbn=1-85233-539-4",
}

@Article{ferreira:2001:CS,
  author =       "C\^andida Ferreira",
  title =        "Gene Expression Programming: {A} New Adaptive
                 Algorithm for Solving Problems",
  journal =      "Complex Systems",
  year =         "2001",
  volume =       "13",
  number =       "2",
  email =        "candidaf@gene-expressionprogramming.com",
  keywords =     "genetic algorithms, genetic programming, GEP",
  URL =          "http://www.complex-systems.com/Archive/hierarchy/abstract.cgi?vol=13&iss=2&art=01",
  abstract =     "Gene expression programming, a genotype/phenotype
                 genetic algorithm (linear and ramified), is presented
                 here for the first time as a new technique for the
                 creation of computer programs. Gene expression
                 programming uses character linear chromosomes composed
                 of genes structurally organized in a head and a tail.
                 The chromosomes function as a genome and are subjected
                 to modification by means of mutation, transposition,
                 root transposition, gene transposition, gene
                 recombination, and one- and two-point recombination.
                 The chromosomes encode expression trees which are the
                 object of selection. The creation of these separate
                 entities (genome and expression tree) with distinct
                 functions allows the algorithm to perform with high
                 efficiency that greatly surpasses existing adaptive
                 techniques. The suite of problems chosen to illustrate
                 the power and versatility of gene expression
                 programming includes symbolic regression, sequence
                 induction with and without constant creation, block
                 stacking, cellular automata rules for the
                 density-classification problem, and two problems of
                 boolean concept learning: the 11-multiplexer and the GP
                 rule problem.",
}

@InProceedings{ferreira:2002:EuroGP,
  title =        "Discovery of the Boolean Functions to the Best
                 Density-Classification Rules Using Gene Expression
                 Programming",
  author =       "C\^andida Ferreira",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "50--59",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "Cellular automata are idealized versions of massively
                 parallel, decentralized computing systems capable of
                 emergent behaviours. These complex behaviors result
                 from the simultaneous execution of simple rules at
                 multiple local sites. A widely studied behavior
                 consists of correctly determining the density of an
                 initial configuration, and both human and
                 computer-written rules have been found that perform
                 with high efficiency at this task. However, the two
                 best rules for the density-classification task,
                 Coevolution1 and Coevolution2, were discovered using a
                 coevolutionary algorithm in which a genetic algorithm
                 evolved the rules and, therefore, only the output bits
                 of the rules are known. However, to understand why
                 these and other rules perform so well and how the
                 information is transmitted throughout the cellular
                 automata, the Boolean expressions that orchestrate this
                 behaviour must be known. The results presented in this
                 work are a contribution in that direction.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{ferreira:2002:FEA,
  author =       "Candida Ferreira",
  title =        "Mutation, Transposition, and Recombination: An
                 Analysis of the Evolutionary Dynamics",
  booktitle =    "4th International Workshop on Frontiers in
                 Evolutionary Algorithms",
  year =         "2002",
  editor =       "Manuel Grana Romay and Richard Duro",
  address =      "North Carolina, USA",
  month =        "8-14 " # mar,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISBN =         "0-9707890-1-7",
  URL =          "http://www.gene-expression-programming.com/webpapers/ferreira-FEA02.pdf",
  abstract =     "Gene expression programming (GEP) uses mutation,
                 transposition, and crossover to create variation.
                 Although there exists a large body of work in genetic
                 algorithms concerning the roles of mutation and
                 recombination, these results not only do not apply to
                 GEP due to the genotype/phenotype representation but
                 also seem to contradict the GEP experience. Therefore,
                 and given the diversity of GEP operators, it is
                 convenient to develop some kind of understanding of
                 their power. The aim of this work is to help develop
                 such an understanding and to show the evolutionary
                 dynamics and the transforming power of each genetic
                 operator, with their advantages and limitations.",
  notes =        "Sat, 23 Mar 2002 17:52:10 GMT
                 genetic_programming@yahoogroups.com

                 FEA2002 In conjunction with Sixth Joint Conference on
                 Information Sciences",
}

@InProceedings{ferreira:2002:WSC,
  author =       "C\^andida Ferreira",
  title =        "Function Finding and the Creation of Numerical
                 Constants in Gene Expression Programming",
  booktitle =    "7th Online World Conference on Soft Computing in
                 Industrial Applications",
  year =         "2002",
  month =        sep # " 23 - " # oct # " 4",
  note =         "on line",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  abstract =     "Gene expression programming is a genotype/phenotype
                 system that evolves computer programs of different
                 sizes and shapes (the phenotype) encoded in linear
                 chromosomes of fixed length (the geno-type). The
                 chromosomes are composed of multiple genes, each gene
                 encoding a smaller sub-program. Furthermore, the
                 structural and functional organization of the linear
                 chromosomes allows the uncon-strained operation of
                 important genetic operators such as mutation,
                 transposition, and recombination. In this work, three
                 function finding problems, including a high dimensional
                 time series prediction task, are analyzed in an attempt
                 to discuss the question of constant creation in
                 evolutionary computation by comparing two different
                 approaches to the problem of constant creation. The
                 first algorithm involves a facility to manipulate
                 random numerical constants, whereas the second finds
                 the numerical constants on its own or invents new ways
                 of representing them. The results presented here show
                 that evolutionary algorithms perform considerably worse
                 if numerical constants are explicitly used.",
  notes =        "WSC7 http://wsc7.ugr.es/",
}

@InProceedings{ferrer:1995:bef,
  author =       "Gabriel J. Ferrer and Worthy N. Martin",
  title =        "Using Genetic Programming to Evolve Board Evaluation
                 Functions for a Boardgame",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "747",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Senet",
  URL =          "http://www.cs.virginia.edu/~gjf2a/work/papers/senet.ps",
  url_2 =        "ftp://cs.ucl.ac.uk/genetic/papers/senet.ps.gz",
  size =         "6 pages",
  abstract =     "In this paper, we employ the genetic programming
                 paradigm to enable a computer to learn to play
                 strategies for the ancient Egyptian boardgame Senet by
                 evolving board evaluation functions. Formulating the
                 problem in terms of board evaluation functions made it
                 feasible to evaluate the fitness of game playing
                 strategies by using tournament-style fitness
                 evaluation. The game has elements of both strategy and
                 chance. Our approach learns strategies which enable the
                 computer to play consistently at a reasonably skillful
                 level.",
  notes =        "ICEC-95 http://www.io.org/~causal/c_p/cpec95.htm
                 Editors not given by IEEE, Organisers David Fogel and
                 Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html

                 Fitness given by knockout tournament, rank-proprtionate
                 selection, mutation and crossover, generational,
                 non-standard random initial population
                 creation/mutation/crossover, no size limit on programs.
                 2 non-seeded runs, 2 seeded runs (504 random + 8
                 different hand-coded). No discussion of statistical
                 significance of results.

                 ",
}

@InProceedings{figueiredopereiraemer:2002:gecco,
  author =       "Maria Cl{\'a}udia Figueiredo Pereira Emer and Silva
                 Regina Vergilio",
  title =        "{GPTesT}: {A} Testing Tool Based On Genetic
                 Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1343--1350",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, search-based
                 software engineering, fault-based testing, induction of
                 programs, mutation analysis, software test criteria",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{finley:1999:E,
  author =       "Marion R. {Finley Jr.} and Haruo Akimaru and Evelyne
                 B. Hausen-Tropper",
  title =        "Element of a theoretical model of tele-learning using
                 genetic algorithms",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "93--98",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@InCollection{fischer:1994:bmpm,
  author =       "Ronald F. Fischer",
  title =        "Applying Genetic Algorithms to Bitmap Pattern
                 Matching",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "41--48",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, GENESIS",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InCollection{flannery:2000:TETBPML,
  author =       "Matthew Flannery",
  title =        "The Evolution of Traffic Behavior Patterns on a
                 Macroscopic Level",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "135--142",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InCollection{flight:1997:psGPtmt,
  author =       "John Flight",
  title =        "The Use of Program State by a Genetic Program to Track
                 a Moving Target",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "57-",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "how a GP might use state variables and feedback from
                 the fitness measure",
  notes =        "part of koza:1997:GAGPs",
}

@InCollection{Flister:1997:rational,
  author =       "Erik D. Flister",
  title =        "The Deceptive Problem of Rational Trading and
                 Negotiation Strategies in Artificial Economic
                 Communities",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "66--75",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{Floreano:1997:gsrq,
  author =       "Dario Floreano and Stefano Nolfi",
  title =        "God Save the Red Queen! Competition in Co-Evolutionary
                 Robotics",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Artifical life and evolutionary robotics",
  pages =        "398--406",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "ftp://kant.irmkant.rm.cnr.it/pub/econets/floreano.co-evolution.ps.Z",
  notes =        "GP-97",
}

@InProceedings{fogel:1996:pedcs,
  author =       "David B. Fogel and Lawrence J. Fogel",
  title =        "Preliminary Experiments on Discriminating between
                 Chaotic Signals and Noise Using Evolutionary
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Evolutionary Programming",
  pages =        "512--520",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 EP paper",
}

@InProceedings{folino:1999:ACGPAC,
  author =       "Gianluigi Folino and Clara Pizzuti and Giandomenico
                 Spezzano",
  title =        "A Cellular Genetic Programming Approach to
                 Classification",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1015--1020",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{folino:2000:GPSAhmeDT,
  author =       "Gianluigi Folino and Clara Pizzuti and Giandomenico
                 Spezzano",
  title =        "Genetic Programming and Simulated Annealing: {A}
                 Hybrid Method to Evolve Decision Trees",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "294--303",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "A method for the data mining task of data
                 classification, suitable to be implemented on massively
                 parallel architectures, is proposed. The method
                 combines genetic programming and simulated annealing to
                 evolve a population of decision trees. A cellular
                 automaton is used to realise a fine-grained parallel
                 implementation of genetic programming through the
                 diffusion model and the annealing schedule to decide
                 the acceptance of a new solution. Preliminary
                 experimental results, obtained by simulating the
                 behaviour of the cellular automaton on a sequential
                 machine, show significant better performances with
                 respect to C4.5.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{folino:2001:EuroGP,
  author =       "Gianluigi Folino and Clara Pizzuti and Giandomenico
                 Spezzano",
  title =        "{CAGE}: {A} Tool for Parallel Genetic Programming
                 Applications",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "64--73",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Parallel
                 programming, Cellular model",
  ISBN =         "3-540-41899-7",
  size =         "10 pages",
  abstract =     "A new parallel implementation of genetic programming
                 based on the cellular model is presented and compared
                 with the island model approach. Although the widespread
                 belief that cellular model is not suitable for parallel
                 genetic programming implementations, experimental
                 results show a better convergence with respect to the
                 island approach, a good scale-up behaviour and a nearly
                 linear speed-up.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{FonluptPPSN2000,
  author =       "C. W. B. Fonlupt and D. Robilliard",
  title =        "Genetic Programming with Dynamic Fitness for a Remote
                 Sensing Application",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VI 6th
                 International Conference",
  editor =       "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
                 Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
                 Merelo and Hans-Paul Schwefel",
  year =         "2000",
  publisher =    "Springer Verlag",
  address =      "Paris, France",
  month =        "16-20 " # sep,
  note =         "LNCS 1917",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-lil.univ-littoral.fr/~robillia/Publis/lil-00-2.ps.gz",
}

@Article{kybernetes:forsyth,
  author =       "Richard Forsyth",
  title =        "{BEAGLE} {A} {Darwinian} Approach to Pattern
                 Recognition",
  journal =      "Kybernetes",
  year =         "1981",
  volume =       "10",
  pages =        "159--166",
  keywords =     "genetic algorithms, genetic programming",
  size =         "8 pages",
  notes =        "Copy from British Library May 1994",
}

@Book{Forsyth:1986:mlESir,
  author =       "Richard Forsyth and Roy Rada",
  title =        "Machine Learning applications in Expert Systems and
                 Information Retrieval",
  publisher =    "Ellis Horwood",
  year =         "1986",
  series =       "Ellis Horwood series in artificial intelligence",
  address =      "Chichester, UK",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Chapters on BEAGLE",
  size =         "275 pages",
}

@InCollection{forsyth:1989:ei,
  author =       "Richard Forsyth",
  title =        "The evolution of intelligence",
  booktitle =    "Machine Learning, Priciples and Techniques",
  publisher =    "Chapman and Hall",
  year =         "1989",
  editor =       "Richard Forsyth",
  chapter =      "4",
  pages =        "65--82",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-412-30570-4",
  notes =        "some general stuff on history of GAs, evolution
                 strategy and evolution programming, cf Fogel 1966,
                 description of Goldberg's natural gas pipeline control
                 GA/classifier experiments. BEAGLE applied to
                 classifiying countries by their flags etc and brief
                 description of PC/Beagle being applied to forensic
                 science {"}where a rule set developed with the aid of
                 PC/BEAGLE was found to descriminate among glass
                 fragments better than standard statistical
                 procedures{"} [page 77].

                 ",
}

@Unpublished{foster:1997:ieGPb,
  author =       "James A. Foster and Terence Soule",
  title =        "Comments on the intron/exon distinction as it relates
                 to genetic programming and biology",
  note =         "Position paper at the Workshop on Exploring Non-coding
                 Segments and Genetics-based Encodings at ICGA-97",
  month =        "21 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, introns",
  URL =          "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/soule.ps",
  notes =        "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/",
  size =         "3 pages",
}

@Article{foster:2001:discipulus,
  author =       "James A. Foster",
  title =        "Review: Discipulus: {A} Commercial Genetic Programming
                 System",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "2",
  pages =        "201--203",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1389-2576",
  URL =          "http://ipsapp009.lwwonline.com/content/getfile/4723/5/8/fulltext.pdf",
}

@InProceedings{francone:1996:bench,
  author =       "Frank D. Francone and Peter Nordin and Wolfgang
                 Banzhaf",
  title =        "Benchmarking the Generalization Capabilities of a
                 Compiling Genetic programming System using Sparse Data
                 Sets",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "72--80",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://pw2.netcom.com/~lmdmit84/GP96%20Camera%20Ready%20Paper.pdf",
  size =         "9 pages",
  notes =        "GP-96 Notes based upon version submitted to GP-96

                 Wed, 17 Apr 1996 09:20:19 PDT

                 When I read your email (koza's), I went back and
                 checked the output on two other problems that we ran as
                 part of that paper. Gaussian 3D and Phoneme
                 Classification. Each of these was a two output problem
                 and the way the classification was set up, one would
                 expect less than 50% correct classification from a
                 randomly created individual.

                 In those problems, we used 10 different random seeds,
                 3000 individuals per run. The following were the
                 results for the best individual from generation 0's
                 classification rate.

                 Mean Best Worst gauss 0.59 0.64 0.55 iris 0.98 0.99
                 0.97 phoneme 0.73 0.75 0.71

                 Note that these figures represent the results of a
                 random search of 30,000 individuals.

                 As Peter Nordin points out in his email to which this
                 is a reply, on the IRIS problem, even the worst figure
                 is very good. In fact it was statistically
                 indistinguishible from a highly optimized KNN beachmark
                 run on twice as large a training set. This is because
                 the IRIS problem is trivial. As pointed out in the
                 above referenced paper, IRIS should probably not be
                 used as a measure of the learning ability of any ML
                 system, notwithstanding its status as a 'classic'
                 problem. It is probably better characterized as a
                 'classic' way to make a ML system look good.

                 On the other two problems, which were much more
                 difficult, the genetic search improved on the random
                 search considerably. The individuals with the best
                 abilitiy to generalize on the test data set were
                 respectively.

                 Best Generalizer Gaussian 3D 72% Phoneme 85%

                 I report these figures here because the generation 0
                 figures are not reported in the above paper
                 directly.

                 Regards

                 Frank Francone

                 ",
}

@InProceedings{banzhaf:1996:mutatation,
  author =       "Wolfgang Banzhaf and Frank D. Francone and Peter
                 Nordin",
  title =        "The Effect of Extensive Use of the Mutation Operator
                 on Generalization in Genetic Programming Using Sparse
                 Data Sets",
  booktitle =    "Parallel Problem Solving from Nature IV, Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  series =       "LNCS",
  volume =       "1141",
  pages =        "300--309",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
                 machine code GP CGPS used on IRIS, Gaussian 3D and
                 phoneme ELENA classification problems. Iris trivial. On
                 others best performance from 50/50 mix of crossover and
                 mutation.

                 Answer extracted via designated hardware register. Stop
                 runs when destructive crossover falls below 10% (used
                 as convergence indicator). Mutation giving rise to more
                 complex introns. GP premature convergence",
}

@Unpublished{banzhaf:1997:emvsea,
  author =       "Wolfgang Banzhaf and Frank D. Frankone and Peter
                 Nordin",
  title =        "Some Emergent Properties of Variable Size {EA}s",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 variable size representation",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "4 pages",
}

@Unpublished{banzhaf:1997:wiGPge,
  author =       "Wolfgang Banzhaf and  Peter Nordin and Frank D.
                 Francone",
  title =        "Why introns in genetic programming grow
                 exponentially",
  note =         "Position paper at the Workshop on Exploring Non-coding
                 Segments and Genetics-based Encodings at ICGA-97",
  month =        "21 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, introns",
  URL =          "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/banzhaf.ps",
  notes =        "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/",
  size =         "3 pages",
}

@InProceedings{francone:1999:HCGP,
  author =       "Frank D. Francone and Markus Conrads and Wolfgang
                 Banzhaf and Peter Nordin",
  title =        "Homologous Crossover in Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1021--1026",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Misc{Francone:2000:lrtads,
  author =       "Frank D. Francone and Peter Nordin and Wolfgang
                 Banzhaf and Larry M. Deschaine",
  title =        "Automatic Induction of Machine Code ({AIM}) Learning
                 Real Time Adaptive Control Strategies",
  howpublished = "www document",
  year =         "2000",
  month =        "11 " # may,
  keywords =     "genetic algorithms, genetic programming, discipulus
                 automatic control, industrial control, model design,
                 machine learning",
  URL =          "http://pw2.netcom.com/%7elmdmit84/AimProcessControl2000.pdf",
  size =         "4 pages",
  notes =        "high level",
}

@InProceedings{Fraser:1994:inkbiro,
  author =       "A. P. Fraser and J. R Rush",
  title =        "Putting {INK} into a {BIR}o: {A} discussion of problem
                 domain knowledge for evolutionary robotics",
  booktitle =    "AISB Workshop on Evolutionary Computing",
  year =         "1994",
  editor =       "T. C. Fogarty",
  address =      "Leeds, UK",
  month =        "11-13 " # apr,
  organisation = "AISB",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Proceedings of the Workshop on Artificial Intelligence
                 and Simulation of Behaviour Workshop on Evolutionary
                 Computing. Workshop in Leeds, UK, April 11-13, 1994
                 This paper does NOT appear in the proceedings published
                 by Springer_Verlag

                 ",
}

@Article{freeland:2002:GPEM,
  author =       "Stephen J. Freeland",
  title =        "The Darwinian Genetic Code: An Adaptation for
                 Adapting?",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "2",
  pages =        "113--127",
  month =        jun,
  keywords =     "error minimization, genetic code, evolution,
                 adaptation",
  ISSN =         "1389-2576",
  abstract =     "The genetic code is a ubiquitous interface between
                 inert genetic information and living organisms, as such
                 it plays a fundamental role in defining the process of
                 evolution. There have been many attempts to identify
                 features of the code that are themselves adaptations.
                 So far, the strongest evidence for an adaptive code is
                 that the assignments of amino acids (encoded objects)
                 to codons (coding units) appear to be organized so as
                 to minimize the change in amino acid hydrophobicity
                 that results from random mutations. One possibility not
                 previously discussed is that this feature of the code
                 may in fact represent an adaptation to maximize the
                 efficiency of adaptive evolution, particularly given
                 the maximized connectedness of protein fitness
                 landscapes afforded by the redundancy of the code.",
  notes =        "Special issue on Gene Expression Kargupta:2002:GPEM
                 Article ID: 408585",
}

@InProceedings{freeman:1998:lrGPcfg,
  author =       "Jennifer J. Freeman",
  title =        "A Linear Representation for {GP} using Context Free
                 Grammars",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "72--77",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, CFG/GP,
                 PORS",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{Freitas:1997:GPf2dm,
  author =       "Alex A. Freitas",
  title =        "A Genetic Programming Framework for Two Data Mining
                 Tasks: Classification and Generalized Rule Induction",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms, SQL",
  pages =        "96--101",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "citeseer.nj.nec.com/43454.html",
  notes =        "GP-97

                 Lazy learning, separation of query tree encodes
                 Tuple-Set Descriptor (SQL), from goal attribute. Goal
                 subject to three types of mutation",
}

@InProceedings{freitas:1998:GAdkn,
  author =       "Alex A. Freitas",
  title =        "A Genetic Algorithm for Discovering Knowledge
                 Nuggets",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Article{freitas:2001:GPEM,
  author =       "Alex A. Freitas",
  title =        "Book Review: Data Mining Using Grammar-Based Genetic
                 Programming and Applications",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "2",
  pages =        "197--199",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISSN =         "1389-2576",
  URL =          "http://ipsapp009.lwwonline.com/content/getfile/4723/5/7/fulltext.pdf",
  notes =        "review of ManLeungWong:book",
}

@InProceedings{french:2001:gecco,
  title =        "Evolving a Nervous System of Spiking Neurons for a
                 Behaving Robot",
  author =       "R. L. B. French and R. I. Damper",
  pages =        "1099--1106",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "evolutionary robotics, genetic programming, spiking,
                 neurons, emergent behaviours",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{frey:2001:gecco,
  title =        "Evolving Strategies for Global Optimization - {A}
                 Finite State Machine Approach",
  author =       "Clemens Frey and Gunter Leugering",
  pages =        "27--33",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, finite state
                 machines, optimizing controllers, dynamic systems,
                 adapted spatial optimization strategies",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@Article{frey:2002a,
  author =       "Clemens Frey",
  title =        "Co-Evolution of Finite State Machines for
                 Optimization: Promotion of Devices Which Search
                 Globally",
  journal =      "International Journal of Computational Intelligence
                 and Applications",
  year =         "2002",
  volume =       "1",
  number =       "2",
  pages =        "1--16",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1469-0268",
  URL =          "http://www.mathematik.tu-darmstadt.de/~frey/",
  size =         "16 p.",
  abstract =     "In this work a co-evolutionary approach is used in
                 conjunction with Genetic Programming operators in order
                 to find certain transition rules for two-step discrete
                 dynamical systems. This issue is similar to the
                 well-known artificial-ant problem. We seek the dynamic
                 system to produce a trajectory leading from given
                 initial values to a maximum of a given spatial
                 functional. This problem is recast into the framework
                 of input-output relations for controllers, and the
                 optimization is performed on program trees describing
                 input filters and finite state machines incorporated by
                 these controllers simultaneously. In the context of
                 Genetic Programming there is always a set of test cases
                 which has to be maintained for the evaluation of
                 program trees. These test cases are subject to
                 evolution here, too, so we employ a so-called
                 host-parasitoid model in order to evolve optimizing
                 dynamical systems. Reinterpreting these systems as
                 algorithms for finding the maximum of a functional
                 under constraints, we have derived a paradigm for the
                 automatic generation of adapted optimization algorithms
                 via optimal control. We provide numerical examples
                 generated by the GP-system MathEvEco. These examples
                 refer to key properties of the resulting strategies and
                 they include statistical evidence showing that for this
                 problem of system identification the co-evolutionary
                 approach is superior to standard Genetic Programming.",
}

@PhdThesis{Frey:thesis,
  author =       "Clemens Frey",
  title =        "Virtual Ecosystems - Evolutionary and Genetic
                 Programming from the perspective of modern means of
                 ecosystem-modelling",
  school =       "Darmstadt University of Technology",
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  notes =        "See Frey:2002",
}

@Book{frey:2002,
  author =       "Clemens Frey",
  title =        "Virtual Ecosystems - Evolutionary and Genetic
                 Programming from the perspective of modern means of
                 ecosystem-modelling",
  publisher =    "Institute for Terrestrial Ecosystems, Bayreuth",
  year =         "2002",
  volume =       "93",
  series =       "Bayreuth Forum Ecology",
  address =      "Bayreuth, Germany",
  note =         "(in German)",
  email =        "frey@mathematik.tu-darmstadt.de",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0944-4122",
  URL =          "http://www.bitoek.uni-bayreuth.de/bitoek/en/pub/pub/pub_detail.php?id_obj=7556",
  size =         "199 p.",
  abstract =     "The realm of Evolutionary Computation covers many
                 tools commonly used for solving complex discrete and
                 continuous global optimization problems. These methods,
                 which are known as Genetic Algorithms, Evolution
                 Strategies, Evolutionary Programming and Genetic
                 Programming, stem from efforts of modeling adaptive
                 systems, from engineering and computer science. They
                 are based on the idea of restating the Darwinian
                 principles of natural evolution in algorithmic terms in
                 order to get problem-solving methods for non-biological
                 applications. Today Genetic Algorithms, Evolution
                 Strategies and Evolutionary Programming mainly serve as
                 mathematical techniques of numerical optimization.
                 Genetic Programming likewise is an adaptation
                 technique, but there is a different focus: based on
                 evolutionary principles Genetic Programming enables us
                 to automatically generate computer programs.The
                 underlying hypotheses of this book is that the main
                 point of natural, biological evolution is its data
                 processing aspect. Evolution is seen as a certain way
                 of processing individuals' and populations' genetic
                 data. Referring to Evolutionary Computation there is a
                 very interesting question now: Is it appropriate to
                 employ Genetic Programming and similar algorithms in
                 order to investigate natural evolution? Of course this
                 means turning around the application profile of
                 Evolutionary Computation, so where do we have to alter
                 its algorithmic structure and the like? Finally,
                 supposed there is a modified method, how do the results
                 of both the classic algorithm and the modified variant
                 compare to each other?In the first chapter we state the
                 general notion of a search strategy. It may be a living
                 being's policy of resource allocation, for example, but
                 the notion covers optimization methods, too. A search
                 strategy shall be defined in mathematical terms as
                 being a dynamical system combined with a quality
                 measure which is based on the trajectories the
                 dynamical system produces. The author proposes a
                 precise formulation for what a search strategy is
                 generally claimed to accomplish, namely to generate
                 dynamic behavior which gets us to the neighborhood of a
                 predefined goal, possibly obeying certain constraints
                 within the dynamics of the search process.Chapter two
                 contains a gentle introduction into the field of
                 Evolutionary Computation, namely Adaptive Systems,
                 Genetic Algorithms, Evolution Strategies and
                 Evolutionary Programming. We focus on Genetic
                 Programming, however, and take a look at a paradigmatic
                 experiment for automatically finding search strategies,
                 i.e. the so-called artificial ant-experiment. In doing
                 so the reader is also shown how the mathematical
                 framework built in the first chapter may be used to
                 formulate the artificial ant-problem.",
  abstract =     "The following chapter addresses the issue of
                 artificially creating evolution in virtual or simulated
                 ecosystems and the question whether this can be done
                 with the help of Evolutionary Computation. Since we
                 want to analyse shortcomings of the conventional
                 approaches and necessary adjustments, basic features of
                 natural evolution are stated and discussed at first.
                 Then we take a closer look at the area of Artificial
                 Life and discuss specific software from this field.
                 This discussion is concerned with so-called strong
                 approaches like tierra and avida as well as weak
                 approaches like the ecosystem-oriented Tragic++ system;
                 besides, connections to social learning paradigms and
                 Nouveau Artificial Intelligence are highlighted. Taking
                 this broad view into account we conclude this chapter
                 by listing a set of features which have to be comprised
                 by a serious a model for evolution in virtual
                 ecosystems. The gist of these desired features says
                 that it is feasible to represent strategy programs as
                 trees like in Genetic Programming, for this kind of
                 representation causes a non-trivial, morphogenic
                 mapping between the genotypic and the phenotypic space.
                 It has to be conceded, however, that exogenously and
                 a-priori given fitness-functions as well as the
                 synchronous reproduction schemes which are almost
                 always used in Genetic Programming are not appropriate
                 for modeling evolution in virtual ecosystems. Chapters
                 four to six describe how a system called MathEvEco was
                 formulated and implemented according to these
                 guidelines. Chapter four focuses on strongly typed tree
                 representations of programs. Feasible sets of strongly
                 typed program trees are defined precisely and their
                 relationship with context-free grammars and the
                 parameter-dependent evaluation of program trees are
                 investigated in mathematical terms. These mathematical
                 tools having been made available, genetic operators and
                 initialization procedures of MathEvEco are stringently
                 formulated in the fifth chapter. The system was
                 supposed to be as flexible as possible. To this end the
                 author has not only accessed a strongly typed version
                 of the very classic crossover operator, but included a
                 bunch of strongly typed mutation operators and the
                 novel PTC2 algorithm for randomly generating program
                 trees. In order to allow algorithmic comparisons the
                 operators may be assembled in two fundamentally
                 different ways; they may either be merged into a system
                 of common Genetic Programming or they may be assembled
                 as the desired system for modeling evolution in virtual
                 ecosystems. Both of these possibilities are described,
                 still in mathematical terms.The resulting systems are
                 called MathEvEco-GP and MathEvEco-AL, respectively.",
  abstract =     "While chapter five has been written in order to allow
                 these systems to be communicated in a transparent and
                 precise manner, chapter six shall illuminate their
                 actual implementation within the scope of the
                 mathematical software system Mathematica. To this end
                 we show how program trees are handled in Mathematica,
                 how model-specific and problem-specific knowledge is to
                 be inserted by the user of MathEvEco, and in which way
                 the various genetic operators have been implemented.
                 Since MathEvEco can not only be run on a single machine
                 but rather on clusters of workstations, there is a
                 special treatment of aspects of parallel programming,
                 too. Finally the functionality of MathEvEco is
                 exemplified by means of a symbolic regression
                 problem.The final chapter seven is dedicated to a case
                 study. It consists of automatically generating search
                 devices which is a special case of the general setting
                 having been introduced in chapter one. There are a two
                 different interpretations of this special problem. On
                 the one hand side it may be understood in terms of
                 numerical optimization; we presuppose an multi-modal
                 objective function which may be imagined as a
                 three-dimensional surface having many peaks. Strategies
                 have to be evolved by MathEvEco-GP which are only
                 provided with local information about this surface but
                 are nevertheless required to lead the search devices to
                 one of the highest peaks. On the other hand side the
                 special problem may be understood in terms of an
                 ecosystem where many organisms struggle for allocating
                 a resource. It is quite important to realize that in
                 this case there is a natural component of interaction
                 since individual organisms consume resources from their
                 immediate neighborhood and thus affect organisms there,
                 too. For this kind of ecosystem simulation we have
                 utilized the MathEvEco-AL evolution variant which
                 provides implicit fitness assessment and asynchronous
                 reproduction of 'living` organisms, i.e. devices.In
                 both cases program trees represent strategies for
                 potentially interacting devices. From a computer
                 science point of view each of these devices is made up
                 of a finite state machine and an input filter which
                 maps continuous input from various channels into a
                 finite set of symbols. The finite state machine's
                 output iteratively controls the search device during a
                 predefined maximum number of steps. The results of our
                 various experiments with MathEvEco-GP show that if
                 interaction and thus parallel search are introduced, it
                 is much more likely that global optima, i.e. the
                 highest peaks of the objective function will be located
                 by the devices. In all experiments we were able to find
                 robust strategies; this means that under certain
                 conditions strategies evolved in conjunction with an
                 objective function A will also perform well if acting
                 on a different function B.",
  abstract =     "We have also undertaken experiments involving the
                 co-evolution of strategies and test cases; we show that
                 co-evolution increases search capabilities of the
                 strategies evolved with MathEvEco-GP.Compared to this
                 system realizing classical yet strongly typed Genetic
                 Programming, MathEvEco-AL is fundamentally different
                 because of its modeling claims. The results of our
                 experiments indicate, however, that in this system
                 evolution of search strategies is realized, too. This
                 is supported by many parameters of the evolving
                 ecosystem, e.g. increases in average ages of
                 individuals, increases in their average resource load
                 and a steady increase of the overall population size
                 over time. These observations point out that the
                 virtual organisms evolve and gradually learn how to
                 deal with exterior constraints defined mainly by the
                 resource distribution objective function. Moreover,
                 because we have utilized the same design for the
                 devices evolved in MathEvEco-GP as well as in
                 MathEvEco-AL, the resulting strategies compare very
                 well. The extra advantage of the latter system is, of
                 course, that it enables us to seriously investigate the
                 interaction of ecosystems and their evolutionary
                 formation without having to presuppose artifacts like
                 explicit fitness functions. The mathematical tool for
                 doing this are hierarchical dynamic systems. We
                 conclude, after all, that it is possible to start from
                 classical Genetic Programming and build a system for
                 answering relevant questions about ecosystem-related
                 evolution processes. Because of common building blocks
                 of both the classical and the new system, the results
                 can be compared quite easily. The Mathematica packages
                 MathEvEco comprising these systems may be obtained from
                 the author.For this book touches many different
                 scientific issues there is an detailed section of
                 annotations deepening biological and ecosystem modeling
                 aspects as well as referring to the scientific history
                 of Evolutionary Computation. An appendix covers
                 software engineering with Mathematica. The extensive
                 bibliography allows readers to take a closer look at
                 the issues having been addressed. A subject index and a
                 list of mathematical symbols conclude this work.",
  title2 =       "Evolutionäre und Genetische Programmierung im Lichte
                 moderner Ökosystemmodellierung",
  abstract =     "Genetische Algorithmen und verwandte
                 Evolutions-algorithmen spielen in der angewandten
                 Mathematik und in der Informatik eine wichtige Rolle
                 als Werkzeuge, mit deren Hilfe komplizierte
                 Optimierungsprobleme näherungsweise gelöst werden
                 können. Die Methoden basieren auf der Idee, Prinzipien
                 natürlicher Evolutionsabläufe algorithmisch zu
                 formulieren und geeignet anzupassen, damit sie zur
                 Problemlösung in nicht-biologischen Anwendungsfeldern
                 eingesetzt werden können. Man verwendet zum Beispiel
                 die sogenannte Genetische Programmierung zur
                 automatischen, evolutionsbasierten Erzeugung von
                 Computerprogrammen. Der Autor des vorliegenden Bandes
                 geht von der Hypothese aus, dass natürliche Evolution
                 einen Verar-beitungsprozess genetischer Information
                 darstellt. Es wird untersucht, ob Evolutionsalgorithmen
                 in Umkehrung ihres bisherigen Profils auch als Modell
                 für biologische Evolution verwendet werden können. An
                 welchen Stellen muss die Methodik zu diesem Zweck
                 verändert werden? - Zur Beant-wortung dieser Fragen
                 wird eine mathematische Formulierung des Darwinschen
                 Evolutionsprozesses im Rahmen hierarchischer, diskreter
                 dynamischer Systeme vorgeschlagen. Auf diesem Fundament
                 werden bestehende Methoden (Genetische Programmierung,
                 Artificial Life) analysiert und ein neues,
                 individuenbasiertes Evolutionsmodell realisiert. Dieses
                 Modell wurde als Mathematica-Paket unter dem Namen
                 MathEvEco implementiert; es wird in diesem Band
                 ausführlich dargestellt, ebenso wie die vielen
                 durchgeführten Versuche zur automatischen Erzeugung von
                 Suchprogrammen, sowie ihre Parameter und Ergebnisse.
                 Der Leser gewinnt also nicht nur einen Einblick in den
                 aktuellen Stand von Evolutionsalgorithmen und Ansätzen
                 zur Simulation von Evolution in virtuellen Ökosystemen,
                 sondern wird schließlich auch in der Lage sein, eigene
                 Evolu-tionsexperimente durchzuführen.",
  notes =        "Bayreuther Forum Ökologie 93, 1-199 (2002)",
}

@InProceedings{Freyeretal1998,
  author =       "Stephan Freyer and J{\"o}rg Graefe and Matthias
                 Heinzel and Peter Marenbach",
  address =      "Aachen, Germany",
  booktitle =    "Eufit '98, 6th European Congress on Intelligent
                 Techniques and Soft Computing, ELITE - European
                 Laboratory for Intelligent TechniquesEngineering",
  editor =       "Hans-J{\"u}rgen Zimmermann",
  pages =        "1471--1475",
  title =        "Evolutionary Generation and Refinement of Mathematical
                 Process Models",
  volume =       "III",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming, SMOG,
                 bioprocess, modelling",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/LIT",
  email =        "pmarenbach@gmx.net",
  notes =        "http://www.eufit.org/proceedings/98/volume3.htm

                 BASF AG laboratories, high noise. Monod,
                 SubLimTeissier, SubLimJost, SubInhAnstrews, SubInhWebb
                 MATLAB/SIMULINK. Stresses importance of user
                 understandable models, using prior knowledge, parsimony
                 versus accuracy (trade off in fitness function). Batch
                 fed fermentation.

                 ",
}

@InCollection{friedman:2000:EPPR,
  author =       "Patri Friedman",
  title =        "Evolving a Program to Play Rock-Paper-Scissors",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "143--152",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{friedrich:1996:emfgbb,
  author =       "Christoph M. Friedrich and Claudio Moraga",
  title =        "An Evolutionary Method to Find Good Building-Blocks
                 for Architectures of Artificial Neural Networks",
  booktitle =    "Proceedings of the Sixth International Conference on
                 Information Processing and Management of Uncertainty in
                 Knowledge-Based Systems (IPMU '96)",
  year =         "1996",
  pages =        "951--956",
  address =      "Granada, Spain",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://archive.cis.ohio-state.edu/pub/neuroprose/friedrich.ipmu96.ps.Z",
  abstract =     "This paper deals with the combination of Evolutionary
                 Algorithms and Artificial Neural Networks (ANN). A new
                 method is presented, to find good building-blocks for
                 architectures of Artificial Neural Networks. The method
                 is based on {\em Cellular Encoding}, a representation
                 scheme by F. Gruau, and on Genetic Programming by J.
                 Koza. First it will be shown that a modified Cellular
                 Encoding technique is able to find good architectures
                 even for non-boolean networks. With the help of a
                 graph-database and a new graph-rewriting method, it is
                 secondly possible to build architectures from modular
                 structures. The information about building-blocks for
                 architectures is obtained by statistically analyzing
                 the data in the graph-database. Simulation results for
                 two real-world problems are given.",
}

@InProceedings{LeeannFu:1998:XCSQ,
  author =       "Leeann L. Fu",
  title =        "The {XCS} Classifier System and {Q}-learning",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, Classifier Systems",
  notes =        "GP-98LB",
}

@InProceedings{fuchs:1996:esnnGA,
  author =       "Matthias Fuchs",
  title =        "Evolving Strategies Based on the Nearest Neighbor Rule
                 and a Genetic Algorithm",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "485--490",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 GA paper",
}

@InProceedings{Fuchs:1997:spclGP,
  author =       "Matthias Fuchs and Dirk Fuchs and Marc Fuchs",
  title =        "Solving Problems of Combinatory Logic with Genetic
                 Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "102--110",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{fuchs:1998:xmetsc,
  author =       "Matthias Fuchs",
  title =        "Crossover versus Mutation: An Empirical and
                 Theoretical Case Study",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "78--85",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{fuchs:1999:GLTPSUGP,
  author =       "Marc Fuchs and Dirk Fuchs and Matthias Fuchs",
  title =        "Generating Lemmas for Tableau-based Proof Search Using
                 Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1027--1032",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{fuchs:1999:LPANATBCIGP,
  author =       "Matthias Fuchs",
  title =        "Large Populations Are Not Always The Best Choice In
                 Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1033--1038",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{fuhner:2001:gecco,
  title =        "EvolVision - an Evolvica visualization tool",
  author =       "Tim Fuhner and Christian Jacob",
  pages =        "176",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 EvolVision, Evolvica, visualization, Mathematica, Java,
                 client/server application, plug-in architecture,
                 pedigree diagrams",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{ga:Dickinson87,
  author =       "Cory Fujiki and John Dickinson",
  title =        "Using the Genetic Algorithm to Generate Lisp Source
                 Code to Solve the Prisoner's Dilemma",
  booktitle =    "Genetic Algorithms and their Applications: Proceedings
                 of the second international conference on Genetic
                 Algorithms",
  year =         "1987",
  editor =       "John J. Grefenstette",
  pages =        "236--240",
  address =      "MIT, Cambridge, MA, USA",
  month =        "28-31 " # jul,
  organisation = "AAAI, Naval Research Laboratory, Bolt Beranek and
                 Newman, Inc",
  publisher_address = "Hillsdale, NJ, USA",
  publisher =    "Lawrence Erlbaum Associates",
  keywords =     "genetic algorithms",
  size =         "5 pages",
  notes =        "Complete Lisp S-Expressions generated but are
                 constrained to be a (variable length??) list of
                 condition-action pairs, each of which is an
                 s-expresion. These S-expressions are initially created
                 at random and do _not_ evolve. Instead Mutation, Invert
                 and crossover create new individuals using these
                 existing components.",
}

@InProceedings{fukunaga:1995:dsef,
  author =       "Alex S. Fukunaga and Andrew B. Kahng",
  title =        "Improving the Performance of Evolutionary Optimization
                 by Dynamically Scaling the Evolution Function",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "1",
  pages =        "182--187",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-aig.jpl.nasa.gov/home/fukunaga/publications/ICEC95-priming-camera.ps",
  url_2 =        "http://www.io.org/~causal/c_p/icec95/ec95s106.htm#p0182",
  size =         "6 pages",
  abstract =     "Traditional evolutionary optimization algorithms
                 assume a static environment in which solutions are
                 evolved. Incremental evolution is an approach through
                 which a dynamic evaluation function is scaled over time
                 in order to improve the performance of evolutionary
                 optimization. In this paper, we present empirical
                 results that demonstrate the effectiveness of this
                 approach for genetic programming. Using two domains, a
                 two-agent pursuit-evasion game and the Tracker
                 trail-following task, we demonstrate that incremental
                 evolution is most successful when applied near the
                 beginning of an evolutionary run. We also show that
                 incremental evolution can be successful when the
                 intermediate evaluation functions are more difficult
                 than the target evaluation function, as well as they
                 are easier than the target function.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 ",
}

@InProceedings{fukunaga:1998:gchpGP,
  author =       "Alex Fukunaga and Andre Stechert and Darren Mutz",
  title =        "A Genome Compiler for High Performance Genetic
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "86--94",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www-aig.jpl.nasa.gov/public/home/fukunaga/publications/compile-camera.ps",
  notes =        "GP-98

                 Thu, 25 Jun 1998 10:31:36 PDT We've recently developed
                 a gp system based on lil-gp which evolves s-expressions
                 and compiles it to machine code (specifically, Sparc
                 machine code) to speed up evaluation. In our system,
                 we've found that the overhead of compilation is
                 negligible, since the vast majority of the time spent
                 in execution in an s-expression interpreter (in our
                 case, the lil-gp interpreter) is consumed by the
                 recursive traversal of the tree.

                 A full description, comparisons with previous
                 GP-compiler systems and some experimental results with
                 symbolic regression and image compression are
                 described",
}

@InProceedings{fukunaga:1998:enlpmllicGP,
  author =       "Alex Fukunaga and Andre Stechert",
  title =        "Evolving Nonlinear Predictive Models for Lossless
                 Image Compression with Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "95--102",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{fukunaga:1999:PGA,
  author =       "Alex S. Fukunaga",
  title =        "Portfolios of Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "786",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{fukuyama:1999:APSORPVCEPS,
  author =       "Yoshikazu Fukuyama and Shinichi Takayama and Yosuke
                 Nakanishi and Hirotaka Yoshida",
  title =        "A Particle Swarm Optimization for Reactive Power and
                 Voltage Control in Electric Power Systems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1523--1528",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{furutani:1999:ASIPGAML,
  author =       "Hiroshi Furutani",
  title =        "Analytical Solutions for Infinite Population Genetic
                 Algorithms on Multiplicative Landscape",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "204--211",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  abstract =     "eigen values, eigenvectors, walsh functions",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{fyfe:1999:AFE,
  author =       "Colin Fyfe and John Paul Marney and Heather F. E.
                 Tarbert",
  title =        "Technical analysis versus market efficiency - a
                 genetic programming approach",
  journal =      "Applied Financial Economics",
  year =         "1999",
  volume =       "9",
  number =       "2",
  pages =        "183--191",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://alidoro.catchword.com/vl=8080356/cl=18/nw=1/fm=docpdf/rpsv/catchword/routledg/09603107/v9n2/s7/p183",
  size =         "9 pages",
  abstract =     "In the paper the authors maintain that the prevalence
                 of technical analysis in professional investment argues
                 that such techniques should perhaps be taken more
                 seriously by academics. The new technique of genetic
                 programming is used to investigate a long time series
                 of price data for a quoted property investment company,
                 to discern whether there are any patterns in the data
                 which could be used for technical trading purposes. A
                 successful buy rule is found which generates returns in
                 excess of what would be expected from the best-fitting
                 null time-series model. Nevertheless, this turns out to
                 be a more sophisticated variant of the buy and hold
                 rule, which the authors term timing specific buy and
                 hold. Although the rule does outperform simple buy and
                 hold, it really does not provide sufficient grounds for
                 the rejection of the efficient market hypothesis,
                 though it does suggest that further investigation of
                 the specific conditions of applicability of the EMH may
                 be appropriate.",
}

@InProceedings{gagne:2002:gecco,
  author =       "Christian Gagn{\'e} and Marc Parizeau",
  title =        "Open {BEAGLE}: {A} New {C++} Evolutionary Computation
                 Framework",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "888",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, poster paper,
                 artificial intelligence, evolutionary computation
                 framework, object oriented genetic programming,
                 software engineering, software tools",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{gagne:2002:gecco:lbp,
  title =        "Open {BEAGLE:} {A} New Versatile {C}++ Framework for
                 Evolutionary Computation",
  author =       "Christian Gagn{\'e} and Marc Parizeau",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "161--168",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 C++ STL GPL",
}

@InProceedings{gaivoronski:1999:MCESAN,
  author =       "Alexei A. Gaivoronski",
  title =        "Modeling of Complex Economic Systems with Agent Nets",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1265--1272",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{galeano:2002:stiosaapgople,
  author =       "G. Galeano and F. Fernandez and M. Tomassini and L.
                 Vanneschi",
  title =        "Studying the influence of Synchronous and Asynchronous
                 parallel {GP} on Programs' Length Evolution",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "1727--1732",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We present a study of parallel and distributed genetic
                 programming models and their relationships with the
                 bloat phenomenon. The experiments that we have
                 performed have also allowed us to find an interesting
                 link between the number of processes, subpopulations
                 and the model we should use when applying parallelism
                 to GP. We study the synchronous and asynchronous
                 version of the island-model in GP domain.",
}

@InProceedings{gallagher:1999:EADNNAIVLSM,
  author =       "John C. Gallagher and Randall D. Beer",
  title =        "Evolution and Analysis of Dynamical Neural Networks
                 for Agents Integrating Vision, Locomotion, and
                 Short-Term Memory",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1273--1280",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{gallagher:1999:REOFPDE,
  author =       "Marcus Gallagher and Marcus Frean and Tom Downs",
  title =        "Real-valued Evolutionary Optimization using a Flexible
                 Probability Density Estimator",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "840--846",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{garces-perez:1996:sflp,
  author =       "Jaime Garces-Perez and Dale A. Schoenefeld and Roger
                 L. Wainwright",
  title =        "Solving Facility Layout Problems Using Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "182--190",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{garcia:1999:efrbcGAPga,
  author =       "Santiago Garcia and Fermin Gonzalez and Luciano
                 Sanchez",
  title =        "Evolving Fuzzy Rule Based Classifiers with {GA-P}: {A}
                 Grammatical Approach",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "203--210",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Combination of grammar based GP and GA-P with fuzzy
                 rules. UCI machine learning databases

                 First author is Santiago García Carbajal",
}

@InCollection{garcia:2000:EMFFASGA,
  author =       "Guillermo Garcia",
  title =        "Estimation of Multiple Fundamental Frequencies in
                 Audio Signals using a Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "153--159",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{gargano:1998:GAfssmsttec,
  author =       "Michael L. Gargano and William Edelson and Olga
                 Koval",
  title =        "A Genetic Algorithm With Feasible Search Space For
                 Minimal Spanning Trees With Time-Dependent Edge Costs",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "495",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{garmendia-doval:1998:etrsf,
  author =       "A. Beatriz Garmendia-Doval and Chilukuri K. Mohan and
                 Mohit K. Prasad",
  title =        "Evolving Tree Representations of Stack Filters",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "103--108",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{Garzon:1997:mDNAc,
  author =       "M. Garzon and P. Neathery and R. Deaton and R. C.
                 Murphy and D. R. Franschetti and S. E. {Stevens Jr.}",
  title =        "A New Metric for {DNA} Computing",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "DNA Computing",
  pages =        "472--478",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{garzon:1999:OSG,
  author =       "Max H. Garzon and Russell J. Deaton and Ken Barnes",
  title =        "On Self-Assembling Graphs in vitro",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1805--1809",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "dna and molecular computing",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{garzon1998:egDNAc,
  author =       "Max Garzon and Rusell Deaton and Luis F. Ni<164>o and
                 Ed Stevens and Michal Wittner",
  title =        "Encoding Genomes for {DNA} Computing",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "684--690",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Unpublished{gathercole:1994:stss,
  author =       "Chris Gathercole and Peter Ross",
  title =        "Some Training Subset Selection Methods for Supervised
                 Learning in Genetic Programming",
  note =         "Presented at ECAI'94 Workshop on Applied Genetic and
                 other Evolutionary Algorithms",
  year =         "1994",
  keywords =     "genetic algorithms, genetic programming, LEF, DSS",
  URL =          "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_dss_paper_resubmitted_to_ecai94workshop.ps.gz",
  size =         "13 pages",
}

@InProceedings{ga94aGathercole,
  author =       "Chris Gathercole and Peter Ross",
  title =        "Dynamic Training Subset Selection for Supervised
                 Learning in Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and Reinhard
                 M{\"a}nner",
  series =       "LNCS",
  volume =       "866",
  pages =        "312--321",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58484-6",
  URL =          "ftp://ftp.dai.ed.ac.uk/pub/ga/94-006.ps.Z",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
  abstract =     "Desctibes how to reduce the number of fitness case
                 evaluations in difficult GP problems by selecting a
                 small subset of the training data. Dynamic Subset
                 Selection can produce better results than GP in less
                 than 20% of the time. Population size of 5,000 and
                 10,000.",
  notes =        "PPSN3",
}

@TechReport{Gathercole,
  author =       "Chris Gathercole and Peter Ross",
  title =        "The {MAX} Problem for Genetic Programming -
                 Highlighting an Adverse Interaction between the
                 Crossover Operator and a Restriction on Tree Depth",
  institution =  "Department of Artificial Intelligence, University of
                 Edinburgh",
  year =         "1995",
  address =      "80 South Bridge, Edinburgh, EH1 1HN, UK

                 ",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.dai.ed.ac.uk:/pub/user/chrisg/max-problem-in-GP.for_submission_to_gp-96.ps.gz",
  size =         "10 pages",
  notes =        "

                 p.s. On a related theme, and only blowing my own
                 trumpet a little bit, I have recently written a paper
                 [Gathercole] (soon to be submitted to GP96) which looks
                 at an unfortunate interaction in GP between the
                 Crossover operator and restrictions on tree size.

                 Its more or less finished

                 Published as Gathercole:1996:aicrtd",
}

@InProceedings{Gathercole:1996:aicrtd,
  author =       "Chris Gathercole and Peter Ross",
  title =        "An Adverse Interaction between Crossover and
                 Restricted Tree Depth in Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "291--296",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_max-problem-in-GP_camera-ready-version.for-GP-96.ps.gz",
  size =         "6 pages",
  notes =        "GP-96, Update of Gathercole. Slides at
                 http://www.dai.ed.ac.uk/students/chrisg/gp96_slides.html

                 {"}penalising large trees appears to work well,
                 especially when it is used only to discriminate between
                 trees that would otherwise have the same fitness.{"}
                 p296",
}

@InProceedings{Gathercole:1997:sp,
  author =       "Chris Gathercole and Peter Ross",
  title =        "Small Populations over Many Generations can beat Large
                 Populations over Few Generations in Genetic
                 Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "111--118",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_for_public_gp97_small_pops.ps.gz",
  notes =        "GP-97 slides at
                 http://www.dai.ed.ac.uk/students/chrisg/gp97/small_pops/slides.html
                 tictactoe, noughts and crosses, uci thyroid",
}

@InProceedings{Gathercole:1997:lef,
  author =       "Chris Gathercole and Peter Ross",
  title =        "Tackling the Boolean Even {N} Parity Problem with
                 Genetic Programming and Limited-Error Fitness",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "119--127",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_for_public_gp97_lef.ps.gz",
  notes =        "GP-97 slides at
                 http://www.dai.ed.ac.uk/students/chrisg/gp97/lef/slides.html",
}

@PhdThesis{gathercole:thesis,
  author =       "Chris Gathercole",
  title =        "An Investigation of Supervised Learning in Genetic
                 Programming",
  school =       "University of Edinburgh",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.dai.ed.ac.uk/pub/daidb/papers/pt9810.ps.gz",
  size =         "207 pages",
  abstract =     "This thesis is an investigation into Supervised
                 Learning (SL) in Genetic Programming (GP). With its
                 flexible tree-structured representation, GP is a type
                 of Genetic Algorithm, using the Darwinian idea of
                 natural selection and genetic recombination, evolving
                 populations of solutions over many generations to solve
                 problems. SL is a common approach in Machine Learning
                 where the problem is presented as a set of examples. A
                 good or fit solution is one which can successfully deal
                 with all of the examples.

                 In common with most Machine Learning approaches, GP has
                 been used to solve many trivial problems. When applied
                 to larger and more complex problems, however, several
                 difficulties become apparent. When focusing on the
                 basic features of GP, this thesis highlights the
                 immense size of the GP search space, and describes an
                 approach to measure this space. A stupendously flexible
                 but frustratingly useless representation, Anarchically
                 Automatically Defined Functions, is described. Some
                 difficulties associated with the normal use of the GP
                 operator Crossover (perhaps the most common method of
                 combining GP trees to produce new trees) are
                 demonstrated in the simple MAX problem. Crossover can
                 lead to irreversible sub-optimal GP performance when
                 used in combination with a restriction on tree size.
                 There is a brief study of tournament selection which is
                 a common method of selecting fit individuals from a GP
                 population to act as parents in the construction of the
                 next generation.

                 The main contributions of this thesis however are two
                 approaches for avoiding the fitness evaluation
                 bottleneck resulting from the use of SL in GP. To
                 establish the capability of a GP individual using SL,
                 it must be tested or evaluated against each example in
                 the set of training examples. Given that there can be a
                 large set of training examples, a large population of
                 individuals, and a large number of generations, before
                 good solutions emerge, a very large number of
                 evaluations must be carried out, often many tens of
                 millions. This is by far the most time-consuming stage
                 of the GP algorithm. Limited Error Fitness (LEF) and
                 Dynamic Subset Selection (DSS) both reduce the number
                 of evaluations needed by GP to successfully produce
                 good solutions, adaptively using the capabilities of
                 the current generation of individuals to guide the
                 evaluation of the next generation. LEF curtails the
                 fitness evaluation of an individual after it exceeds an
                 error limit, whereas DSS picks out a subset of examples
                 from the training set for each generation.

                 Whilst LEF allows GP to solve the comparatively small
                 but difficult Boolean Even N parity problem for large N
                 without the use of a more powerful representation such
                 as Automatically Defined Functions, DSS in particular
                 has been successful in improving the performance of GP
                 across two large classification problems, allowing the
                 use of smaller population sizes, many fewer and faster
                 evaluations, and has more reliably produced as good or
                 better solutions than GP on its own.

                 The thesis ends with an assertion that smaller
                 populations evolving over many generations can perform
                 more consistently and produce better results than the
                 `established' approach of using large populations over
                 few generations.

                 ",
}

@InProceedings{gelenbe:1996:GAas,
  author =       "Erol Gelenbe",
  title =        "Genetic Algorithms with Analytical Solution",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "437--443",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 GA paper",
}

@InProceedings{GeyerSchulz92d,
  crossref =     "Hoehle92",
  author =       "Andreas Geyer--Schulz",
  title =        "Fuzzy Rule Languages and Genetic Algorithms",
  year =         "1992",
  pages =        "36--38",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Proceedings{Hoehle92,
  editor =       "Ulrich H{\"o}hle and Peter Klement",
  booktitle =    "$14^{th}$ Linz Seminar on Fuzzy Set Theory:
                 Non-Classical Logics and their Applications",
  title =        "$14^{th}$ Linz Seminar on Fuzzy Set Theory:
                 Non-Classical Logics and their Applications",
  year =         "1992",
  publisher =    "Johannes Kepler Universit{\"a}t Linz",
  address =      "Linz",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@InProceedings{GeyerSchulz92b,
  crossref =     "Lowen92",
  author =       "Andreas Geyer--Schulz",
  title =        "Fuzzy Classifier Systems",
  year =         "1992",
  pages =        "345--354",
}

@Proceedings{Lowen92,
  editor =       "Robert Lowen and Marc Roubens",
  booktitle =    "Fuzzy Logic: State of the Art",
  title =        "Fuzzy Logic: State of the Art",
  year =         "1993",
  series =       "Series D: System Theory, Knowledge Engineering and
                 Problem Solving",
  organisation = "IFSA",
  publisher =    "Kluwer Academic Publishers",
  address =      "Dordrecht",
}

@InProceedings{GeyerSchulz92c,
  crossref =     "Bandemer92",
  author =       "Andreas Geyer--Schulz",
  title =        "On the Specification of Fuzzy Data in Management",
  year =         "1992",
  pages =        "105--110",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Proceedings{Bandemer92,
  editor =       "Hans Bandemer",
  booktitle =    "Modelling Uncertain Data",
  title =        "Modelling Uncertain Data",
  year =         "1993",
  volume =       "68",
  series =       "Mathematical Research",
  organisation = "GAMM",
  publisher =    "Akademie Verlag",
  address =      "Berlin",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@InProceedings{GeyerSchulz93b,
  crossref =     "Frisch93",
  author =       "Andreas Geyer--Schulz",
  title =        "{Z}ur {B}eschleunigung des {L}ernens genetischer
                 {A}lgorithmen mittels unscharfer {R}egelsprachen",
  year =         "1993",
  pages =        "73--85",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Proceedings{Frisch93,
  editor =       "Walter Frisch and Alfred Taudes",
  booktitle =    "Informationswirtschaft",
  title =        "Informationswirtschaft",
  year =         "1993",
  month =        sep,
  publisher =    "Physica-Verlag",
  address =      "Heidelberg",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Book{GeyerSchulz95a,
  author =       "Andreas Geyer--Schulz",
  title =        "Fuzzy Rule-Based Expert Systems and Genetic Machine
                 Learning",
  publisher =    "Physica-Verlag",
  address =      "Heidelberg",
  year =         "1995",
  volume =       "3",
  series =       "Studies in Fuzziness",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@TechReport{GeyerSchulz95c,
  author =       "Andreas Geyer--Schulz",
  title =        "Genetic Machine Learning",
  institution =  "ACM SIGAPL",
  address =      "New York, N.Y.",
  year =         "1995",
  note =         "Tutorial held at APL'95 at San Antonio, Texas",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@InProceedings{GeyerSchulz96a,
  crossref =     "Herrera96",
  author =       "Andreas Geyer--Schulz",
  title =        "The {M}{I}{T} Beer Distribution Game Revisited:
                 Genetic Machine Learning and Managerial Behavior in a
                 Dynamic Decision Making Experiment",
  year =         "1996",
  pages =        "658--682",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Proceedings{Herrera96,
  editor =       "F. Herrera and J. L. Verdegay",
  booktitle =    "Genetic Algorithms and Soft Computing",
  title =        "Genetic Algorithms and Soft Computing",
  year =         "1996",
  month =        sep,
  volume =       "8",
  series =       "Studies in Fuzziness and Soft Computing",
  organisation = "Physica-Verlag",
  publisher =    "Physica-Verlag",
  address =      "Heidelberg",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Book{GeyerSchulz96b,
  author =       "Andreas Geyer--Schulz",
  title =        "Fuzzy Rule-Based Expert Systems and Genetic Machine
                 Learning",
  publisher =    "Physica-Verlag",
  address =      "Heidelberg",
  year =         "1996",
  volume =       "3",
  series =       "Studies in Fuzziness and Soft Computing",
  edition =      "2nd revised",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Proceedings{Biethahn96,
  editor =       "J. Biethahn and A. H{\"o}hnerloh and J. Kuhl and V.
                 Nissen",
  booktitle =    "Betriebliche Anwendungen von Fuzzy Technologien",
  title =        "Betriebliche Anwendungen von Fuzzy Technologien",
  year =         "1996",
  organisation = "AFN -- Arbeitsgemeinschaft Fuzzy Logik und
                 Softcomputing Norddeutschland",
  publisher =    "Georg-August Universit{\"a}t G{\"o}ttingen, Institut
                 f{\"u}r Wirtschaftsinformatik",
  address =      "G{\"o}ttingen",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@InProceedings{GeyerSchulz96c,
  crossref =     "Biethahn96",
  author =       "Andreas Geyer--Schulz",
  title =        "{D}as {L}ernen von {B}estellregeln in
                 {D}istributionsketten: {E}ine betriebswirtschaftliche
                 {A}nwendung von {F}uzzy {G}enetic {P}rogramming",
  year =         "1996",
  pages =        "92--106",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Article{GeyerSchulz96d,
  author =       "Andreas Geyer--Schulz",
  title =        "Fuzzy Genetic Programming and Dynamic Decision
                 Making",
  journal =      "Proc. ICSE'96",
  year =         "1996",
  month =        jun,
  pages =        "686--691",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Article{GeyerSchulz96e,
  author =       "Andreas Geyer--Schulz",
  title =        "Compound Derivations in Fuzzy Genetic Programming",
  journal =      "Proc. NAFIPS'96",
  year =         "1996",
  month =        jul,
  pages =        "510--514",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Article{GeyerSchulz96f,
  author =       "Andreas Geyer--Schulz",
  title =        "Learning Strategies for Managing New and Innovative
                 Products",
  journal =      "Proc. GfKl'96 - 20. Jahrestagung der Gesellschaft
                 f{\"u}r Klassifikation",
  year =         "1996",
  month =        mar,
  note =         "accepted",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@Article{GeyerSchulz96g,
  author =       "Andreas Geyer--Schulz",
  title =        "Fuzzy Genetic Algorithms",
  journal =      "Handbook of Fuzzy Systems",
  year =         "1996",
  month =        apr,
  note =         "Work in progress",
  keywords =     "Genetic Programming, Genetic Algorithms",
}

@InProceedings{Geyer-Schulz:1997:700,
  author =       "Andreas Geyer-Schulz",
  title =        "The Next 700 Programming Languages for Genetic
                 Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "128--136",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{Ghanea-Hercock:1994:Earca,
  author =       "R. Ghanea-Hercock and A. P Fraser",
  title =        "Evolution of autonomous robot control architectures",
  booktitle =    "Evolutionary Computing",
  publisher =    "Springer-Verlag",
  year =         "1994",
  editor =       "T. C. Fogarty",
  series =       "Lecture Notes in Computer Science",
  address =      "Leeds, UK",
  month =        "11-13 " # apr,
  organisation = "AISB",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Proceedings of the Workshop on Artificial Intelligence
                 and Simulation of Behaviour Workshop on Evolutionary
                 Computing. Workshop in Leeds, UK, April 11-13, 1994",
}

@InProceedings{ghanea-hercock:1999:DGPMA,
  author =       "Robert Ghanea-Hercock and Divine T. Ndumu and Jaron
                 Collis",
  title =        "Distributed Genetic Programming with Mobile Agents",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1441",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, artificial
                 life, adaptive behavior and agents, poster papers",
  ISBN =         "1-55860-611-4",
  abstract =     "java based mobil agents, MATS",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ghozeil:1996:dpspdEP,
  author =       "Adam Ghozeil and David B. Fogel",
  title =        "Discovering Patterns in Spatial Data using
                 Evolutionary Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Evolutionary Programming",
  pages =        "521--527",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 EP paper",
}

@InProceedings{giacobini:2002:gecco,
  author =       "Mario Giacobini and Marco Tomassini and Leonardo
                 Vanneschi",
  title =        "How Statistics Can Help In Limiting The Number Of
                 Fitness Cases In Genetic Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "889",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, poster paper,
                 entropy, fitness Cases, statistics",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{giacobini:ppsn2002:pp371,
  author =       "Mario Giacobini and Marco Tomassini and Leonardo
                 Vanneschi",
  title =        "Limiting the Number Fitness Cases in Genetic
                 Programming Using Statistics",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "371 ff.",
  year =         "2002",
  editor =       "J.-J. Merelo Guerv\'os and P. Adamidis and H.-G. Beyer
                 and J.-L. Fern\'andez-Villaca\~nas and H.-P. Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Keywords: Implementation::Parameter tuning,
                 Technique::Fitness - Evaluation, Technique::Genetic
                 programming - general, Theory of EC::Theory of
                 evolutionary computing - general",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900371.pdf",
}

@InProceedings{giani:1998:spccs,
  author =       "Antonella Giani",
  title =        "A Study of Parallel Cooperative Classifier Systems",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ai.mit.edu/people/unamay/phd-ws-abstracts/gianni.ps",
  notes =        "GP-98LB, GP-98PhD Student Workshop",
}

@InProceedings{gibbs:1996:eikGP,
  author =       "Jonathan Gibbs",
  title =        "Easy Inverse Kinematics using Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "422",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@Article{gibbs:1996:GP96review,
  author =       "W. Wayt Gibbs",
  title =        "Programming with Primordial Ooze",
  journal =      "Scientific American",
  year =         "1996",
  volume =       "275",
  number =       "4",
  pages =        "30--31",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciam.com/1096issue/1096techbus3.html",
  size =         "1 page",
  notes =        "Summary Report on GP96. Notes on papers by Jamie J.
                 Fernandez, Conor Ryan, Brian Howley, Lee Spector and
                 Adrian Thompson",
}

@Article{gibbs:2001:sciam,
  author =       "W. Wayt Gibbs",
  title =        "Cybernetic Cells",
  journal =      "Scientific American",
  year =         "2001",
  volume =       "265",
  number =       "2",
  pages =        "42--47",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "favourable mention of Koza's psb 2001 work
                 koza:2001:PSB",
}

@InCollection{gibbs:2002:IENBCGP,
  author =       "Kevin A. Gibbs",
  title =        "Implementation and Evaluation of a Novel
                 {``}Branch{''} Construct for Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "93--101",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp Artificial ant. Lawn Mower.
                 {"}allowing arbitrary code reuse{"} or {"}potentially
                 infinite number of ADFs{"}. Goto. {"}branch{"} function
                 with {"}random{"} destination p95. Limits on total
                 number of instructions and number of branch
                 instructions, defaults given if limits reached. lilgp.
                 Branch destinations stored as relative offsets into the
                 array of instructions.

                 ",
}

@InProceedings{gigure:1998:psosGA1,
  author =       "Philippe Gigure and David E. Goldberg",
  title =        "Population Sizing for Optimum Sampling with Genetic
                 Algorithms: {A} Case Study of the Onemax Problem",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "496--503",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{gilbert:1998:GPvshdd,
  author =       "Richard J. Gilbert and Royston Goodacre and Beverly
                 Shann and Douglas B. Kell and Janet Taylor and Jem J.
                 Rowland",
  title =        "Genetic Programming-Based Variable Selection for
                 High-Dimensional Data",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "109--115",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{gilbert:1997:,
  author =       "R. J. Gilbert and R. Goodacre and A. M. Woodward and
                 D. B. Kell",
  title =        "Genetic programming: {A} novel method for the
                 quantitative analysis of pyrolysis mass spectral data",
  journal =      "ANALYTICAL CHEMISTRY",
  year =         "1997",
  volume =       "69",
  number =       "21",
  pages =        "4381--4389",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://pubs.acs.org/journals/ancham/article.cgi/ancham/1997/69/i21/pdf/ac970460j.pdf",
  abstract =     "A technique for the analysis of multivariate data by
                 genetic programming (GP) is described, with particular
                 reference to the quantitative analysis of orange juice
                 adulteration data collected by pyrolysis mass
                 spectrometry (PyMS). The dimensionality of the input
                 space was reduced by ranking variables according to
                 product moment correlation or mutual information with
                 the outputs. The GP technique as described gives
                 predictive errors equivalent to, if not better than,
                 more widespread methods such as partial least squares
                 and artificial neural networks but additionally can
                 provide a means for easing the interpretation of the
                 correlation between input and output variables. The
                 described application demonstrates that by using the GP
                 method for analyzing PyMS data the adulteration of
                 orange juice with 10% sucrose solution can be
                 quantified reliably over a 0-20% range with an RMS
                 error in the estimate of ? 1%.",
  notes =        "

                 ",
}

@InProceedings{gilbert:1999:,
  author =       "Richard J. Gilbert and Helen E. Johnson and Michael K.
                 Winson and Jem J. Rowland and Royston Goodacre and
                 Aileen R. Smith and Michael A. Hall and Douglas B.
                 Kell",
  title =        "Genetic Programming as an Analytical Tool for
                 Metabolome Data",
  booktitle =    "Late-Breaking Papers of EuroGP-99",
  year =         "1999",
  editor =       "W. B. Langdon and Riccardo Poli and Peter Nordin and
                 Terry Fogarty",
  pages =        "23--33",
  address =      "Goteborg, Sweden",
  month =        "26-27 " # may,
  organisation = "EvoGP",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic programming, in conjunction with advanced
                 analytical instruments, is a novel tool for the
                 investigation of complex biological systems at the
                 whole-tissue level.

                 In this study, samples from tomato fruit grown
                 hydroponically under both high- and low-salt conditions
                 were analysed using Fourier-transform infrared
                 spectroscopy (FTIR), with the aim of identifying
                 spectral and biochemical features linked to salinity in
                 the growth environment.

                 FTIR spectra are not amenable to direct visual
                 analysis, so supervised machine learning was used to
                 generate models capable of classifying the samples
                 based on their spectral characteristics. The genetic
                 programming (GP) method was chosen, since it has
                 previously been shown to perform with the same accuracy
                 as conventional data modelling methods, but in a
                 readily-interpretable form.

                 Examination of the GP-derived models showed that there
                 was a small number of spectral regions that were
                 consistently being used. In particular, the spectral
                 region containing absorbances potentially due to a
                 cyanide/nitrile functional group was identified as
                 discriminatory. The explanatory power of the GP models
                 enabled a chemical interpretation of the biochemical
                 differences to be proposed. The combination of FTIR and
                 GP is therefore a powerful and novel analytical tool
                 which, in this study, improves our understanding of the
                 biochemistry of salt tolerance in tomato plants.",
  notes =        "EuroGP'99LB part of langdon:1999:egplb",
}

@Misc{gilbert:p450,
  author =       "Richard Gilbert and Kris Birchall and William Bains",
  title =        "Classification of Cytochrome {P450} 3{A4} Ligands
                 Using Genetic Programming",
  year =         "2002",
  email =        "info@amedis-pharma.com",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.amedis-pharma.com/Docs/3A4_ligand_poster.ppt",
  abstract =     "The cytochrome P450 [CYP] family is a set of
                 hæm-containing oxidoreductase enzymes which are
                 involved in the first-pass metabolism of xenobiotic
                 compounds such as drug molecules. CYP 3A4 is the most
                 abundant of these enzymes in humans, and is capable of
                 metabolising approximately 80% of drugs to some extent.
                 As CYP3A4 has a limited capacity, both competing
                 substrates and inhibitors can affect the rate at which
                 CYP3A4 metabolises drugs, and hence the amount of the
                 compound that reaches systemic circulation. Identifying
                 whether a compound is metabolised by CYPs in general,
                 and CYP3A4 in particular, is important for judging its
                 potential as a drug. We describe an approach to the
                 computational identification of CYP3A4 ligands
                 (substrates and inhibitors) that is based on a type of
                 evolutionary computing called genetic programming. The
                 method is a supervised learning system, i.e. it is
                 guided by past examples, in this case actual measured
                 biological data on CYP ligand status. The GP system
                 creates predictive models by Darwinian operations of
                 mutation, crossover and fitness selection, operating on
                 a population of potential solutions. Parent solutions
                 are chosen according to their ability to explain the
                 training data. New models are generated by mutation or
                 crossover, and may replace less-fit individuals already
                 in the population. After sufficient iterations, the
                 population comprises models able to explain the
                 observations much more effectively than the initial
                 random population. Applying this to publicly available
                 CYP3A4 data, we show that we can predict the ligand
                 status of a diverse set of known drugs to approximately
                 90% accuracy, and to predict whether a ligand will be a
                 substrate or an inhibitor to approximately 85%
                 accuracy. The GP method also identifies structural
                 characteristics of the molecule which it is using to
                 build the decision algorithms, and these are consistent
                 with more exhaustive, quantum mechanical predictions of
                 substrate status. The evolutionary nature of GPs allows
                 generation of multiple solutions, which allow
                 statistical validation of the results.",
  notes =        "Amedis Pharmaceuticals Limited, Upton House, Baldock
                 Street, Royston, Herts SG8 5AY, UK",
}

@InCollection{Gillespie:1997:GAspspsd,
  author =       "Jaysen Gillespie",
  title =        "A Genetic Algorithm Solution to the Project Selection
                 Problem Using Static and Dynamic Fitness Functions",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "76--85",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{gillmor:1998:aaaDNAcrUVp,
  author =       "S. D. Gillmor and Q. Liu and L. Wang and C. E. Jordan
                 and A. G. Frutos and A. J. Theil and T. C. Stother and
                 A. E. Condon and R. M. Corn and L. M. Smith and M. G.
                 Lagally",
  title =        "Addressed-Array Approach to {DNA} Computation Readout
                 through {UV} Photopatterning",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InCollection{gleason:2000:TCDDGAGP,
  author =       "Sean Gleason",
  title =        "Tuning and Creation of Discrete Differentiators using
                 Genetic Algorithms and Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "160--169",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{glickman:1998:ea:edsa,
  author =       "Matthew Glickman and Katia Sycara",
  title =        "Evolutionary Algorithms: Exploring the Dynamics of
                 Self-Adaptation",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "762--769",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolutionary programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{glickman:1999:EGBLICE,
  author =       "Matthew R. Glickman and Katia Sycara",
  title =        "Evolution of Goal-Directed Behavior from Limited
                 Information in a Complex Environment",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1281--1288",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{globus:1998:amduet,
  author =       "Al Globus and John Lawtonb and Todd Wipkeb",
  title =        "Automatic molecular design using evolutionary
                 techniques",
  booktitle =    "The Sixth Foresight Conference on Molecular
                 Nanotechnology",
  year =         "1998",
  editor =       "Al Globus and Deepak Srivastava",
  address =      "Westin Hotel in Santa Clara, CA, USA",
  month =        nov # " 12-15, 1998",
  organisation = "Foresight Institute",
  keywords =     "genetic algorithms, genetic programming, ring
                 crossover, graphs, drugs",
  URL =          "http://www.foresight.org/Conferences/MNT6/Papers/Globus/index.html",
  URL =          "http://www.nas.nasa.gov/Research/Reports/Techreports/1999/nas-99-005.html",
  abstract =     "Molecular nanotechnology is the precise,
                 three-dimensional control of materials and devices at
                 the atomic scale. An important part of nanotechnology
                 is the design of molecules for specific purposes. This
                 paper describes early results using genetic software
                 techniques to automatically design molecules under the
                 control of a fitness function. The fitness function
                 must be capable of determining which of two arbitrary
                 molecules is better for a specific task. The software
                 begins by generating a population of random molecules.
                 The population is then evolved towards greater fitness
                 by randomly combining parts of the better individuals
                 to create new molecules. These new molecules then
                 replace some of the worst molecules in the population.
                 The unique aspect of our approach is that we apply
                 genetic crossover to molecules represented by graphs,
                 i.e., sets of atoms and the bonds that connect them. We
                 present evidence suggesting that crossover alone,
                 operating on graphs, can evolve any possible molecule
                 given an appropriate fitness function and a population
                 containing both rings and chains. Prior work evolved
                 strings or trees that were subsequently processed to
                 generate molecular graphs. In principle, genetic graph
                 software should be able to evolve other graph
                 representable systems such as circuits, transportation
                 networks, metabolic pathways, computer networks, etc.",
  notes =        "http://www.foresight.org/Conferences/MNT6/index.html",
}

@InProceedings{Gockel:1997:GAsctg,
  author =       "Nicole Gockel and Martin Keim and Rolf Drechsler and
                 Bernd Becker",
  title =        "A Genetic Algorithm for Sequential Circuit Test
                 Generation based on Symbolic Fault Simulation",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "363--369",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@Unpublished{Gockel:1997:lheavsr,
  author =       "Nicole Gockel and Rolf Drechsler and Bernd Becker",
  title =        "Learning Heuristics by Evolutionary Algorithms with
                 Variable Size Representation",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, Evolvable Hardware, variable size
                 representation",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "3 pages",
}

@InProceedings{Goh:2000:GECCO,
  author =       "Gerard Kian-Meng Goh and James A. Foster",
  title =        "Evolving Molecules for Drug Design Using Genetic
                 Algorithms via Molecular Trees",
  pages =        "27--33",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{goh:2001:gadsacpc,
  author =       "C. Goh and Y. Li",
  title =        "{GA} Automated Design and Synthesis of Analog Circuits
                 with Practical Constrains",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "170--177",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, CAD, Circuit
                 Synthesis, preferred value components, PSpice",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =

                 Fixed length chromosome but inclusion of {"}null{"}
                 makes it effectively variable length but bounded.",
}

@InProceedings{goldberg:1998:good,
  author =       "David E. Goldberg and Una-May O'Reilly",
  title =        "Where does the Good Stuff Go, and Why? How contextual
                 semantics influence program structure in simple genetic
                 programming",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "16--36",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  URL =          "http://www.ai.mit.edu/people/unamay/papers/eurogp.final.ps",
  size =         "21 pages",
  abstract =     "Using deliberately designed primitive sets, we
                 investigate the relationship between context-based
                 expression mechanisms and the size, height and density
                 of genetic program trees during the evolutionary
                 process. We show that contextual semantics influence
                 the composition, location and flows of operative code
                 in a program. In detail we analyze these dynamics and
                 discuss the impact of our findings on micro-level
                 descriptions of genetic programming.",
  notes =        "EuroGP'98 Also presented at the Canadian AI-98
                 Workshop on Evolutionary Computation Schedule, 17 June
                 1998 Simon Fraser University Harbour Center, Canada",
}

@InProceedings{goldberg:1999:OGSH,
  author =       "David E. Goldberg and Siegfried Voessner",
  title =        "Optimizing Global-Local Search Hybrids",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "220--228",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{goldberg:1999:UTEGACP,
  author =       "David E. Goldberg",
  title =        "Using Time Efficiently: Genetic-Evolutionary
                 Algorithms and the Continuation Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "212--219",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{goldfish:1996:nwfmGP,
  author =       "Andrew Goldfish",
  title =        "Noisy Wall-Following and Maze Navigation through
                 Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "423",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@InProceedings{golovkin:1999:PXSAUGA,
  author =       "Igor E. Golovkin and Roberto C. Mancini and Sushil J.
                 Louis",
  title =        "Plasma {X}-ray Spectra Analysis Using Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1529--1534",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{golubski:1999:eNNsmGP,
  author =       "Wolfgang Golubski and Thomas Feuring",
  title =        "Evolving Neural Network Structures by Means of Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "211--220",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@InProceedings{golubski:2002:EuroGP,
  title =        "New Results on Fuzzy Regression by Using Genetic
                 Programming",
  author =       "Wolfgang Golubski",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "308--315",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "In this paper we continue the work on symbolic fuzzy
                 regression problems. That means that we are interesting
                 in finding a fuzzy function <i>f</i> with best matches
                 given data pairs <i>(x<sub>i</sub>,y<sub>i</sub>)</i>
                 <i>1<= i <= k</i> of fuzzy numbers. We use a genetic
                 programming approach for finding a suitable fuzzy
                 function and will present test results about linear,
                 quadratic and cubic fuzzy functions.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@Article{goodacre:1999:dcvbcppmsGP,
  author =       "R. Goodacre and R. J. Gilbert",
  title =        "The detection of caffeine in a variety of beverages
                 using Curie-point pyrolysis mass spectrometry and
                 genetic programming",
  journal =      "The Analyst",
  year =         "1999",
  volume =       "124",
  pages =        "1069--1074",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.rsc.org/CFCart/displayarticlefree.cfm?article=8%2D9%223%24%5D%5EQB%218%27%5D%5CY%28%3C%5C%23R5%3DX4PPL%3D29%23%3C%0A",
  abstract =     "Freeze dried coffee, filter coffee, tea and cola were
                 analysed by Curie-point pyrolysis mass spectrometry
                 (PyMS). Cluster analysis showed, perhaps not
                 surprisingly, that the discrimination between coffee,
                 tea and cola was very easy. However, cluster analysis
                 also indicated that there was a secondary difference
                 between these beverages which could be attributed to
                 whether they were caffeine- containing or
                 decaffeinated. Artificial neural networks (ANNs) could
                 be trained, with the pyrolysis mass spectra from some
                 of the freeze dried coffees, to classify correctly the
                 caffeine status of the unseen spectra of freeze dried
                 coffee, filter coffee, tea and cola in an independent
                 test set. However, the information in terms of which
                 masses in the mass spectrum are important was not
                 available, which is why ANNs are often perceived as a
                 'black box' approach to modelling spectra. By contrast,
                 genetic programs (GPs) could also be used to classify
                 correctly the caffeine status of the beverages, but
                 which evolved function trees (or mathematical rules)
                 enabling the deconvolution of the spectra and which
                 highlighted that m/z 67, 109 and 165 were the most
                 significant massed for this classification. Moreover,
                 the chemical structure of these mass ions could be
                 assigned to the reproducible pyrolytic degradation
                 products from caffeine.",
}

@Article{goodacre:2000:ddabmbscppmsftis,
  author =       "Royston Goodacre and Beverley Shann and Richard J.
                 Gilbert and Éadaoin M. Timmins and Aoife V. McGovern
                 and Bjorn K. Alsberg and Douglas B. Kell and Niall A.
                 Logan",
  title =        "The detection of the dipicolinic acid biomarker in
                 Bacillus spores using Curie-point pyrolysis mass
                 spectrometry and Fourier-transform infrared
                 spectroscopy",
  journal =      "Analytical Chemistry",
  year =         "2000",
  volume =       "72",
  number =       "1",
  pages =        "119--127",
  month =        "1 " # jan,
  publisher =    "American Chamical Society",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Chemometric methods based on rule induction and
                 genetic programming were used to determine the
                 physiological state (vegetative cells or spores)
                 correctly, and these methods produced mathematical
                 rules which could be simply interpreted in biochemical
                 terms. For PyMS it was found that m/z 105 was
                 characteristic and is a pyridine ketonium ion (C6H3ON+)
                 obtained from the pyrolysis of dipicolinic acid
                 (pyridine-2,6-dicarboxylic acid; DPA), a substance
                 found in spores but not in vegetative cells; this was
                 confirmed using pyrolysis-gas chromatography-mass
                 spectrometry. In addition, a pyridine ring vibration at
                 1447 - 1439 cm-1 from DPA was found to be highly
                 characteristic of spores in FT-IR analysis. Thus,
                 although the original datasets recorded hundreds of
                 spectral variables from whole cells simultaneously, a
                 simple biomarker can be used for the rapid and
                 unequivocal detection of spores of these organisms.",
}

@Proceedings{goodman:2001:GECCOlb,
  title =        "Late Breaking Papers at the 2001 Genetic and
                 Evolutionary Computation Conference",
  year =         "2001",
  editor =       "Erik Goodman",
  address =      "San Francisco, California, USA",
  month =        "7-11 " # jul,
  size =         "pages",
}

@InProceedings{gordillo:1997:ocipGPpa,
  author =       "F. Gordillo and A. Bernal",
  title =        "Optimal Control of an Inverted Pendulum Using Genetic
                 Programming: Practical Aspects",
  booktitle =    "ICANNGA97",
  year =         "1997",
  address =      "University of East Anglia, Norwich, UK",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}

@InProceedings{gordillo:1999:ATSDGCA,
  author =       "Francisco Gordillo and Ismael Alcala and Javier
                 Aracil",
  title =        "A Tool for Solving Differential Games with
                 Co-evolutionary Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1535--1542",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{gordon:1994:usni,
  author =       "Benjamin M. Gordon",
  title =        "Exploring the Underlying Structure of Natural Images
                 Through Genetic Programming",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "49--56",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming, MSE, pixels",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{gordon:1999:TGAMPST,
  author =       "V. Scott Gordon and Rebecca Pirie and Adam Wachter and
                 Scottie Sharp",
  title =        "Terrain-Based Genetic Algorithm ({TBGA}): Modeling
                 Parameter Space as Terrain",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "229--235",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{gordon:2001:GPEM,
  author =       "Timothy G. W. Gordon",
  title =        "Book Review: Hardware evolution: automatic design of
                 electronic circuits in reconfigurable hardware by
                 artificial evolution",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "4",
  pages =        "409--411",
  month =        dec,
  keywords =     "genetic algorithms, evolvable hardware",
  ISSN =         "1389-2576",
  notes =        "Book review of ISBN: 3-540-76253-1 Author: Adrian
                 Thompson Publisher: Springer-Verlag London Ltd. 1998.",
}

@InProceedings{gorges-schleuter:1999:AALSES,
  author =       "Martina Gorges-Schleuter",
  title =        "An Analysis of Local Selection in Evolution
                 Strategies",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "847--854",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{gottlieb:1999:EAMKPRBFR,
  author =       "Jens Gottlieb",
  title =        "Evolutionary Algorithms for Multidimensional Knapsack
                 Problems: the Relevance of the Boundary f the Feasible
                 Region",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "787",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{graae:2000:svhrGP,
  author =       "Cristopher T. M. Graae and Peter Nordin and Mats
                 Nordahl",
  title =        "Stereoscopic Vision for a Humanoid Robot Using Genetic
                 Programming",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and George D. Smith
                 and David Corne and Martin Oates and Emma Hart and Pier
                 Luca Lanzi and Egbert Jan Willem and Yun Li and Ben
                 Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "12--21",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67353-9",
  notes =        "EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel,
                 EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland,
                 UK, April 17, 2000
                 Proceedings

                 http://evonet.dcs.napier.ac.uk/evoworkshops/

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67353-9",
}

@InCollection{Graf:Banzhaf:EA95,
  author =       "J. Graf and W. Banzhaf",
  title =        "Interactive Evolution for Simulated Natural
                 Evolution",
  booktitle =    "Artificial Evolution",
  publisher =    "Springer Verlag",
  year =         "1996",
  editor =       "J.-M. Alliot and E. Lutton and E. Ronald and M.
                 Schoenauer and D. Snyers",
  volume =       "1063",
  series =       "LNCS",
  pages =        "259--272",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Selected papers from two conferences: Evolution
                 Artificielle 94 and Evolution Artificielle 95
                 http://www.cmap.polytechnique.fr/www.eark/ea95.html

                 ",
}

@InProceedings{graham:1998:opdidcGA,
  author =       "Jonathan M. Graham",
  title =        "Optimal Placement of Distributed Iterrelated Data
                 Components using Genetic Algorithms",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Article{graham-rowe:1999:elvis,
  author =       "Duncan Graham-Rowe",
  title =        "Elvis Lives",
  journal =      "New Scientist",
  year =         "1999",
  month =        "21 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.newscientist.com/ns/19990821/newsstory4.html",
  size =         "2 pages",
  abstract =     "Description of Peter Nordin humanoid robot Elvis",
}

@Article{graham-rowe:2002:radio,
  author =       "Duncan Graham-Rowe",
  title =        "Radio emerges from the electronic soup",
  journal =      "New Scientist",
  year =         "2002",
  month =        "13 " # aug,
  keywords =     "genetic algorithms, evolvable hardware",
  URL =          "http://www.newscientist.com/news/news.jsp?id=ns99992732",
  size =         "1 page",
  abstract =     "A self-organising electronic circuit has stunned
                 engineers by turning itself into a radio receiver.",
  notes =        "Paul Layzell and Jon Bird at the University of
                 Sussex",
}

@InProceedings{grand:1997:creatures,
  author =       "Stephen Grand and Dave Cliff and Anil Malhotra",
  title =        "Creatures: Artificial Life Autonmous Software Agents
                 for Home Entertainment",
  booktitle =    "The First International Conference on Autonomous
                 Agents (Agents '97)",
  year =         "1997",
  editor =       "W. Lewis Johnson",
  pages =        "22--29",
  address =      "Marina del Rey, California, USA",
  publisher_address = "1515 Broadway, New York, NY 10036, USA",
  month =        feb # " 5-8",
  organisation = "ACM SIGART",
  publisher =    "ACM Press",
  keywords =     "Arificial Life",
  ISBN =         "0-89791-877-0",
  notes =        "http://www.isi.edu/isd/AA97/info.html",
}

@MastersThesis{grant:msc,
  author =       "Michael S. Grant",
  title =        "An Investigation into Genetic Programming",
  school =       "Department of Computer Science and Applied
                 Mathematics, Aston University",
  year =         "1996",
  address =      "Birmingham, UK",
  month =        sep,
  email =        "michael.grant@bbc.co.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.toothworks.co.uk/michael/msc.zip",
  size =         "150 pages",
  abstract =     "An investigation was undertaken of the field of
                 Genetic Programming, an offshoot of Genetic Algorithms.
                 The GP system was implemented in Emacs Lisp. Study was
                 undertaken of three alternative methods of GP - the
                 original method, the Stack system and the Pygmy
                 Algorithm. The implementation of the Stack system was
                 shown to suffer from premature convergence; that of the
                 Pygmy Algorithm was shown under certain conditions to
                 be superior to the original method.

                 A novel problem, that of generating mazes, was
                 implemented and shown to be capable of solution by the
                 GP system and by the Pygmy Algorithm.",
}

@PhdThesis{grant:phd,
  author =       "Michael Sean Grant",
  title =        "An Investigation into the Suitability of Genetic
                 Programming for Computing Visibility Areas for Sensor
                 Planning",
  school =       "Department of Computing and Electrical Engineering,
                 Heriot-Watt University",
  year =         "2000",
  address =      "Riccarton, Edinburgh EH14 4AS, United Kingdom",
  month =        may,
  email =        "michael.grant@bbc.co.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.toothworks.co.uk/michael/phd.zip",
  size =         "293 pages",
  abstract =     "This thesis considers the application of Genetic
                 Programming to visibility space calculation, for Sensor
                 Planning in Machine Vision. This is a problem
                 considerably more complex than most for which GP has
                 been used; no closed-form algorithm for it yet exists
                 in the most general case.

                 The main contributions and results are the application
                 of GP to a new field, and the conclusion that GP is
                 better suited to solve this complex problem by a
                 generate-and-test approach than an analytic one.

                 Three systems were implemented to evolve programs for
                 calculating visibility spaces. The first used untyped
                 GP and low-level operations, for maximum flexibility in
                 evolution, but could solve the problem only for trivial
                 cases.

                 The second used high-level geometric operations and
                 typed GP, but tended to get trapped in local optima.
                 Approaches used, unsuccessfully, to obviate this
                 included altering the fitness cases and function set
                 both statically and dynamically, parameter tuning,
                 seeding the population, using program templates, and
                 using a simpler system for modelling evolution.

                 The third system, which used a generate-and-test
                 approach, evolved useful solutions. When seeded with
                 hand-crafted partial solutions, it was able to improve
                 them considerably.

                 The work shows the potential of GP to evolve or refine
                 a region-growing generate-and-test algorithm for
                 calculating visibility spaces, a problem not hitherto
                 approached by the GP community.",
}

@TechReport{gray:1996:ssi,
  author =       "G. J. Gray and Yun Li and D. J. Murray-Smith and K. C.
                 Sharman",
  title =        "Structural System Identification Using Genetic
                 Programming and a Block Diagram Oriented Simulation
                 Tool",
  institution =  "Department of Electronics and Electrical Engineering,
                 University of Glasgow",
  year =         "1996",
  type =         "Technical Report",
  number =       "CSC-96003",
  address =      "Glasgow, G12 8QQ, U.K.",
  month =        "13 " # jun,
  note =         "Submitted to: Electronics Letters",
  keywords =     "genetic algorithms, genetic programming, system
                 identification, nonlinear mathematical modelling,
                 SIMULINK",
  URL =          "ttp://www.mech.gla.ac.uk/~gary/csc96003.ps",
  abstract =     "Genetic programming can be used for structural
                 optimisation. Combined with a hybrid simplex/simulated
                 annealing algorithm, it is applied to the
                 identification of nonlinear dynamic models from
                 simulated experimental data. Nonlinear models similar
                 to the original test model of the system are identified
                 yielding both correct structures and accurate
                 parameters",
  notes =        "See gray:1996:ssi2

                 ",
}

@Article{gray:1996:ssi2,
  author =       "Gary J. Gray and Yun Li and D. J. Murray-Smith and K.
                 C. Sharman",
  title =        "Structural system identification using genetic
                 programming and a block diagram oriented simulation
                 tool",
  journal =      "Electronics Letters",
  year =         "1996",
  volume =       "32",
  number =       "15",
  pages =        "1422--1424",
  month =        "18 " # jul,
  keywords =     "genetic algorithms, genetic programming, structural
                 system identification, block diagram, simulation tool,
                 structural optimisation, hybrid simplex/simulated
                 annealing algorithm, nonlinear dynamic model,
                 identification, simulation, simulated annealing,
                 nonlinear dynamical systems",
  ISSN =         "0013-5194",
  URL =          "http://ieeexplore.ieee.org/iel1/2220/11173/00511160.pdf?isNumber=11173",
  abstract =     "Genetic programming can be used for structural
                 optimisation. Combined with a hybrid simplex/simulated
                 annealing algorithm, it is applied to the
                 identification of nonlinear dynamic models from
                 simulated experimental data. Nonlinear models similar
                 to the original test model of the system are
                 identified, yielding both correct structures and
                 accurate parameters.",
  notes =        "See also gray:1996:ssi SIMULINK, MATLAB. Numerical
                 parameters optimised using combination of Nelder
                 simplex minimisation and simulated annealing.
                 A.P.Fraser's gpc++.",
}

@InProceedings{gray:1996:nmsti,
  author =       "Gary J. Gray and David J. Murray-Smith and Yun Li and
                 Ken C. Sharman",
  title =        "Nonlinear Model Structure Identification Using Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "32--37",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.mech.gla.ac.uk/~gary/tankgp96.ps",
  abstract =     "Genetic programming can be used to evolve an algebraic
                 expression as part of an equation representing measured
                 inputoutput response data. Parts of the nonlinear
                 differential equations describing a dynamic system are
                 identified along with their numerical parameters using
                 genetic programming. The results of several such
                 optimisations are analysed to produce a nonlinear
                 physical representation of the dynamic system. This
                 method is applied to the identification of fluid flow
                 through pipes in a coupled water tank system. A
                 representative nonlinear model is identified.",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670

                 See also FACULTY OF ENGINEERING, GLASGOW G12 8QQ, U.K.
                 TECHNICAL REPORT: CSC-96xxx",
}

@InProceedings{gray:1997:,
  author =       "G. J. Gray and T. Weinbrenner and D. J. Murray-Smith
                 and Y. Li and K. C. Sharman",
  title =        "Issues in Nonlinear Model Structure Identification
                 Using Genetic Programming",
  booktitle =    "Second International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1997",
  editor =       "Ali Zalzala",
  pages =        "308--313",
  address =      "University of Strathclyde, Glasgow, UK",
  publisher_address = "Savoy Place, London WC2R 0BL, UK",
  month =        "1-4 " # sep,
  publisher =    "Institution of Electrical Engineers",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GALESIA'97",
}

@Article{Gray:1998:CEP,
  author =       "Gary J. Gray and David J. Murray-Smith and Yun Li and
                 Ken C. Sharman and Thomas Weinbrenner",
  title =        "Nonlinear model structure identification using genetic
                 programming",
  journal =      "Control Engineering Practice",
  volume =       "6",
  pages =        "1341--1352",
  year =         "1998",
  number =       "11",
  keywords =     "genetic algorithms, genetic programming, nonlinear
                 models, system identification, helicopter dynamics,
                 Nonlinear control systems, Identification (control
                 systems), Mathematical programming, Differential
                 equations, Error analysis, Mathematical models,
                 Computer simulation, Water tanks, Helicopter rotors,
                 Speed control, Control system analysis",
  URL =          "http://www.sciencedirect.com/science/article/B6V2H-3W1GPR8-4/1/047d9c74e28a6a1a117a3ed9a6d6c409",
  abstract =     "Genetic Programming is an optimisation procedure which
                 may be applied to the identification of the nonlinear
                 structure of a dynamic model from experimental data. In
                 such applications, the model structure may be described
                 either by differential equations or by a block diagram
                 and the algorithm is configured to minimise the sum of
                 the squares of the error between the recorded
                 experimental response from the real system and the
                 corresponding simulation model output. The technique
                 has been applied successfully to the modelling of a
                 laboratory scale process involving a coupled water tank
                 system and to the identification of a helicopter rotor
                 speed controller and engine from flight test data. The
                 resulting models provide useful physical insight.",
}

@InProceedings{gray:1996:GPcbtNMR,
  author =       "H. F. Gray and R. J. Maxwell and I. Martinez-Perez and
                 C. Arus and S. Cerdan",
  title =        "Genetic Programming for Classification of Brain
                 Tumours from Nuclear Magnetic Resonance Biopsy
                 Spectra",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "424",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@InProceedings{Gray:1997:GPmcMRS,
  author =       "H. F. Gray and R. J. Maxwell",
  title =        "Genetic Programming for Multi-class Classification of
                 Magnetic Resonance Spectroscopy Data",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "137",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{gray:1997:GPcmd,
  author =       "Helen Gray",
  title =        "Genetic Programming for Classification of Medical
                 Data",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "291",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@Article{gray:1998:GPcfs:aNMRshbtb,
  author =       "Helen F. Gray and Ross J. Maxwell and Irene
                 Martinez-Perez and Carles Arus and Sebastian Cerdan",
  title =        "Genetic programming for classification and feature
                 selection: analysis of {1H} nuclear magnetic resonance
                 spectra from human brain tumour biopsies",
  journal =      "NMR Biomedicine",
  year =         "1998",
  volume =       "11",
  number =       "4-5",
  pages =        "217--224",
  month =        jun # "-" # aug,
  keywords =     "genetic algorithms, genetic programming, brain tumour,
                 artificial intelligence, classification, feature
                 selection",
  abstract =     "Genetic programming (GP) is used to classify tumours
                 based on 1H nuclear magnetic resonance (NMR) spectra of
                 biopsy extracts. Analysis of such data would ideally
                 give not only a classification result but also indicate
                 which parts of the spectra are driving the
                 classification (i.e. feature selection). Experiments on
                 a database of variables derived from 1H NMR spectra
                 from human brain tumour extracts (n = 75) are reported,
                 showing GP's classification abilities and comparing
                 them with that of a neural network. GP successfully
                 classified the data into meningioma and non-meningioma
                 classes. The advantage over the neural network method
                 was that it made use of simple combinations of a small
                 group of metabolites, in particular glutamine,
                 glutamate and alanine. This may help in the choice of
                 the most informative NMR spectroscopy methods for
                 future non-invasive studies in patients.",
  notes =        "PMID: 9719576, UI: 98384081 Computer Science
                 Department, Arhus University, Denmark.",
}

@InProceedings{Greeff:1997:eemmps,
  author =       "D. J. Greeff and C. Aldrich",
  title =        "Evolution of Empirical Models for Metallurgical
                 Process Systems",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "138",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{greene:1998:dasdd,
  author =       "Buster Greene",
  title =        "A Deterministic Analysis of Stationary
                 Diploid/Dominance",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "770--776",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolutionary programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@PhdThesis{greene:thesis,
  author =       "Francis Greene",
  title =        "Genetic Synthesis of Signal Processing Networks
                 Utilizing Diploid/Dominance",
  school =       "Department of Electrical Engineering. University of
                 Washington",
  year =         "1997",
  address =      "Seattle, USA",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@InProceedings{Greene:2000:GECCO,
  author =       "William A. Greene",
  title =        "A Non-Linear Schema Theorem for Genetic Algorithms",
  pages =        "189--194",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{greene:2001:NBAGA,
  author =       "William A. Greene",
  title =        "Non-Linear Bit Arrangements in Genetic Algorithms",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "138--144",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms",
  notes =        "GECCO-2001LB. Two dimensional grid chromosome, three_D
                 cubes, complete binary tree. Follows up
                 Greene:2000:GECCO 576bit onemax. eight queens problem
                 (also 20 queens). Three target binary trees (all 9
                 levels, full, each node labelled with 0 or 1). Twins,
                 Palindrome trees.",
}

@InCollection{greenfield:2000:ECAPHE,
  author =       "Aaron Greenfield",
  title =        "Evolution of Communication Among Prey in a Hostile
                 Environment",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "170--179",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{greenwold:2000:AGG,
  author =       "Simon M. Greenwold",
  title =        "{AGENCY} {GP}: Genetic programming for architectural
                 design",
  booktitle =    "Graduate Student Workshop",
  year =         "2000",
  editor =       "Conor Ryan and Una-May O'Reilly and William B.
                 Langdon",
  pages =        "273--276",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{Greenwood:1997:chaosES,
  author =       "Garrison W. Greenwood",
  title =        "Experimental Observation of Chaos in Evolution
                 Strategies",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "evolutionary programming and evolution strategies",
  pages =        "439--444",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@Article{greenwood:2001:bicm,
  author =       "Garrison W. Greenwood",
  title =        "Book Review: Bio-Inspired Computing Machines: Towards
                 Novel Computational Architectures",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "1",
  pages =        "75--78",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 programming, evolution strategies, evolvable hardware,
                 FPGA, L-Systems",
  ISSN =         "1389-2576",
  notes =        "review of mange:1998:bicm",
}

@Unpublished{grefenstette:1997:vivposn,
  author =       "John Grefenstette and Kenneth {De Jong} and Connie
                 Ramsey and Annie Wu",
  title =        "The Virtual Virus Project",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, variable size representation",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "1 page",
}

@InProceedings{gregory:1998:GAoddq,
  author =       "Michael Gregory",
  title =        "Genetic Algorithm Optimisation of Distributed Database
                 Queries",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "271--276",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c047.pdf",
  size =         "6 pages",
  abstract =     "Distributed relational database query optimisation is
                 a combinatorial optimisation problem. This paper
                 reports on an initial investigation into the potential
                 for a genetic algorithm (GA) to optimise distributed
                 queries. A genetic algorithm is developed and its
                 performance compared with alternative stochastic
                 optimisation techniques: random search, multistart, and
                 simulated annealing. The problem of fully reducing all
                 tables in a tree query is used to compare the
                 techniques. For this problem, evaluating the fitness
                 function is an expensive operation. The proposed GA
                 uses a tree-structured data model with tailored
                 crossover and mutation operators that avoid the need to
                 fully re-evaluate the fitness function for new
                 solutions. Query optimisation is a task that must be
                 performed in real-time. A technique is required that
                 performs well at the start of a search, but avoids the
                 problem of premature convergence. The proposed GA uses
                 a local search phase to deliver the required real-time
                 performance. Experiments show that the proposed GA can
                 perform better than the alternative techniques tested.
                 The potential for a GA to deliver valuable distributed
                 query processing cost reductions is demonstrated.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@InProceedings{grimbleby:1995:,
  author =       "B. Grimbleby",
  title =        "An automatic Analogue Network Synthesis using Genetic
                 Algoriths",
  booktitle =    "First International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1995",
  editor =       "A. M. S. Zalzala",
  volume =       "414",
  pages =        "53--58",
  address =      "Sheffield, UK",
  publisher_address = "London, UK",
  month =        "12-14 " # sep,
  publisher =    "IEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-85296-650-4",
  notes =        "12--14 September 1995, Halifax Hall, University of
                 Sheffield, UK see also
                 http://www.iee.org.uk/LSboard/Conf/program/galprog.htm

                 Evolves passive analogue circuits using a fixed length
                 GA which allows non-ops to specify network connectivity
                 and components forming links.

                 {"}Even a small ammout of cross-over provides
                 considerable efficiency benefits{"} [page 55]",
}

@InProceedings{grimes:1995:gtprtm,
  author =       "C. A. Grimes",
  title =        "Application of Genetic Techniques to the Planning of
                 Railway Track Maintenance Work",
  booktitle =    "First International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1995",
  editor =       "A. M. S. Zalzala",
  volume =       "414",
  pages =        "467--472",
  address =      "Sheffield, UK",
  publisher_address = "London, UK",
  month =        "12-14 " # sep,
  publisher =    "IEE",
  keywords =     "genetic algorithms, genetic programming, scheduling,
                 maintenance, PC-MARPAS",
  ISBN =         "0-85296-650-4",
  abstract =     "Track maintenance work was planned using GA and GP,
                 with profit as the optimisation criteria. The results
                 where compared with an existing determinstic
                 technique.

                 It was found the GP method gave the best results, with
                 the GA method giving good results for a short section
                 (10 miles) and poor results for a long section (50
                 miles).",
  notes =        "12--14 September 1995, Halifax Hall, University of
                 Sheffield, UK see also
                 http://www.iee.org.uk/LSboard/Conf/program/galprog.htm

                 ",
}

@Article{gritz:1995:GPafm,
  author =       "L. Gritz and J. K. Hahn",
  title =        "Genetic Programming for Articulated Figure Motion",
  journal =      "Journal of Visualization and Computer Animation",
  year =         "1995",
  volume =       "6",
  number =       "3",
  pages =        "129--142",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.gwu.edu/pub/graphics/papers/gpafm.ps",
  abstract =     "

                 Three dimensional computer animation has become
                 increasingly popular over the past decade. Computer
                 animation now has an important role in entertainment,
                 education, and simulation. For computer animation of
                 characters, the role of the animator has unfortunately
                 stayed similar to that of a stop motion animator,
                 rather than like a film director. Research in computer
                 animation has tried to address this by giving higher
                 levels of control to the animator, but these methods
                 often result in lack of fine control over the animated
                 characters. This is inadequate because fine control is
                 essential to both aesthetics and the ability of the
                 animator to direct a meaningful narrative. This
                 dissertation presents methods of articulated figure
                 motion control which attempt to bridge the gap between
                 high level direction and low level control of subtle
                 motion. These methods define motion in terms of goals
                 and ratings. The agents are dynamically-controlled
                 robots whose behavior is determined by robotic
                 controller programs. The controller programs for the
                 robots are evaluated at each time step to yield torque
                 values which drive the dynamic simulation of the
                 motion. We use the AI technique of Genetic Programming
                 (GP) to automatically derive control programs for the
                 agents which achieve the goals. This type of motion
                 specification is an alternative to key framing which
                 allows a highly automated, learning-based approach to
                 generation of motion. This method of motion control is
                 very general (it can be applied to any type of motion),
                 yet it allows for specifications of the types of
                 specific motion which are desired for a high quality
                 animation. We show that complex, specific, physically
                 plausible, and aesthetically appealing motion can be
                 generated using these methods. Both skill-based and
                 action-based motion can be specified in this manner. We
                 also introduce the new paradigm of key marks, a
                 generalization of key framing which is not subject to
                 many of the limitations of key framing.",
  notes =        "

                 Larry Gritz is at Pixar:
                 http://www.seas.gwu.edu/~graphics/papers/gritzdissert.html
                 http://www.seas.gwu.edu/student/gritz/index.html

                 James Hahn is at the George Washington University:
                 http://www.seas.gwu.edu/facu lty/hahn/

                 ",
}

@InProceedings{Gritz:1997:GPec3da,
  author =       "Larry Gritz and James K. Hahn",
  title =        "Genetic Programming Evolution of Controllers for 3-{D}
                 Character Animation",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "139--146",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www.seas.gwu.edu/student/gritz/papers/gritz-gp97/gritz-gp97.ps.gz",
  abstract =     "The dominant paradigm for 3-D character animation
                 requires an animator to specify the values for all
                 degrees of freedom of an articulated figure at key
                 frames. Specifying motion that is physically believable
                 and biologically plausible is a tedious practice
                 requiring great skill. We use evolutionary techniques
                 (specifically Genetic Programming) as a means of
                 controller synthesis for character animation.
                 Controllers which drive a dynamic simulation of the
                 character are evolved using the goals of the animation
                 as an objective function, resulting in physically
                 plausible motion. We discuss the development of
                 objective functions used to guide the controller
                 evolution, making reusable skill controllers, and
                 comparisons of the convergence rates for different
                 parameters of the evolutionary runs.",
  notes =        "GP-97 S-expression per degree of freedom in each joint
                 in the character. Joints controlled by proportional
                 derivative (PD) controllers. Aninmated desk lamp Luxo,
                 Jr. L*xo 4 links and 3 internally controllable degrees
                 of freedom. Robust = reusable. Randomly generated test
                 cases",
}

@PhdThesis{gritz:dissertation,
  author =       "Larry Israel Gritz",
  title =        "Evolutionary Controller Synthesis for 3-{D} Character
                 Animation",
  school =       "The George Washington University",
  year =         "1999",
  address =      "Washington, DC, USA",
  month =        "15 " # may,
  keywords =     "genetic algorithms, genetic programming, computer
                 animation",
  URL =          "http://www.seas.gwu.edu/~graphics/papers/gritzdissert.html",
  size =         "113 pages",
  abstract =     "Three dimensional computer animation has become
                 increasingly popular over the past decade. Computer
                 animation now has an important role in entertainment,
                 education, and simulation. For computer animation of
                 characters, the role of the animator has unfortunately
                 stayed similar to that of a stop motion animator,
                 rather than like a film director. Research in computer
                 animation has tried to address this by giving higher
                 levels of control to the animator, but these methods
                 often result in lack of fine control over the animated
                 characters. This is inadequate because fine control is
                 essential to both aesthetics and the ability of the
                 animator to direct a meaningful narrative. This
                 dissertation presents methods of articulated figure
                 motion control which attempt to bridge the gap between
                 high level direction and low level control of subtle
                 motion. These methods define motion in terms of goals
                 and ratings. The agents are dynamically-controlled
                 robots whose behavior is determined by robotic
                 controller programs. The controller programs for the
                 robots are evaluated at each time step to yield torque
                 values which drive the dynamic simulation of the
                 motion. We use the AI technique of Genetic Programming
                 (GP) to automatically derive control programs for the
                 agents which achieve the goals. This type of motion
                 specification is an alternative to key framing which
                 allows a highly automated, learning-based approach to
                 generation of motion. This method of motion control is
                 very general (it can be applied to any type of motion),
                 yet it allows for specifications of the types of
                 specific motion which are desired for a high quality
                 animation. We show that complex, specific, physically
                 plausible, and aesthetically appealing motion can be
                 generated using these methods. Both skill-based and
                 action-based motion can be specified in this manner. We
                 also introduce the new paradigm of key marks, a
                 generalization of key framing which is not subject to
                 many of the limitations of key framing.",
}

@InProceedings{gronroos:1999:ACSMENN,
  author =       "Marko Gronroos",
  title =        "A Comparison of Some Methods for Evolving Neural
                 Networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1442",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{gross:2002:gecco,
  author =       "R. Gross and K. Albrecht and W. Kantschik and W.
                 Banzhaf",
  title =        "Evolving Chess Playing Programs",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "740--747",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, chess,
                 distributed computing, evolution strategies",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@TechReport{Gruau:1992:cegNN,
  author =       "F Gruau",
  title =        "Cellular encoding of Genetic Neural Networks",
  institution =  "Laboratoire de l'Informatique du Parallilisme. Ecole
                 Normale Supirieure de Lyon",
  year =         "1992",
  type =         "Technical report",
  number =       "92-21",
  address =      "France",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Gruau92,
  author =       "Frederic Gruau",
  title =        "Genetic Synthesis of Boolean Neural Networks with a
                 Cell Rewriting Developmental Process",
  booktitle =    "Proceedings of the Workshop on Combinations of Genetic
                 Algorithms and Neural Networks (COGANN92)",
  editor =       "J. D. Schaffer and D. Whitley",
  publisher =    "The IEEE Computer Society Press",
  pages =        "55--74",
  year =         "1992",
  keywords =     "genetic algorithms, connectionism, neural networks",
}

@Article{Gruau93,
  author =       "Frederic Gruau",
  editor =       "Simon Lucas",
  title =        "Cellular encoding as a graph grammar",
  journal =      "IEE Colloquium on Grammatical Inference: Theory,
                 Applications and Alternatives",
  volume =       "(Digest No.092)",
  pages =        "17/1--10",
  publisher =    "IEE",
  address =      "London",
  month =        "22-23 " # apr,
  year =         "1993",
  keywords =     "genetic algorithm connectionism neural networks
                 cogann",
  abstract =     "ABSTRACT Cellular encoding is a method for encoding a
                 family of neural networks into a set of labeled trees.
                 Such sets of trees can be evolved by the genetic
                 algorithm so as to find a particular set of trees that
                 encodes a family of Boolean neural networks for
                 computing a family of Boolean functions. Cellular
                 encoding is presented as a graph grammar. A method is
                 proposed for translating a cellular encoding into a set
                 of graph grammar rewriting rules of the kind used in
                 the Berlin algebraic approach to graph rewriting. The
                 genetic search of neural networks via cellular encoding
                 appears as a grammatical inference process where the
                 language to parse is implicitly specified, instead of
                 explicitly by positive and negative examples.
                 Experimental results shows that the genetic algorithm
                 can infer grammars that derive neural networks for the
                 parity, symmetry and decoder Boolean function of
                 arbitrary large size.",
}

@InProceedings{icga93:gruau,
  author =       "Frederic Gruau",
  title =        "Genetic Synthesis of Modular Neural Networks",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  pages =        "318--325",
  month =        "17-21 " # jul,
  address =      "University of Illinois at Urbana-Champaign",
  keywords =     "genetic algorithms, genetic programming",
  size =         "8 pages",
  notes =        "

                 ",
}

@PhdThesis{Gruau:1994:thesis,
  author =       "F. Gruau",
  title =        "Neural Network Synthesis using Cellular Encoding and
                 the Genetic Algorithm.",
  school =       "Laboratoire de l'Informatique du Parallilisme, Ecole
                 Normale Supirieure de Lyon",
  year =         "1994",
  address =      "France",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://lip.ens-lyon.fr/pub/Rapports/PhD/PhD94-01-E.ps.Z",
  url2 =         "ftp://lip.ens-lyon.fr/pub/Rapports/PhD/PhD94-01-F.ps.Z",
  size =         "151 pages",
  abstract =     "Artificial neural networks used to be considered only
                 as a machine that learns using small modifications of
                 internal parameters. Now this is changing. Such
                 learning method do not allow to generate big neural
                 networks for solving real world problems. This thesis
                 defends the following three points:

                 (1) The key word to go out of that dead-end is
                 {"}modularity{"}. (2) The tool that can generate
                 modular neural networks is cellular encoding. (3) The
                 optimization algorithm adapted to the search of
                 cellular codes is the genetic algorithm.

                 The first point is now a common idea. A modular neural
                 network means a neural network that is made of several
                 sub-networks, arranged in a hierarchical way. For
                 example, the same sub-network can be repeated. This
                 thesis encompasses two parts.

                 The first part demonstrates the second point. Cellular
                 encoding is presented as a machine language for neural
                 networks, with a theoretical basis (it is a parallel
                 graph grammar that checks a number of properties) and a
                 compiler of high level language. The second part of the
                 thesis shows the third point. Application of genetic
                 algorithm to the synthesis of neural networks using
                 cellular encoding is a new technology. This technology
                 can solve problems that were still unsolved with neural
                 networks. It can automatically and dynamically
                 decompose a problem into a hierarchy of sub-problems,
                 and generate a neural network solution to the problem.
                 The structure of this network is a hierarchy of
                 sub-networks that reflects the structure of the
                 problem. The technology allows to experience new
                 scientific domains like the interaction between
                 learning and evolution, or the set up of learning
                 algorithms that suit the GA.",
  notes =        "

                 ",
}

@InCollection{kinnear:gruau,
  title =        "Genetic micro programming of Neural Networks",
  author =       "Frederic Gruau",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "495--518",
  keywords =     "genetic algorithms, genetic programming",
  chapter =      "24",
  size =         "25 pages",
}

@TechReport{Gruau:1993:ceNNile,
  author =       "F Gruau and D. Whitley",
  title =        "The cellular development of neural networks: The
                 interaction of learning and evolution",
  institution =  "Laboratoire de l'Informatique du Parallilisme, Ecole
                 Normale Supirieure de Lyon",
  year =         "1993",
  type =         "Technical report",
  number =       "93-04",
  address =      "France",
  keywords =     "genetic algorithms, genetic programming",
}

@Article{Gruau:1993:alcdp,
  author =       "F Gruau and D. Whitley",
  title =        "Adding learning to the cellular development process: a
                 comparative study",
  journal =      "Evolutionary Computation",
  year =         "1993",
  volume =       "1",
  number =       "3",
  pages =        "213--233",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{gruau:1995:plad,
  author =       "Frederic Gruau and Darrell Whitley",
  title =        "A Programming Language for Artificial Development",
  booktitle =    "Evolutionary Programming {IV} Proceedings of the
                 Fourth Annual Conference on Evolutionary Programming",
  year =         "1995",
  editor =       "John Robert McDonnell and Robert G. Reynolds and David
                 B. Fogel",
  pages =        "415--434",
  address =      "San Diego, CA, USA",
  month =        "1-3 " # mar,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, Neural Networks, parellel
                 architectures",
  ISBN =         "0-262-13317-2",
  size =         "20 pages",
  notes =        "EP-95, Extension of cellular encoding. Says can build
                 neural network that can emulate any functional language
                 (eg SISAL).",
}

@Article{gruau:1995:admnn,
  author =       "Frederic Gruau",
  title =        "Automatic Definition of Modular Neural Networks",
  journal =      "Adaptive Behaviour",
  year =         "1995",
  volume =       "3",
  number =       "2",
  pages =        "151--183",
  keywords =     "genetic algorithm, genetic programming, animats,
                 cellular encoding, modularity, locomotion, automatic
                 definition of neural subnetworks",
  notes =        "ANN for controlling six legged robot locomotion",
}

@InCollection{gruau:1996:aigp2,
  author =       "Frederic Gruau",
  title =        "On Using Syntactic Constraints with Genetic
                 Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "377--394",
  chapter =      "19",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
}

@InProceedings{gruau:1996:ceVdeGNN,
  author =       "Frederic Gruau and Darrell Whitley and Larry Pyeatt",
  title =        "A Comparison between Cellular Encoding and Direct
                 Encoding for Genetic Neural Networks",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "81--89",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@TechReport{gruau:1996:ceier,
  author =       "Frederic Gruau and Kameel Quatramaran",
  title =        "Cellular Encoding for Interactive Evolutionary
                 Robotics",
  institution =  "School of Cognitive and Computing Sciences, University
                 of Sussex",
  year =         "1996",
  type =         "Cognitive Science Research Paper",
  number =       "425",
  address =      "Falmer, Brighton, Sussex, UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp425.ps.Z",
  url_2 =        "http://www.cogs.susx.ac.uk/cgi-bin/htmlcogsreps?csrp425",
  abstract =     "This work reports experiments in interactive
                 evolutionary robotics. The goal is to evolve an
                 Artificial Neural Network (ANN) to control the
                 locomotion of an 8-legged robot. The ANNs are encoded
                 using a cellular developmental process called cellular
                 encoding. In a previous work similar experiments have
                 been carried on successfully on a simulated robot. They
                 took however around 1 million different ANN
                 evaluations. In this work the fitness is determined on
                 a real robot, and no more than a few hundreds
                 evaluations can be performed. Various ideas were
                 implemented so as to decrease the required number of
                 evaluations from 1 million to 200. First we used cell
                 cloning and link typing. Second we did as many things
                 as possible interactively: interactive problem
                 decomposition, interactive syntactic constraints,
                 interactive fitness. More precisely: 1- A modular
                 design was chosen where a controller for an individual
                 leg, with a precise neuronal interface was developed.
                 2- Syntactic constraints were used to promote useful
                 building blocs and impose an 8-fold symmetry. 3- We
                 determine the fitness interactively by hand. We can
                 reward features that would otherwise be very difficult
                 to locate automatically. Interactive evolutionary
                 robotics turns out to be quite successful, in the first
                 bug-free run a global locomotion controller that is
                 faster than a programmed controller could be evolved.",
  size =         "23 pages",
}

@InCollection{Gruau:EA95,
  author =       "F. Gruau",
  title =        "Modular Genetic Neural Networks for Six-Legged
                 Locomotion",
  booktitle =    "Artificial Evolution",
  publisher =    "Springer Verlag",
  year =         "1996",
  editor =       "J.-M. Alliot and E. Lutton and E. Ronald and M.
                 Schoenauer and D. Snyers",
  volume =       "1063",
  series =       "LNCS",
  pages =        "201--219",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Selected papers from two conferences: Evolution
                 Artificielle 94 and Evolution Artificielle 95
                 http://www.cmap.polytechnique.fr/www.eark/ea95.html

                 ",
}

@InProceedings{guerra-salcedo:1998:gsfss,
  author =       "C<130>sar Guerra-Salcedo and Darrell Whitley",
  title =        "Genetic Search for Feature Subset Selection: {A}
                 Comparison Between {CHC} and {GENESIS}",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "504--509",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{guerra-salcedo:1999:GAFSEC,
  author =       "Cesar Guerra-Salcedo and Darrell Whitley",
  title =        "Genetic Approach to Feature Selection for Ensemble
                 Creation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "236--243",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, data
                 mining",
  ISBN =         "1-55860-611-4",
  abstract =     "boosting and bagging",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 CHC cataclysmic mutation, uniform crossover. EDT,
                 k-means (KMA) Statlog and UCI, LandSat DNA Segment. Big
                 study, difficult to follow. Lots of references.

                 ",
}

@InProceedings{guigue:1999:SALGA,
  author =       "Alexis Guigue and Sofiane Oussedik and Daniel
                 Delahaye",
  title =        "Sequencing Aircraft Landings by Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "788",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{gupta:2000:CGGUGP,
  author =       "Binod Gupta",
  title =        "Context-Free Grammar Generation Using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "180--187",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InCollection{gurganious:1999:ABWEUGA,
  author =       "Darryl Gurganious",
  title =        "Adaptive Beamformer Weight Estimation Using Genetic
                 Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "49--57",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{gustafson:2000:GAK,
  author =       "Steven M. Gustafson and William H. Hsu",
  title =        "Genetic programming for strategy learning in soccer
                 playing agents: {A} {KDD}-based architecture",
  booktitle =    "Graduate Student Workshop",
  year =         "2000",
  editor =       "Conor Ryan and Una-May O'Reilly and William B.
                 Langdon",
  pages =        "277--280",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{gustafson:2001:EuroGP,
  author =       "Steven M. Gustafson and William H. Hsu",
  title =        "Layered Learning in Genetic Programming for a
                 Co-operative Robot Soccer Problem",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "291--301",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Layered
                 Learning, Hierarchical abstractions, Robot soccer,
                 Robots, Multiagent systems",
  ISBN =         "3-540-41899-7",
  size =         "11 pages",
  abstract =     "We present an alternative to standard genetic
                 programming (GP) that applies layered learning
                 techniques to decompose a problem. GP is applied to
                 subproblems sequentially, where the population in the
                 last generation of a subproblem is used as the initial
                 population of the next subproblem. This method is
                 applied to evolve agents to play keep-away soccer, a
                 subproblem of robotic soccer that requires cooperation
                 among multiple agents in a dynnamic environment. The
                 layered learning paradigm allows GP to evolve better
                 solutions faster than standard GP. Results show that
                 the layered learning GP outperforms standard GP by
                 evolving a lower fitness faster and an overall better
                 fitness. Results indicate a wide area of future
                 research with layered learning in GP.

                 ",
  notes =        "EuroGP'2001, part of miller:2001:gp. See also
                 gustafson:mastersthesis",
}

@MastersThesis{gustafson:mastersthesis,
  author =       "Steven M. Gustafson",
  title =        "Layered learning in genetic programming for a
                 co-operative robot soccer problem",
  school =       "Kansas State University",
  year =         "2000",
  address =      "Manhattan, KS, USA",
  month =        dec,
  email =        "smg@cs.nott.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.nott.ac.uk/~smg",
  notes =        "Related Publications from Masters Thesis:

                 William H. Hsu and Steven M. Gustafson. Wrappers for
                 automatic parameter tuning in multi-agent optimization
                 by genetic programming. In IJCAI-2001 Workshop on
                 Wrappers for Performance Enhancement in Knowledge
                 Discovery in Databases (KDD), Seattle, Washington, USA,
                 4 August 2001. hsu:2001:waptmaoGP

                 W. H. Hsu and S. M. Gustafson. Genetic Programming for
                 Layered Learning of Multi-agent Tasks. In Late-Breaking
                 Papers of the Genetic and Evolutionary Computation
                 Conference (GECCO-2001), San Francisco, CA, June, 2001.
                 hsu:2001:gpllmt

                 S. M. Gustafson and W. H. Hsu. Layered learning in
                 genetic programming for a co-operative robot soccer
                 problem. In J. F. Miller et al, editors, Proceedings of
                 EuroGP'2001, v. 2038 of LNCS,p ages 291--301, Lake
                 Como, Italy, 18-20 April 2001. Springer-Verlag.
                 gustafson:2001:EuroGP",
}

@InProceedings{gustafson:2002:EuroGP,
  title =        "A Puzzle to Challenge Genetic Programming",
  author =       "Edmund Burke and Steven Gustafson and Graham Kendall",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "238--247",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "This report represents an initial investigation into
                 the use of genetic programming to solve the N-prisoners
                 puzzle. The puzzle has generated a certain level of
                 interest among the mathematical community. We believe
                 that this puzzle presents a significant challenge to
                 the field of evolutionary computation and to genetic
                 programming in particular. The overall aim is to
                 generate a solution that encodes complex decision
                 making. Our initial results demonstrate that genetic
                 programming can evolve good solutions. We compare these
                 results to engineered solutions and discuss some of the
                 implications. One of the consequences of this study is
                 that it has highlighted a number of research issues and
                 directions and challenges for the evolutionary
                 computation community.We conclude the article by
                 presenting some of these directions which range over
                 several areas of evolutionary computation, including
                 multi-objective fitness, coevolution and cooperation,
                 and problem representations.",
  notes =        "EuroGP'2002, part of lutton:2002:GP Best poster",
}

@InCollection{guyaguler:2000:RPWTDRMP,
  author =       "Baris Guyaguler",
  title =        "Regression on Petroleum Well Test Data with the
                 Reservoir Model as a Parameter",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "188--197",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InCollection{haberman:1994:aa,
  author =       "Mike Haberman",
  title =        "Altrusitic Ants",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "34--43",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "Ant World mazes

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{hackworth:1999:IPARAGA,
  author =       "Tim Hackworth",
  title =        "India and Pakistan, a classic ``Richardson'' Arms
                 Race: {A} Genetic Algorithmic approach",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1543--1550",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hackworth:1999:GS,
  author =       "Tim Hackworth",
  title =        "Genetic algorithms; Some effects of redundancy in
                 chromosomes",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "99--106",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@Unpublished{hafner:1996:GGP,
  author =       "Christian Hafner and Juerg Froehlich and Hansueli
                 Gerber",
  title =        "Generalized Genetic Program",
  note =         "Submitted to the 'Evolutionary Computation' Journal",
  year =         "1996",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://alphard.ethz.ch/gp.htm",
  abstract =     "A novel hybrid approach for the Symbolic Regression
                 problem is presented. First, the classical series
                 expansion approach and the traditional Genetic
                 Programming approach are outlined. In order to overcome
                 the specific problems of them, a combination is
                 analyzed and two specific implementations are
                 presented. Both the Extended Genetic Programming and
                 the Generalized Genetic Programming approach are based
                 on series expansions with genetic optimizations of the
                 basis functions combined with linear and nonlinear
                 parameter optimizations, but they exhibit important
                 differences in their 'philosophy' and in the details of
                 the implementation. The advantages of our approaches
                 are demonstrated with simple examples that are hard to
                 solve with traditional Genetic Programming. It is
                 demonstrated that the performance can drastically be
                 improved.",
  notes =        "postscript generated by MS word appears to be faulty.
                 GGP

                 ",
  size =         "25 pages",
}

@InProceedings{hafner:1999:GFAUHEA,
  author =       "Christian Hafner and Jürg Fröhlich",
  title =        "Generalized Function Analysis Using Hybrid
                 Evolutionary Algorithms",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "287--294",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series,
                 evolutionary computation, generalized function
                 analysis, hybrid evolutionary algorithms, time series
                 prediction, prominent codes, future data, symbolic
                 regression, series expansions, parameter optimization
                 techniques, highly complex codes, physics, economy",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://ieeexplore.ieee.org/iel5/6342/16952/00781938.pdf",
  size =         "8 pages",
  abstract =     "Two novel codes for the prediction of time series are
                 presented. Unlike most of the prominent codes based on
                 finding a process that predicts the future data, these
                 codes are based on function analysis and symbolic
                 regression. Both codes are based on a generalization
                 and combination of series expansions, parameter
                 optimization techniques, and genetic programming. These
                 highly complex codes are outlined and applied to
                 different examples of physics and economy.",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143 Extrapolation.
                 GCP v. EGP. Sunspot, Dow Jones, stock price prediction.
                 Full Binary trees of depth 3.",
}

@InProceedings{hagedorn:2001:agpsppr,
  author =       "John G. Hagedorn and Judith E. Devaney",
  title =        "A Genetic Programming System with a Procedural Program
                 Representation",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "152--159",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://math.nist.gov/mcsd/savg/papers/g2001.ps.gz",
  notes =        "GECCO-2001LB, NIST",
}

@InProceedings{hagiya:1998:tamc,
  author =       "Masami Hagiya",
  title =        "Towards Autonomous Molecular Computers",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "691--699",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{hahn:1994:p-p,
  author =       "Mark S. Hanh",
  title =        "Simulating Evolution In a Kolmogorov Predator-Prey
                 Model With Genetic Extensions",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "44--53",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{haith:1999:CPS,
  author =       "Gary L. Haith and Silvano P. Colombano and Jason D.
                 Lohn and Dimitris Stassinopoulos",
  title =        "Coevolution for Problem Simplification",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "244--251",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Book{hall:1995:AIsd,
  author =       "Curt Hall and Paul Harmon",
  title =        "{AI} in Software Development: Genetic Programming,
                 Fuzzy Logic, and Neural Nets",
  publisher =    "cutter",
  year =         "1995",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cutter.com/itgroup/reports/aisoft.htm",
  abstract =     "Neural network products are already being used for
                 character recognition, real estate evaluation,
                 {"}what-if{"} simulations for manufacturing, allocating
                 airline seats, trading stocks and bonds, and detecting
                 credit-card fraud.

                 Two more cutting-edge technologies -- genetic
                 programming and fuzzy-logic techniques -- are just
                 entering the marketplace, promising many more
                 innovative applications.

                 AI in Software Development presents a clear overview of
                 these exciting developments ... without hype and
                 exaggerated projections. Drawn from issues of the
                 monthly newsletter Intelligent Software Strategies,
                 this practical report demonstrates the in-depth
                 expertise and clear explanations that Curt Hall and
                 Paul Harmon are known for.",
  notes =        "lovering@cutter.com",
  size =         "45 pages",
}

@Article{hamda:2002:IJAI,
  author =       "Hatem Hamda and Francois Jouve and Evelyne Lutton and
                 Marc Schoenauer and Michele Sebag",
  title =        "Unstructured Representations in Evolutionary
                 Topological Optimum Design",
  journal =      "International Journal of Applied Intelligence",
  year =         "2002",
  note =         "Accepted for Special Issue on Creative Evolutionary
                 Systems",
  keywords =     "genetic algorithms",
  ISSN =         "0924-669X",
  URL =          "http://www-rocq.inria.fr/fractales/Publications/creative_soumis.ps.gz",
  URL =          "http://www.wkap.nl/prod/j/0924-669X",
  size =         "29 pages",
  abstract =     "This paper proposes a few steps to escape structured
                 extensive representations for evolutionary solving of
                 Topological Optimum Design (TOD) problems: early
                 results have shown the ability of Evolutionary methods
                 to find numerical solutions to yet unsolved TOD
                 problems, but those approaches were limited because the
                 complexity of the representation was that of a fixed
                 underlying mesh. Different compact unstructured
                 representations are introduced, the complexity of which
                 is self-adaptive, i.e. is evolved by the algorithm
                 itself. The Voronoi-based representations are variable
                 length lists of alleles that are directly decoded into
                 shapes, while the IFS representation, based on fractal
                 theory, involves a much more complex morphogenetic
                 process. First results demonstrates that Voronoi-based
                 representations allow one to push further the limits of
                 Evolutionary Topological Optimum Design by actually
                 removing the correlation between the complexity of the
                 representations and that of the discretization. Further
                 comparative results among all these representations on
                 simple test problems indicate that the complex
                 causality in the IFS representation disfavor it
                 compared to the Voronoi-based representations.",
  notes =        "Bentely and Corne Special issue",
}

@InProceedings{hamel:2002:gecco,
  author =       "Lutz Hamel",
  title =        "Breeding Algebraic Structures---An Evolutionary
                 Approach To Inductive Equational Logic Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "748--755",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, algebraic
                 specification, concept learning, equational logic,
                 inductive logic programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{hampo:1992:new,
  author =       "Richard Hampo",
  title =        "Genetic Programming: {A} New Paradigm for Control and
                 Analysis",
  booktitle =    "Third ASME Symposium on Transportation Systems",
  year =         "1992",
  pages =        "155--163",
  note =         "Invited Paper at ASME Winter Annual Meeting, 9--13
                 November, Anaheim, California, USA",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{hampo:1992:cvs,
  author =       "R. J. Hampo and K. A. Marko",
  title =        "Application of Genetic Programming to Control of
                 Vehicle Systems",
  booktitle =    "Proceedings of the Intelligent Vehicles '92
                 Symposium",
  year =         "1992",
  note =         "June 29 - July 1, 1992, Detroit, Mi, USA",
  keywords =     "genetic algorithms, genetic programming",
}

@Unpublished{hampo:1992:newford,
  author =       "R. J. Hampo",
  title =        "The Genetic Programming Paradigm: {A} New Tool for
                 Analysis and Control",
  note =         "Ford Proprietary",
  month =        "6 " # mar,
  year =         "1992",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Ford Technical Report SR-92-114",
}

@InProceedings{Hampo:1994:ICemdagGP,
  author =       "Richard J. Hampo and Bruce D. Bryant and Kenneth A.
                 Marko",
  title =        "{IC} Engine Misfire Detection Algorithm Generation
                 Using Genetic Programming",
  booktitle =    "EUFIT'94",
  year =         "1994",
  pages =        "1674--1678",
  address =      "Promenade 9, D-52076, Aachen, Germany",
  month =        "20--23 " # sep,
  publisher =    "ELITE-Foundation",
  keywords =     "genetic algorithms, genetic programming",
  size =         "5 pages",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/misfire-detection.PS.Z",
  notes =        "

                 Presents 2 GPs and a Neural Net detecting engine
                 missfires from test data. Both GPs better than NN. Not
                 clear what distinction is between two GPs. Uses
                 existing, processed input signals from engine. Says GP
                 easier to implement in existing vehical
                 computer.

                 Author's address: Ford Motor Company Scientific
                 Research Laboratory, PO BOX 2053, 20000 Rotunda Drive,
                 MD 2036, Dearborn, Michigan 48121-2053, USA",
}

@InCollection{han:2000:GHSPGA,
  author =       "Todd Han",
  title =        "Generating Hard Satisfiability Problems with Genetic
                 Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "198--205",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{Hand:1997:gn,
  author =       "Charles Hand",
  title =        "Genetic Nets",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@Misc{hand:1994:GPreview,
  author =       "David J. Hand",
  title =        "Evolutionary computation",
  keywords =     "genetic algorithms, genetic programming",
  size =         "0.7 pages",
  journal =      "Statistics and Computing",
  year =         "1994",
  volume =       "4",
  number =       "2",
  pages =        "158",
  month =        jun,
  note =         "Review of Koza's {"}Genetic Programming{"}",
  notes =        "Special issue on Evolutionary Programming. Favourable
                 review of koza:book",
}

@InProceedings{handa:1999:CGASDCSP,
  author =       "Hisashi Handa and Osamu Katai and Tadataka Konishi and
                 Mitsuru Baba",
  title =        "Coevolutionary Genetic Algorithms for Solving Dynamic
                 Constraint Satisfaction Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "252--257",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{icga93:handley,
  author =       "Simon Handley",
  title =        "Automatic Learning of a Detector for alpha-helices in
                 Protein Sequences Via Genetic Programming",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "271--278",
  size =         "8 pages",
  abstract =     "This paper reports preliminary results from an attempt
                 to predict the secondary structure of globular
                 proteins. The genetic programming system was used to
                 evolve programs that classified each residue in ten
                 proteins as being either in an a-helix or in a
                 {"}coil{"} (everything else). The proteins were chosen
                 to be non-homologous and to contain mostly a-helices.
                 The ten proteins were divided in half into a training
                 set, that was used to drive the evolution, and a
                 testing set, that was used to test the resultant
                 programs. The fitness of the programs was based on the
                 correlation coefficient between the observed and the
                 predicted a-helicity of the residues. The fittest
                 program produced by the genetic programming system had
                 a correlation of 0.316 between the observed
                 classifications and the classifications predicted by
                 the program (on the proteins in the testing set).",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/alpha-helices.ps.gz",
  notes =        "GP based upon balkiness and hydrophilicity of the 7
                 amino acid residues closest to a point along the chain
                 (repeat for whole chain). Train on five known P test on
                 five more. NOT GOOD, GP learns structure of the
                 training set well but this is not a very good predictor
                 for the others",
}

@InProceedings{Handley:1993:GPagplGP,
  author =       "S. Handley",
  title =        "The genetic planner: The automatic generation of plans
                 for a mobile robot via genetic programming",
  booktitle =    "Proceedings of the Eighth IEEE International Symposium
                 on Intelligent Control",
  year =         "1993",
  organisation = "The IEEE Control System Society",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Planning is the creation of programs to control an
                 agent, such as a robot. Traditionally, planners have
                 maintained a logical model of the agent's world and
                 planned by reasoning about what plans do to that world.
                 The Genetic Planner uses artificial selection, sexual
                 mixing (recombination) and fitness proportionate
                 reproduction to breed computer programs (i.e., to
                 plan). The Genetic Planner uses a simulation of the
                 world to execute candidate computer programs (i.e.,
                 candidate plans). This paper describes The Genetic
                 Planner and shows it at work on a simple problem: a
                 robot on a 2-D grid.",
  notes =        "Chicago, IL, USA

                 ",
}

@InProceedings{Handley:1991:agplGPADF,
  author =       "S. Handley",
  title =        "The automatic generation of plans for a mobile robot
                 via genetic programming with automatically defined
                 functions",
  booktitle =    "Proceedings of the Fifth Workshop on Neural Networks:
                 An International Conference on Computational
                 Intelligence: Neural Networks, Fuzzy Systems,
                 Evolutionary Programming, and Virtual Reality",
  year =         "1991",
  organisation = "The Society for Computer Simulation",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{kinnear:handley,
  title =        "The Automatic Generations of Plans for a Mobile Robot
                 via Genetic Programming with Automatically Defined
                 Functions",
  author =       "Simon G. Handley",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "18",
  pages =        "391--407",
  keywords =     "genetic algorithms, genetic programming",
  size =         "17 pages",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/kinnear.ps.gz",
  abstract =     "Planning is the creation of programs to control an
                 agent, such as a robot. Traditionally, planners have
                 maintained a logical model of the agent's world and
                 planned by reasoning about what plans do to that world.
                 In this chapter I describe a new planner, the Genetic
                 Planner, that uses artificial selection, sexual mixing
                 (recombination) and fitness proportionate reproduction
                 to breed computer programs (i.e., to plan). This
                 planner uses a simulation of the world to execute
                 candidate computer programs (i.e., candidate plans). I
                 first describe this planner and then I show it at work
                 on a simple problem---a robot on a 2-D grid. Also,
                 Koza's Automatically Defined Functions (ADFs) are used
                 and the results compared with the non-ADF genetic
                 programming system.",
  notes =        "

                 Move about 49 by 49 world, move boxes, fails to switch
                 on light. Describes Genetic Planner (=GP plus fitness
                 function based upon hiow close to succeeding multiple
                 predicates are)

                 ",
}

@InProceedings{Handley:1994:DAGpcp,
  author =       "S. Handley",
  title =        "On the use of a directed acyclic graph to represent a
                 population of computer programs",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "154--159",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/caching_paper___first_draft.ps.gz",
  size =         "6 pages",
  abstract =     "This paper demonstrates a technique that reduces the
                 time and space requirements of genetic programming. The
                 population of parse trees is stored as a directed
                 acyclic graph (DAG), rather than as a forest of trees.
                 This saves space by not duplicating structurally
                 identical subtrees. Also, the value computed by each
                 subtree for each fitness case is cached, which saves
                 computation both by not recomputing subtrees that
                 appear more than once in a generation and by not
                 recomputing subtrees that are copied from one
                 generation to the next. I have implemented this
                 technique for a number of problems and have seen a 15-
                 to 28-fold reduction in the number of nodes extant per
                 generation and an 11- to 30-fold reduction in the
                 number of nodes evaluated per run (for populations of
                 size 500).",
  notes =        "Converts whole GP population to a directed Acyclic
                 Graph, which is functionally equivelent. With
                 primatives that have NO SIDE EFFECTS is able to cache
                 earlier sub tree evaluations so they donot have to be
                 re-evaluated, even if occur in a different individual.
                 Claims speed ups of 11-30 fold.",
}

@InProceedings{Handley:1994:alAHGP,
  author =       "S. Handley",
  title =        "Automated learning of a detector for the cores of
                 a-helices in protein sequences via genetic
                 programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "474--479",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/helix_segments_paper.ps.gz",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  abstract =     "I used Koza's genetic programming to evolve programs
                 that classified contiguous regions of proteins as being
                 a-helix cores or not. I snipped positive and negative
                 examples of a-helix core regions out of a set of 90
                 proteins. These proteins were chosen from the
                 Brookhaven Protein Data Bank to be non-homologous. The
                 fitness of the programs was defined as the correlation
                 coefficient between the observed and the predicted
                 a-helicity of the above regions. The fittest program
                 produced by the genetic programming system that
                 predicted the training set at least as well as the
                 testing set had a correlation of 0.4818 between the
                 observed classifications and the classifications
                 predicted by the program (on the proteins in the
                 testing set).",
}

@InProceedings{handley:1994:solvent,
  author =       "Simon G. Handley",
  title =        "The prediction of the degree of exposure to solvent of
                 amino acid residues via genetic programming",
  booktitle =    "Second International Conference on Intelligent Systems
                 for Molecular Biology",
  year =         "1994",
  address =      "Stanford University, Stanford, CA, USA",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/pburied.ps.gz",
  abstract =     "In this paper I evolve programs that predict the
                 degree of exposure to solvent (the buriedness) of amino
                 acid residues given only the primary structure. I use
                 genetic programming to evolve programs that take as
                 input the primary structure and that output the
                 buriedness of each residue. I trained these programs on
                 a set of 82 proteins from the Brookhaven Protein Data
                 Bank (PDB) and cross-validated them on a separate
                 testing set of 40 proteins, also from the PDB. The best
                 program evolved had a correlation of 0.434 between the
                 predicted and observed buriednesses on the testing
                 set.",
}

@InCollection{handley:1994:al,
  author =       "Simon G. Handley and Tod Klingler",
  title =        "Automated learning of a detector for a-helices in
                 protein sequences via genetic programming",
  booktitle =    "Artificial Life at Stanford 1993",
  year =         "1993",
  editor =       "John R. Koza",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-171957-6",
  notes =        "Part of koza:1993:alife Student works for course
                 {"}Artificial Life{"} (Computer Science 425) at
                 Stanford University offered during 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{handley:1995:DNAsplice,
  author =       "Simon Handley",
  title =        "Predicting Whether or Not a 60-base {DNA} Sequence
                 Contains a Centrally-Located Splice Site Using Genetic
                 Programming",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "98--103",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/splicej.ps.gz",
  url2 =         "http://www-leland.stanford.edu/~shandley/postscript/ML95GPwkshp.ps.gz",
  size =         "6 pages",
  abstract =     "An evolutionary computation technique, genetic
                 programming, was used to create programs that classify
                 DNA sequences into one of three classes: (1) contains a
                 centrally-located donor splice site, (2) contains a
                 centrally-located acceptor splice site, and (3)
                 contains neither a donor nor an acceptor. The
                 performance of the programs created are competitive
                 with previous work.",
  notes =        "Pop size 64,000 part of rosca:1995:ml",
}

@InProceedings{handley:1995:IorE,
  author =       "Simon Handley",
  title =        "Classifying Nucleic Acid Sub-Sequences as Introns or
                 Exons Using Genetic Programming",
  booktitle =    "Proceedings of the Third International Conference on
                 Intelligent Systems for Molecular Biology (ISMB-95)",
  year =         "1995",
  editor =       "Christopher Rawlins and Dominic Clark and Russ Altman
                 and Lawrence Hunter and Thomas Lengauer and Shoshana
                 Wodak",
  pages =        "162--169",
  address =      "Cambridge, UK",
  publisher_address = "Menlo Park, CA, USA",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/iep-ISMB.ps.gz",
  abstract =     "An evolutionary computation technique, genetic
                 programming, was used to create programs that classify
                 messenger RNA sequences into one of two classes: (1)
                 the sequence is expressed as (part of) a protein
                 (called an exon), or (2) not expressed as protein
                 (called an intron).",
  notes =        "

                 ",
}

@InProceedings{handley:1995:coliP,
  author =       "Simon Handley",
  title =        "Predicting Whether or not a Nucleic Acid Sequence is
                 an {E}. coli Promoter Region using Genetic
                 Programming",
  booktitle =    "Proceedings of the First International Symposium on
                 Intelligence in Neural and Biological Systems INBS-95",
  year =         "1995",
  pages =        "122--127",
  address =      "Herndon, Virginia, USA",
  publisher_address = "Los Alamitos, California, USA",
  month =        "29-31 " # may,
  organisation = "IEEE Comitteee on Pattern Analysis and Machine
                 Intelligence (PAMI)",
  publisher =    "IEEE Computer Society Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-leland.stanford.edu/~shandley/postscript/postscript/INBS-camera-ready.ps.gz",
  abstract =     "This paper shows that an evolutionary computing
                 technique, genetic programming, can create programs
                 that classify DNA sequences as E. coli promoter vs
                 non-E. coli promoter. The performance of the programs
                 are competitive with pervious work.",
  notes =        "Pop size 32,000

                 ",
}

@InProceedings{handley:1995:DNAspliceF,
  author =       "Simon Handley",
  title =        "Predicting Whether Or Not a 60-Base {DNA} Sequence
                 Contains a Centrally-Located Splice Site Using Genetic
                 Programming",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "17--22",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP{\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InProceedings{handley:1996:pdesaarGP,
  author =       "Simon Handley",
  title =        "The Prediction of the Degree of Exposure to Solvent of
                 Amino Acid Residues via Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "297--300",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "4 pages",
  notes =        "GP-96",
}

@InProceedings{handley:1996:nfsssp,
  author =       "Simon Handley",
  title =        "A New Class of Function Sets for Solving Sequence
                 Problems",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "301--308",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "8 pages",
  notes =        "GP-96",
}

@PhdThesis{handley:thesis,
  author =       "Simon Handley",
  title =        "Automatically Discovering Solutions that Flexibly
                 Combine Iterative and non-Iterative Computations",
  school =       "Department of Computer Science, Stanford University",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@InProceedings{hara:1999:EAADG,
  author =       "Akira Hara and Tomoharu Nagao",
  title =        "Emergence of the cooperative behavior using {ADG};
                 Automatically Defined Groups",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1039--1046",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{harik:1999:A,
  author =       "Georges R. Harik and Fernando G. Lobo",
  title =        "A parameter-less genetic algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "258--265",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{harmeling:2000:SSPGA,
  author =       "Stefan Harmeling",
  title =        "Solving Satisfiability Problems with Genetic
                 Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "206--213",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{harrell:1999:EAPFIWMDP,
  author =       "Laura J. Harrell and S. Ranji Ranjithan",
  title =        "Evaluation of Alternative Penalty Function
                 Implementations in a Watershed Management Design
                 Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1551--1558",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Harries:1997:eaossGP,
  author =       "Kim Harries and Peter Smith",
  title =        "Exploring Alternative Operators and Search Strategies
                 in Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "147--155",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97

                 even-4-parity, even-5-parity, artificial ant (Santa Fe
                 trail), regression of x^4-3x^3+9x^2-27x Depth-based
                 crossover (depth fair, SameDepths and DiffDepths)
                 NoBias and combinations of crossovers. SameDepths does
                 badly on even-5-parity otherwise crossovers similar to
                 each other. 58,100 runs

                 Several different types of mutation (and combination of
                 mutation) used as stochastic {"}hill
                 climbers{"}.

                 Steady state, tournament size=2, limit of 1000 nodes,
                 kinnear's Hoist and mutation both at 1 percent.

                 GP mutation generally good hill climber, small and
                 self-crossover generally awful. See sec 4 discussion.
                 )",
}

@Misc{harries:1998:cgediass,
  author =       "K. Harries and P. W. H. Smith",
  title =        "Code Growth, Explicitly Defined Introns and
                 Alternative Selection Schemes",
  howpublished = "www",
  year =         "1998",
  note =         "Earlier version of Evolutionary Computation 6 (4),
                 336-360, 1998",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.city.ac.uk/homes/peters/pub/Introns6.prn",
  size =         "pages",
  notes =        "Date: Thu, 25 May 2000 19:39:54 -0400 (EDT) From: Sean
                 Luke For harries:1998:cgediass, you write that the URL
                 {"}breaks my version of ghostview{"}. This is because
                 it's not quite a PostScript file -- it's an HP laser
                 printer job. The first four lines go: (Between the
                 ###'s)

                 ### -12345X@PJL JOB @PJL SET RESOLUTION = 600 @PJL
                 ENTER LANGUAGE = POSTSCRIPT ###

                 You need to delete these four lines, including the
                 space, and rename the file with a {"}.ps{"} extension.
                 Then it displays nicely in GhostView, and also prints
                 nicely.

                 Final version is PWHSmith:1998:cgediass",
}

@TechReport{Harris:1996:edgegpRN,
  author =       "Christopher Harris and Bernard Buxton",
  title =        "Evolving Edge Detectors",
  year =         "1996",
  institution =  "UCL",
  type =         "Research Note",
  number =       "RN/96/3",
  address =      "Gower Street, London, WC1E 6BT, UK",
  month =        jan,
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/edgegp.ps",
  keywords =     "genetic algorithms, genetic programming, Edge
                 Detection",
  abstract =     "Edge detection is the process of detecting
                 discontinuities in signals and images. We apply Genetic
                 Programming techniques to the production of
                 high-performance edge detectors for 1-D signals and
                 image profiles. The method, which it is intended to
                 extend to the development of practical edge detectors
                 for use in image processing and machine vision, uses
                 theoretical performance measures as criteria for the
                 experimental design.",
}

@InProceedings{Harris:1996:edgegp,
  author =       "Christopher Harris and Bernard Buxton",
  title =        "Evolving Edge Detectors with Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "309--315",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@TechReport{Harris:1996:gpcomRN,
  author =       "Christopher Harris and Bernard Buxton",
  title =        "{GP}-{COM}: {A} Distributed, Component-Based Genetic
                 Programming System in {C}++",
  year =         "1996",
  institution =  "UCL",
  type =         "Research Note",
  number =       "RN/96/2",
  address =      "Gower Street, London, WC1E 6BT, UK",
  month =        jan,
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/gpcom.ps",
  keywords =     "genetic algorithms, genetic programming, Software
                 System",
  abstract =     "Widespread adoption of Genetic Programming techniques
                 as a domain-independent problem solving tool depends on
                 a good underlying software structure. A system is
                 presented that mirrors the conceptual make-up of a GP
                 system. Consisting of a loose collection of software
                 components, each with strict interface definitions and
                 roles, the system maximises flexibility and minimises
                 effort when applied to a new problem domain.",
}

@InProceedings{Harris:1996:gpcom,
  author =       "Christopher Harris and Bernard Buxton",
  title =        "{GP}-{COM}: {A} Distributed, Component-Based Genetic
                 Programming System in {C}++",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "425",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@TechReport{Harris:1997:ledGPpsa,
  author =       "Christopher Harris and Bernard Buxton",
  title =        "Low-level Edge Detection Using Genetic Programming:
                 performance, specificity and application to real-world
                 signals",
  year =         "1997",
  institution =  "UCL",
  type =         "Research Note",
  number =       "RN/97/7",
  address =      "Gower Street, London, WC1E 6BT, UK",
  keywords =     "genetic algorithms, genetic programming, Edge
                 Detection",
  notes =        "Submitted to a special issue of Image and Vision
                 Computing on Evolutionary Optimisation in Image and
                 Vision Computing",
}

@InProceedings{harris:1997:STGPphtexc,
  author =       "Christopher Harris",
  title =        "Strongly Types {GP} to promote hierarchy through
                 explicit syntax constraints",
  booktitle =    "Late Breaking Papers at the GP-97 Conference",
  year =         "1997",
  editor =       "John Koza",
  pages =        "72--80",
  address =      "Stanford, CA, USA",
  publisher_address = "Stanford, California, 94305-3079 USA",
  month =        "13-16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/C.Harris/stgp_structure.ps.gz",
  size =         "9 pages",
  notes =        "GP-97LB

                 It's ms-word postscript, so use pageview to look at it
                 rather than ghostview, should print fine.",
}

@InProceedings{harris:1997:ehSTGP,
  author =       "Christopher Harris",
  title =        "Enforcing Hierarchy on Solutions with Strongly Typed
                 Genetic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "292",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@PhdThesis{harris:thesis,
  author =       "Christopher Harris",
  title =        "An investigation into the Application of Genetic
                 Programming techniques to Signal Analysis and Feature
                 Detection",
  school =       "University College, London",
  year =         "1997",
  month =        "26 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/c.harris/thesisps.zip",
  size =         "186 pages",
}

@InProceedings{harris:1999:PIWRUGA,
  author =       "S. D. Harris and R. Mustata and L. Elliott and D. B.
                 Ingham and D. Lesnic",
  title =        "Parameter Identification Within Rocks Using Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1779",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{harris:1999:TRCRRUGA,
  author =       "S. D. Harris and L. Elliott and D. B. Ingham and M.
                 Pourkashanian and C. W. Wilson",
  title =        "The Retrieval of Chemical Reaction Rates Using Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1780",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{harris:2000:GPFF,
  author =       "Sarah Harris",
  title =        "Genetically-Learned 7-Input Parity Function by an 8 x
                 8 {FPGA}",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "214--220",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{hart:1999:AISASCE,
  author =       "Emma Hart and Peter Ross",
  title =        "An Immune System Approach to Scheduling in Changing
                 Environments",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1559--1566",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hart:1999:CEPEPSAADDA,
  author =       "William E. Hart",
  title =        "Comparing Evolutionary Programs and Evolutionary
                 Pattern Search Algorithms: {A} Drug Docking
                 Application",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "855--862",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{hart:1995:TAGPCMPE,
  author =       "John Hart",
  title =        "The Application of Genetic Programming to Cooperative
                 Movement Planning and Execution",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "86--95",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{hart:2002:gecco:lbp,
  title =        "Evolving Software with Multiple Outputs and Multiple
                 Populations",
  author =       "John Hart and Martin Shepperd",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "223--227",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 Tries to evolve controller for fridge. Variable length
                 string.",
}

@InProceedings{hartley:1999:A,
  author =       "Adrian R. Hartley",
  title =        "Accuracy-based fitness allows similar performance to
                 humans in static and dynamic classification
                 environments",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "266--273",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{harvey:1998:bcGP,
  author =       "Brad Harvey and James A. Foster and Deborah Frincke",
  title =        "Byte Code Genetic Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Unpublished{harvey:1997:ob,
  author =       "Inman Harvey",
  title =        "Open the Box",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, variable size representation,
                 SAGA",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "4 pages",
}

@InProceedings{harvey:1999:TBCGP,
  author =       "Brad Harvey and James Foster and Deborah Frincke",
  title =        "Towards Byte Code Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1234",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{harvey:1999:TOMSMFSRPGA,
  author =       "K. Burton Harvey and Chrisila C. Pettey",
  title =        "The Outlaw Method for Solving Multimodal Functions
                 with Split Ring Parallel Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "274--280",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Hasegawa:1997:mg2br,
  author =       "Yasuhisa Hasegawa and Toshio Fukuda",
  title =        "Motion Generation of Two-link Brachiation Robot",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Artifical life and evolutionary robotics",
  pages =        "407--412",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{hatanaka:2001:hmimbgp,
  author =       "Toshiharu Hatanaka and Katsuji Uosaki",
  title =        "Hammerstein Model Identification Method Based on
                 Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1430--1435",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, System
                 identification, Hammerstein models, Nonlinear systems,
                 Evolutionary computation",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 AIC Akaike information criterion",
}

@InProceedings{hatta:1998:appiGA,
  author =       "Koichi Hatta and Shin'ichi Wakabayashi and Tetsushi
                 Koide",
  title =        "Adapting Parameters Based on Pedigree of Individuals
                 in a Genetic Algorithm",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "510--517",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InCollection{haugh:2002:ELCDGP,
  author =       "Justin C. Haugh",
  title =        "Evaluation of Life Cycle Differentiation using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "102--110",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp 10 by 10 world. Snakes and
                 mice. lilgp problem -> gpc++ 0.40",
}

@MastersThesis{haynes:1994:masters,
  author =       "Thomas D. Haynes",
  title =        "A Simulation of Adaptive Agents in a Hostile
                 Environment",
  school =       "University of Tulsa",
  year =         "1994",
  address =      "Tulsa, OK, USA",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/masters.ps",
  size =         "254 pages",
  abstract =     "The Genetic Programming Algorithm is used to construct
                 an Autonomous Agent capable of learning how to survive
                 a hostile environment. Randomly generated programs,
                 which control the interaction of the Agent with its
                 environment, are recombined to form better programs.
                 Each generation of the population of Agents is placed
                 into the Simulator with the ultimate goal of producing
                 an Agent capable of surviving any environment. The
                 Simulator determines the raw fitness of each Agent by
                 interpreting the associated program. General programs
                 are evolved to solve this problem. Different
                 environmental setups are presented to show the
                 generality of the solution. Certain constructs always
                 appear to facilitate the solution of subproblems of the
                 task. This is evidenced in similar responses of the
                 Average Fitness per Generation curves for the different
                 runs.",
  notes =        "

                 ",
}

@TechReport{Hayes:1994:ecs,
  author =       "Thomas Haynes and Roger Wainwright and Sandip Sen",
  title =        "Evolving Cooperation Strategies",
  institution =  "The University of Tulsa",
  year =         "1994",
  type =         "Technical Report",
  number =       "UTULSA-MCS-94-10",
  address =      "Tulsa, OK, USA",
  month =        "16 " # dec,
  keywords =     "genetic algorithms, genetic programming, ccoperation
                 strategies",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/icmas95.ps",
  abstract =     "The identification, design, and implementation of
                 strategies for cooperation is a central research issue
                 in the field of Distributed Artificial Intelligence
                 (DAI). We propose a novel approach to the construction
                 of cooperation strategies for a group of problem
                 solvers based on the Genetic Programming (GP) paradigm.
                 GP's are a class of adaptive algorithms used to evolve
                 solution structures that optimize a given evaluation
                 criterion. Our approach is based on designing a
                 representation for cooperation strategies that can be
                 manipulated by GPs. We present results from experiments
                 in the predator-prey domain, which has been extensively
                 studied as an easy-to-describe but difficult-to-solve
                 cooperation problem domain. They key aspect of our
                 approach is the minimal reliance on domain knowledge
                 and human intervention in the construction of good
                 cooperation strategies. Promising comparison results
                 with prior systems lend credence to the viability of
                 this approach.",
  size =         "9 pages",
  notes =        "

                 ",
}

@InProceedings{Hayes:1995:agents,
  author =       "Thomas D. Haynes and Roger L. Wainwright",
  title =        "A Simulation of Adaptive Agents in Hostile
                 Environment",
  booktitle =    "Proceedings of the 1995 ACM Symposium on Applied
                 Computing",
  year =         "1995",
  editor =       "K. M. George and Janice H. Carroll and Ed Deaton and
                 Dave Oppenheim and Jim Hightower",
  pages =        "318--323",
  address =      "Nashville, USA",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/sac95.ps",
  size =         "8 pages",
  abstract =     "In this paper we use the genetic programming technique
                 to evolve programs to control an autonomous agent
                 capable of learning how to survive in a hostile
                 environment. In order to facilitate this goal, agents
                 are run through random environment configurations.
                 Randomly generated programs, which control the
                 interaction of the agent with its environment, are
                 recombined to form better programs. Each generation of
                 the population of agents is placed into the Simulator
                 with the ultimate goal of producing an agent capable of
                 surviving any environment. The environment that an
                 agent is presented consists of other agents, mines, and
                 energy. The goal of this research is to construct a
                 program which when executed will allow an agent (or
                 agents) to correctly sense, and mark, the presence of
                 items (energy and mines) in any environment. The
                 Simulator determines the raw fitness of each agent by
                 interpreting the associated program. General programs
                 are evolved to solve this problem. Different
                 environmental setups are presented to show the
                 generality of the solution. These environments include
                 one agent in a fixed environment, one agent in a
                 fluctuating environment, and multiple agents in a
                 fluctuating environment cooperating together. The
                 genetic programming technique was extremely successful.
                 The average fitness per generation in all three
                 environments tested showed steady improvement. Programs
                 were successfully generated that enabled an agent to
                 handle any possible environment.",
  notes =        "Agent has access to memory holding information on
                 locations it has already visited.

                 Agents are run through random environment
                 configurations. Environment contains other agents,
                 lethal mines and energy. Agents aims to sense and mark
                 these. One example: multiple agents cooperating in a
                 fluctating environment. GP generated an {"}agent to
                 handle any possible enironment{"}.",
}

@InProceedings{Hayes:1995:ecsICMAS,
  author =       "Thomas D. Haynes and Roger L. Wainwright and Sandip
                 Sen",
  title =        "Evolving Cooperating Strategies",
  booktitle =    "Proceedings of the first International Conference on
                 Multiple Agent Systems",
  year =         "1995",
  editor =       "Victor Lesser",
  pages =        "450",
  address =      "San Francisco, USA",
  month =        "12--14 " # jun,
  publisher =    "AAAI Press/MIT Press",
  note =         "Poster",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, cooperation strategies",
  ISBN =         "0-262-62102-9",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/icmas95.ps",
  size =         "1 page",
  abstract =     "The identification, design, and implementation of
                 strategies for cooperation is a central research issue
                 in the field of Distributed Artificial Intelligence
                 (DAI). We propose a novel approach to the construction
                 of cooperation strategies for a group of problem
                 solvers based on the Genetic Programming (GP) paradigm.
                 GP's are a class of adaptive algorithms used to evolve
                 solution structures that optimize a given evaluation
                 criterion. Our approach is based on designing a
                 representation for cooperation strategies that can be
                 manipulated by GPs. We present results from experiments
                 in the predator-prey domain, which has been extensively
                 studied as an easy-to-describe but difficult-to-solve
                 cooperation problem domain. They key aspect of our
                 approach is the minimal reliance on domain knowledge
                 and human intervention in the construction of good
                 cooperation strategies. Promising comparison results
                 with prior systems lend credence to the viability of
                 this approach.",
  notes =        "13 page version available via url

                 ",
}

@InProceedings{Hayes:1995,
  author =       "Thomas Haynes and Roger Wainwright and Sandip Sen and
                 Dale Schoenefeld",
  title =        "Strongly typed genetic programming in evolving
                 cooperation strategies",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "271--278",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-370-0",
  URL =          "http://euler.mcs.utulsa.edu/~rogerw/haynes/icga95.ps",
  abstract =     "A key concern in genetic programming (GP) is the size
                 of the state-space which must be searched for large and
                 complex problem domains. One method to reduce the
                 state-space size is by using Strongly Typed Genetic
                 Programming (STGP). We applied both GP and STGP to
                 construct cooperation strategies to be used by multiple
                 predator agents to pursue and capture a prey agent on a
                 grid-world. This domain has been extensively studied in
                 Distributed Artificial Intelligence (DAI) as an
                 easy-to-describe but difficult-to-solve cooperation
                 problem. The evolved programs from our systems are
                 competitive with manually derived greedy algorithms. In
                 particular the STGP paradigm evolved strategies in
                 which the predators were able to achieve their goal
                 without explicitly sensing the location of other
                 predators or communicating with other predators. This
                 represents an improvement over previous research in
                 this area. The results of our experiments indicate that
                 STGP is able to evolve programs that perform
                 significantly better than GP evolved programs. In
                 addition, the programs generated by STGP were easier to
                 understand.",
  notes =        "Our printers barf at graph on page 8.

                 ",
}

@InProceedings{Hayes:1995:ebspp,
  author =       "Thomas Haynes and Sandip Sen",
  title =        "Evolving Behavioral Strategies in Predators and Prey",
  booktitle =    "IJCAI-95 Workshop on Adaptation and Learning in
                 Multiagent Systems",
  year =         "1995",
  editor =       "Sandip Sen",
  pages =        "32--37",
  address =      "Montreal, Quebec, Canada",
  publisher_address = "San Francisco, CA, USA",
  month =        "20-25 " # aug,
  organisation = "IJCAII,AAAI,CSCSI",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, cooperation
                 strategies",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/icjai95.ps",
  abstract =     "The predator/prey domain is utilized to conduct
                 research in Distributed Artificial Intelligence.
                 Genetic Programing is used to evolve behavioral
                 strategies for the predator agents. To further the
                 utility of the predator strategies, the prey population
                 is allowed to evolve at the same time. The expected
                 competitive learning cycle did not surface. This
                 failing is investigated, and a simple prey algorithm
                 surfaces, which is consistently able to evade capture
                 from the predator algorithms.",
  notes =        "see also Haynes:1996:EBS

                 ",
}

@InProceedings{Haynes95:Team,
  author =       "Thomas Haynes and Sandip Sen and Dale Schoenefeld and
                 Roger Wainwright",
  title =        "Evolving a Team",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "23--30",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We introduce a cooperative co--evolutionary system to
                 facilitate the development of teams of agents.
                 Specifically, we deal with the credit assignment
                 problem of how to fairly split the fitness of a team to
                 all of its participants. We believe that ${k}$
                 different strategies for controlling the actions of a
                 group of ${k}$ agents can combine to form a cooperation
                 strategy which efficiently results in attaining a
                 global goal. A concern is the amount of time needed to
                 either evolve a good team or reach convergence. We
                 present several crossover mechanisms to reduce this
                 time. Even with this mechanisms, the time is large;
                 which precluded the gathering of sufficient data for a
                 statistical base.",
  notes =        "AAAI-95f GP

                 {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457
                 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InCollection{Haynes95:Prey,
  author =       "Thomas Haynes and Sandip Sen",
  title =        "Evolving Behavioral Strategies in Predators and Prey",
  booktitle =    "Adaptation and Learning in Multiagent Systems",
  publisher =    "Springer Verlag",
  year =         "1995",
  editor =       "Gerhard Wei{\ss} and Sandip Sen",
  series =       "Lecture Notes in Artificial Intelligence",
  address =      "Berlin, Germany",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The predator/prey domain is utilized to conduct
                 research in Distributed Artificial Intelligence.
                 Genetic Programming is used to evolve behavioral
                 strategies for the predator agents. To further the
                 utility of the predator strategies, the prey population
                 is allowed to evolve at the same time. The expected
                 competitive learning cycle did not surface. This
                 failing is investigated, and a simple prey algorithm
                 surfaces, which is consistently able to evade capture
                 from the predator algorithms.",
  notes =        "

                 ",
}

@TechReport{Haynes:1995:EMC,
  author =       "Thomas Haynes and Sandip Sen and Dale Schoenefeld and
                 Roger Wainwright",
  title =        "Evolving Multiagent Coordination Strategies with
                 Genetic Programming",
  number =       "UTULSA-MCS-95-04",
  institution =  "The University of Tulsa",
  year =         "1995",
  month =        may # " 31,",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The design and development of behavioral strategies to
                 coordinate the actions of multiple agents is a central
                 issue in multiagent systems research. We propose a
                 novel approach of evolving, rather than handcrafting,
                 behavioral strategies. The evolution scheme used is a
                 variant of the Genetic Programming (GP) paradigm. As a
                 proof of principle, we evolve behavioral strategies in
                 the predator-prey domain that has been studied widely
                 in the Distributed Artificial Intelligence community.
                 We use the GP to evolve behavioral strategies for
                 individual agents, as prior literature claims that
                 communication between predators is not necessary for
                 successfully capturing the prey. The evolved strategy,
                 when used by each predator, performs better than all
                 but one of the handcrafted strategies mentioned in
                 literature. We analyze the shortcomings of each of
                 these strategies. The next set of experiments involve
                 co-evolving predators and prey. To our surprise, a
                 simple prey strategy evolves that consistently evades
                 all of the predator strategies. We analyze the
                 implications of the relative successes of evolution in
                 the two sets of experiments and comment on the nature
                 of domains for which GP based evolution is a viable
                 mechanism for generating coordination strategies. We
                 conclude with our design for concurrent evolution of
                 multiple agent strategies in domains where agents need
                 to communicate with each other to successfully solve a
                 common problem.",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/jp.ps",
  notes =        "

                 ",
}

@InCollection{haynes:1996:aigp2,
  author =       "Thomas D. Haynes and Dale A. Schoenefeld and Roger L.
                 Wainwright",
  title =        "Type Inheritance in Strongly Typed Genetic
                 Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "359--376",
  chapter =      "18",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/hier.ps",
  abstract =     "Genetic Programming (GP) is an automatic method for
                 generating computer programs, which are stored as data
                 structures and manipulated to evolve better programs.
                 An extension restricting the search space is Strongly
                 Typed Genetic Programming (STGP), which has, as a basic
                 premise, the removal of closure by typing both the
                 arguments and return values of functions, and by also
                 typing the terminal set. A restriction of STGP is that
                 there are only two levels of typing. We extend STGP by
                 allowing a type hierarchy, which allows more than two
                 levels of typing.",
}

@InProceedings{haynes:1996:esr,
  author =       "Thomas Haynes and Rose Gamble and Leslie Knight and
                 Roger Wainwright",
  title =        "Entailment for Specification Refinement",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "90--97",
  address =      "Stanford University, CA, USA",
  publisher_address = "Cambridge, MA, USA",
  publisher =    "MIT Press",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/theorem.ps",
  size =         "9 pages",
  abstract =     "Specification refinement is part of formal program
                 derivation, a method by which software is directly
                 constructed from a provably correct specification.
                 Because program derivation is an intensive manual
                 exercise used for critical software systems, an
                 automated approach would allow it to be viable for many
                 other types of software systems. The goal of this
                 research is to determine if genetic programming (GP)
                 can be used to automate the specification refinement
                 process. The initial steps toward this goal are to show
                 that a well--known proof logic for program derivation
                 can be encoded such that a GP--based system can infer
                 sentences in the logic for proof of a particular
                 sentence. The results are promising and indicate that
                 GP can be useful in aiding program derivation.",
  notes =        "GP-96",
}

@TechReport{Haynes:1995:CDG,
  author =       "Thomas Haynes",
  title =        "Clique Detection via Genetic Programming",
  number =       "UTULSA-MCS-95-02",
  institution =  "The University of Tulsa",
  year =         "1995",
  month =        apr # " 24,",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic Programming is utilized as a technique for
                 detecting cliques in a network. Candidate cliques are
                 represented in lists, and the lists are manipulated
                 such that larger cliques are formed from the
                 candidates. The clique detection problem has some
                 interesting implications to the Strongly Typed Genetic
                 Programming paradigm, namely in forming a class
                 hierarchy. The problem is also useful in that it is
                 easy to add noise.",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/tr_clique.ps",
}

@TechReport{Haynes:1996:CDGb,
  author =       "Thomas Haynes and Dale Schoenefeld",
  title =        "Clique Detection via Genetic Programming",
  number =       "UTULSA-MCS-96-05",
  institution =  "The University of Tulsa",
  month =        mar # " 15,",
  notes =        "Full version of GP'96 poster",
  year =         "1996",
  keywords =     "Genetic Programming, Genetic Algorithms",
  abstract =     "Genetic programming is applied to the task of finding
                 all of the cliques in a graph. Nodes in the graph are
                 represented as tree structures, which are then
                 manipulated to form candidate cliques. The intrinsic
                 properties of clique detection complicates the design
                 of a good fitness evaluation. We analyze those
                 properties, and show the clique detector is found to be
                 better at finding the maximum clique in the graph, not
                 the set of all cliques.",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/clique.ps",
}

@InProceedings{haynes:1996:cdGP,
  author =       "Thomas Haynes and Dale Schoenefeld",
  title =        "Clique Detection via Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "426",
  address =      "Stanford University, CA, USA",
  publisher_address = "Cambridge, MA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  abstract =     "Genetic programming is applied to the task of finding
                 all of the cliques in a graph. Nodes in the graph are
                 represented as tree structures, which are then
                 manipulated to form candidate cliques. The intrinsic
                 properties of clique detection complicates the design
                 of a good fitness evaluation. We analyze those
                 properties, and show the clique detector is found to be
                 better at finding the maximum clique in the graph, not
                 the set of all cliques.",
  notes =        "GP-96 see also technical report Haynes:1995:CDGb",
}

@TechReport{Haynes:1996:DCSa,
  author =       "Thomas Haynes",
  title =        "Duplication of Coding Segments in Genetic
                 Programming",
  number =       "UTULSA-MCS-96-03",
  institution =  "The University of Tulsa",
  month =        mar # " 11,",
  year =         "1996",
  keywords =     "Genetic Programming, Genetic Algorithms",
  notes =        "Longer version of AAAI '96 paper",
  abstract =     "Research into the utility of non--coding segments, or
                 introns, in genetic--based encodings has shown that
                 they expedite the evolution of solutions in domains by
                 protecting building blocks against destructive
                 crossover. We consider a genetic programming system
                 where non--coding segments can be removed, and the
                 resultant chromosomes returned into the population.
                 This parsimonious repair leads to premature
                 convergence, since as we remove the naturally occurring
                 non--coding segments, we strip away their protective
                 backup feature. We then duplicate the coding segments
                 in the repaired chromosomes, and place the modified
                 chromosomes into the population. The duplication method
                 significantly improves the learning rate in the domain
                 we have considered. We also show that this method can
                 be applied to other domains.",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/tr_duplicate.ps",
}

@InProceedings{Haynes:1996:DCSb,
  author =       "Thomas Haynes",
  title =        "Duplication of Coding Segments in Genetic
                 Programming",
  booktitle =    "Proceedings of the Thirteenth National Conference on
                 Artificial Intelligence",
  month =        aug,
  year =         "1996",
  address =      "Portland, OR",
  pages =        "344--349",
  keywords =     "Genetic Programming, Genetic Algorithms",
  notes =        "see also tech report Haynes:1996:DCSa",
  abstract =     "Research into the utility of non--coding segments, or
                 introns, in genetic--based encodings has shown that
                 they expedite the evolution of solutions in domains by
                 protecting building blocks against destructive
                 crossover. We consider a genetic programming system
                 where non--coding segments can be removed, and the
                 resultant chromosomes returned into the population.
                 This parsimonious repair leads to premature
                 convergence, since as we remove the naturally occurring
                 non--coding segments, we strip away their protective
                 backup feature. We then duplicate the coding segments
                 in the repaired chromosomes, and place the modified
                 chromosomes into the population. The duplication method
                 significantly improves the learning rate in the domain
                 we have considered. We also show that this method can
                 be applied to other domains.",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/duplicate.ps",
}

@InCollection{Haynes:1996:EBS,
  author =       "Thomas Haynes and Sandip Sen",
  title =        "Evolving Behavioral Strategies in Predators and Prey",
  pages =        "113--126",
  editor =       "Gerhard Wei{\ss} and Sandip Sen",
  booktitle =    "Adaptation and Learning in Multi--Agent Systems",
  year =         "1996",
  publisher =    "Springer Verlag",
  series =       "Lecture Notes in Artificial Intelligence",
  address =      "Berlin, Germany",
  keywords =     "Genetic Programming, Genetic Algorithms",
  notes =        "see also Hayes:1995:ebspp",
  abstract =     "The predator/prey domain is utilized to conduct
                 research in Distributed Artificial Intelligence.
                 Genetic Programing is used to evolve behavioral
                 strategies for the predator agents. To further the
                 utility of the predator strategies, the prey population
                 is allowed to evolve at the same time. The expected
                 competitive learning cycle did not surface. This
                 failing is investigated, and a simple prey algorithm
                 surfaces, which is consistently able to evade capture
                 from the predator algorithms.",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/icjai95.ps",
}

@TechReport{Haynes:1996:CF,
  author =       "Thomas Haynes and Sandip Sen",
  title =        "Cooperation of the Fittest",
  number =       "UTULSA-MCS-96-09",
  institution =  "The University of Tulsa",
  year =         "1996",
  month =        apr # " 12,",
  size =         "9+ pages",
  keywords =     "Genetic Programming, Genetic Algorithms",
  abstract =     "We introduce a cooperative co-evolutionary system to
                 facilitate the development of teams of heterogeneous
                 agents. We believe that $k$ different behavioral
                 strategies for controlling the actions of a group of
                 $k$ agents can combine to form a cooperation strategy
                 which efficiently achieves global goals. We examine the
                 on-line adaption of behavioral strategies utilizing
                 genetic programming. Specifically, we deal with the
                 credit assignment problem of how to fairly split the
                 fitness of a team to all of its participants. We
                 present several crossover mechanisms in a genetic
                 programming system to facilitate the evolution of more
                 than one member in the team during each crossover
                 operation. Our goal is to reduce the time needed to
                 either evolve a good team or reach convergence.",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/coopevol.ps",
  notes =        "evolution of cooperation (multi-agent,multi-tree) NOT
                 coevolution of fitness function evolution. Our printer
                 barfs on page 9.",
}

@InProceedings{haynes:1996:cms,
  author =       "Thomas Haynes",
  title =        "Collective Memory Search",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "38--46",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{haynes1996:cf,
  author =       "Thomas Haynes and Sandip Sen",
  title =        "Cooperation of the Fittest",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "47--55",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{haynes:1997:cms,
  author =       "Thomas Haynes",
  title =        "Collective Memory Search",
  booktitle =    "Proceedings of the 1997 ACM Symposium on Applied
                 Computing",
  year =         "1997",
  editor =       "Barrett Bryant and Janice Carroll and Dave Oppenheim
                 and Jim Hightower and K. M. George",
  pages =        "217--222",
  address =      "Hyatt Sainte Claire Hotel, San Jose, California, USA",
  publisher_address = "New York",
  month =        "28 " # feb # "-2 " # mar,
  publisher =    "Association for Computing Machinery",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ACM SAC-97 0-89791-850-9",
}

@InProceedings{Haynes:1997:adskr,
  author =       "Thomas Haynes",
  title =        "On-line Adaptation of Search via Knowledge Reuse",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "156--161",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{Haynes:1997:caet,
  author =       "Thomas Haynes and Sandip Sen",
  title =        "Crossover Operators for Evolving {A} Team",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "162--167",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{Haynes:1997:ccas,
  author =       "Thomas Haynes",
  title =        "Competitive Computational Agent Society",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "293",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@InProceedings{haynes:1997:pbbGP,
  author =       "Thomas Haynes",
  title =        "Phenotypical Building Blocks for Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Seventh
                 International Conference",
  year =         "1997",
  editor =       "Thomas Back",
  pages =        "26--33",
  address =      "Michigan State University, East Lansing, MI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "19-23 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-487-1",
  size =         "8 pages",
  abstract =     "The theoretical foundations of genetic algorithms (GA)
                 rest on the shoulders of the Schema Theorem, which
                 states that the building blocks, highly fit compact
                 subsets of the chromosome, are more likely to survive
                 from one generation to the next. The theory of genetic
                 programming (GP) is tenuous, borrowing heavily from
                 that of GA. As the GP can be considered to be a GA
                 operating on a tree structure, this borrowing is
                 adequate for most. Part of the problem of tying GP
                 theory to the schema theorem is in the identification
                 of building blocks. We discuss how a building block can
                 be represented in a GP chromosome and the
                 characteristics of building blocks in GP chromosomes.
                 We also present the clique detection domain for which
                 the detection of building blocks is easier than in
                 previous domains utilized in GP research. We illustrate
                 how the clique detection domain facilitates the
                 construction of fitness landscapes similar to those of
                 the Royal Road functions in GA research.",
  notes =        "ICGA-97",
}

@InProceedings{Haynes:1998:CRS,
  author =       "Thomas Haynes",
  title =        "A Comparision of Random Search versus Genetic
                 Programming as Engines for Collective Adaptation",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  volume =       "1447",
  series =       "LNCS",
  pages =        "683--692",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  organisation = "Natural Selection, Inc.",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  URL =          "http://www.cs.twsu.edu/~haynes/random.ps",
  abstract =     "We have integrated the distributed search of genetic
                 programming (GP) based systems with collective memory
                 to form a collective adaptation search method. Such a
                 system significantly improves search as problem
                 complexity is increased. Since the pure GP approach
                 does not scale well with problem complexity, a natural
                 question is which of the two components is actually
                 contributing to the search process. We investigate a
                 collective memory search which utilizes a random search
                 engine and find that it significantly outperforms the
                 GP based search engine. We examine the solution space
                 and show that as problem complexity and search space
                 grow, a collective adaptive system will perform better
                 than a collective memory search employing random search
                 as an engine.",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7

                 {"}With collective adaptation{"}.... {"}A random search
                 engine is more effective than a GP based one, but only
                 at low problem complexity. As the complexity increases,
                 the competetiveness of the GP search engine is more
                 effective than the wide ranging exploration of random
                 search.{"} pages 10-11.",
}

@PhdThesis{haynes:thesis,
  author =       "Thomas Dunlop Haynes",
  title =        "Collective Adaptation: The Sharing of Building
                 Blocks",
  school =       "Department of Mathematical and Computer Sciences,
                 University of Tulsa",
  year =         "1998",
  address =      "Tulsa, OK, USA",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.twsu.edu/~haynes/thesis.ps",
  size =         "147 pages (normal spacing)",
  abstract =     "Weak search heuristics utilize minimal domain
                 knowledge during the search process. Genetic algorithms
                 (GA) and genetic programming (GP) are population based
                 weak search heuristics which represent candidate
                 solutions as chromosomes. The Schemata Theorem forms
                 the basis of the theory of how GAs process building
                 blocks during the domain independent search for a
                 solution to a given problem. Building blocks are
                 templates describing subsets of the chromosome which
                 have a small defining length and are highly fit. The
                 main differences between typical GP and GA
                 implementations are a variable length tree versus a
                 fixed length linear string representation and a n-ary
                 versus a binary alphabet. A consequence of the
                 differences is that what constitutes a building block
                 has been difficult to answer for GP and has led to
                 theories that the Schemata Theorem does not hold for
                 GP.

                 This thesis defines building blocks to be coding
                 segments, which are those subsets of the chromosome
                 that contribute fitness to the evaluation of the
                 chromosome. Building blocks can be extracted from
                 chromosomes and stored in a collective memory, which
                 becomes a repository of partial solutions for both
                 recently discovered building blocks and those
                 discovered earlier. The contributions of this thesis
                 are the mechanisms by which building blocks can be
                 effectively shared both inside and outside
                 chromosomes.

                 The duplication of building blocks inside a chromosome
                 is shown to increase the exploratory power of the weak
                 search heuristics. The perturbation of a candidate
                 solution will affect one copy of the building blocks
                 and if the fitness of the perturbed copy is not better
                 than the original, the duplicate copies may still
                 maintain the overall fitness of the chromosome. The
                 duplication of coding segments is significant in
                 finding better partial solutions with the following
                 weak search heuristics: GP, GA, random search (RS),
                 hill climbing (HC), and simulated annealing (SA). Each
                 algorithm is systematically validated in the clique
                 detection domain against a particular family of graphs,
                 which have the properties that the set of partial
                 solutions is known, the set of partial solutions is
                 larger than viable chromosome lengths, and pruning
                 algorithms are not effective.

                 Collective adaptation is the addition of the collective
                 memory to the weak search heuristic. The solution no
                 longer has to be found inside the chromosomes; the
                 chromosomes can collectively contribute partial
                 solutions such that the overall solution is formed
                 inside the collective memory. Strong search heuristics
                 can extend the partial solutions inside the collective
                 memory and these partial solutions can be transfered
                 back into the chromosomes. The thesis empirically
                 demonstrates that collective adaptation finds
                 significantly better partial solutions with weak search
                 heuristics (GP, GA, RS, HC, and SA).",
}

@InProceedings{haynes:1998:acaspa,
  author =       "Thomas Haynes",
  title =        "Augmenting Collective Adaptation with Simple Process
                 Agents",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "116--121",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{haynes:1998:prdec,
  author =       "Thomas Haynes",
  title =        "Perturbing the Representation, Decoding, and
                 Evaluation of Chromosomes",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "122--127",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{haynes:1998:caxcs,
  author =       "Thomas Haynes",
  title =        "Collective Adaptation: The Exchange of Coding
                 Segments",
  journal =      "Evolutionary Computation",
  year =         "1998",
  volume =       "6",
  number =       "4",
  pages =        "311--338",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming, collective
                 adaptation, coding segments, duplication of coding
                 segments, collective memory",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=6&issue=4",
  abstract =     "Coding segments are those subsegments of the
                 chromosome that contribute positively to the fitness
                 evaluation of the chromosome. Clique detection is a
                 NP-complete problem in which we can detect such coding
                 segments. We extract coding segments from chromosomes,
                 and we investigate the duplication of coding segments
                 inside the chromosome and the collection of coding
                 segments outside of the chromosome. We find that
                 duplication of coding segments inside the chromosomes
                 provides a back-up mechanism for the search heuristics.
                 We further find local search in a collective memory of
                 coding segments outside of the chromosome, collective
                 adaptation, enables the search heuristic to represent
                 partial solutions that are larger than realistic
                 chromosomes lengths and to express the solution outside
                 of the chromosome.",
  notes =        "Special Issue: Variable-Length Representation and
                 Noncoding Segments for Evolutionary Algorithms Edited
                 by Annie S. Wu and Wolfgang Banzhaf",
}

@InProceedings{Haynes:1999:DCAa,
  author =       "Thomas Haynes",
  title =        "Distributed Collective Adaptation Applied to a Hard
                 Combinatorial Optimization Problem",
  booktitle =    "Proceedings of the 1999 ACM Symposium on Applied
                 Computing",
  year =         "1999",
  editor =       "Janice Carroll and Hisham Haddad and Dave Oppenheim
                 and Barrett Bryant and Gary B. Lamont",
  pages =        "339--343",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://adept.cs.twsu.edu/~thomas/mpi.ps",
  abstract =     "We use collective memory to integrate weak and strong
                 search heuristics to find cliques in FC, a family of
                 graphs. We construct FC such that pruning partial
                 solutions will be ineffective. Each weak heuristic
                 maintains a local cache of the collective memory. We
                 examine the impact on the distributed search of the
                 distribution of the collective memory, the search
                 algorithms, and our family of graphs. We find the
                 distributed search performs better than the individual
                 searches, even though the space of partial solutions is
                 combinatorial.",
  notes =        "(GA track)",
}

@InProceedings{Haynes:1999:DCAb,
  author =       "Thomas Haynes",
  title =        "Distributing Collective Adaptation via Message
                 Passing",
  booktitle =    "Proceedings of the 1999 ACM Symposium on Applied
                 Computing",
  year =         "1999",
  editor =       "Janice Carroll and Hisham Haddad and Dave Oppenheim
                 and Barrett Bryant and Gary B. Lamont",
  pages =        "501--505",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://adept.cs.twsu.edu/~thomas/cluster.ps",
  abstract =     "We describe an architecture for implementing a
                 distributed access to a collective memory on a cluster
                 of PC workstations running Linux. The basic memory
                 hierarchy of register, cache, RAM, and main storage is
                 modeled. The message passing interface (MPI) provides
                 the functionality of a virtual bus between the various
                 layers of memory.",
  notes =        "(PC Cluster track)",
}

@Proceedings{haynes:1999:fogp,
  title =        "Foundations of Genetic Programming",
  year =         "1999",
  editor =       "Thomas Haynes and William B. Langdon and Una-May
                 O'Reilly and Riccardo Poli and Justinian Rosca",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp",
  URL =          "ftp://ftp.mad-scientist.com/pub/genetic-programming/papers/fogp99.tar",
  size =         "20 pages",
  notes =        "GECCO'99 WKSHOP",
}

@InProceedings{heckendorn:1999:PTSSGM,
  author =       "Robert B. Heckendorn and Soraya Rana and Darrell
                 Whitley",
  title =        "Polynomial Time Summary Statistics for a
                 Generalization of {MAXSAT}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "281--288",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  abstract =     "NK landscape, Walsh analysis",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{Heiberg:1997:lbn,
  author =       "Vilhelm Heiberg",
  title =        "Learning Bayesian Networks Using a Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "86--97",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "for learning baysian networks .... intelligent Greedy
                 Search outperforms GA and SA",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{heiss-czedik:1997:highlevel,
  author =       "D. Heiss-Czedik",
  title =        "Is Genetic Programming Dependent on High-level
                 Primitives?",
  booktitle =    "ICANNGA97",
  year =         "1997",
  address =      "University of East Anglia, Norwich, UK",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}

@InProceedings{helmer:1999:FSUGAID,
  author =       "Guy Helmer and Johnny Wong and Vasant Honavar and Les
                 Miller",
  title =        "Feature Selection Using a Genetic Algorithm for
                 Intrusion Detection",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1781",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hemberg:2001:adtsg,
  author =       "Martin Hemberg and Una-May O'Reilly and Peter Nordin",
  title =        "{GENR8} - {A} Design Tool for Surface Generation",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "160--167",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, architecture,
                 Lindenmayer systems, BNF grammar, HEMLS, grammatical
                 evolution, Alias|Wavefront Maya",
  notes =        "GECCO-2001LB",
}

@InProceedings{hemberg:2001:adtsg2,
  author =       "Martin Hemberg and Una-May O'Reilly",
  title =        "{GENR8} - {A} Design Tool for Surface Generation",
  booktitle =    "Graduate Student Workshop",
  year =         "2001",
  editor =       "Conor Ryan",
  pages =        "413--416",
  address =      "San Francisco, California, USA",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS, see
                 hemberg:2001:adtsg",
}

@InProceedings{hemberg:2002:gecco:workshop,
  title =        "{GENR8} - Using Grammatical Evolution In {A} Surface
                 Design Tool",
  author =       "Martin Hemberg and Una-May O'Reilly",
  pages =        "120--123",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{hemert:2001:gecco,
  title =        "An Engineering Approach to Evolutionary Art",
  author =       "J. I. van Hemert and M. L. M. Jansen",
  pages =        "177",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster, art,
                 abstract, Internet, human induced fitness function,
                 subjective, gene bank",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InCollection{henry:1994:ca,
  author =       "Kelvin C. Henry",
  title =        "Exploring Cellular Automata Using a Two-Dimensional
                 Genetic Algorithm",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "57--66",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, life, GENESIS",
  ISBN =         "0-18-187263-3",
  notes =        "GA generates rules for Cellular Automata aimming to
                 select those that support proporgating structures. Life
                 used as comparison.

                 This volume contains 20 papers written and submitted by
                 students describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{hernandez:1999:SDMEACS,
  author =       "German Hernandez and Jerome A. Goldstein and Fernando
                 Niao",
  title =        "Stochastic Differential Model for Evolutionary
                 Algorithms over Continuous Spaces",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "863--870",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hernandez-aguirre:2000:gsbfbmgp,
  author =       "Arturo Hernandez-Aguirre and Bill P. Buckles and
                 Carlos A. Coello-Coello",
  title =        "Gate-level Synthesis of Boolean Functions using Binary
                 Multiplexers and Genetic Programming",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "675--682",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, hybrid
                 systems",
  ISBN =         "0-7803-6375-2",
  abstract =     "This paper presents a genetic programming approach for
                 the synthesis of logic functions by means of
                 multiplexers. The approach uses the 1-control line
                 multiplexer as the only design unit. Any logic function
                 (defined by a truth table) can be produced through the
                 replication of this single unit. Our fitness function
                 works in two stages: first, it finds feasible
                 solutions, and then it concentrates on the minimization
                 of the circuit. The proposed approach does not require
                 any knowledge from the appli-cation domain.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@InProceedings{hetland:2002:RASC,
  author =       "Magnus Lie Hetland and P°al Sætrom",
  title =        "Temporal Rule Discovery using Genetic Programming and
                 Specialized Hardware",
  booktitle =    "4th International Conference on Recent Advances in
                 Soft Computing",
  year =         "2002",
  address =      "Nottingham, United Kingdom",
  month =        "12-13 " # dec,
  keywords =     "genetic algorithms, genetic programming, Time series,
                 sequence mining, rule discovery, pattern matching
                 hardware",
  URL =          "http://hetland.org/research/2002/sc2103.pdf",
  abstract =     "Discovering association rules is a well-established
                 problem in the field of data mining, with many existing
                 solutions. In later years, several methods have been
                 proposed for mining rules from sequential and temporal
                 data. This paper presents a novel technique based on
                 genetic programming and specialized pattern matching
                 hardware. The advantages of this method are its
                 exibility and adaptability, and its ability to produce
                 intelligible rules of considerable complexity.",
  notes =        "RASC http://www.rasc2002.info/",
}

@InProceedings{hewgill:2002:gecco:lbp,
  title =        "Real-Time Competitive Evolutionary Computation",
  author =       "Adam Hewgill",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "228--232",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming, alife",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 lil-gp used to evolve brains for robot fish in
                 simulated aquarium",
}

@InCollection{hewlett:1998:RNUGPVFDO,
  author =       "William R. Hewlett",
  title =        "Reynolds Numbers: Using Genetic Programming and Vite
                 to find Formulas to Describe Organizations",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "20--28",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{heywood:2000:rbGPFPGA,
  author =       "M. I. Heywood and A. N. Zincir-Heywood",
  title =        "Register Based Genetic Programming on {FPGA} Computing
                 Platforms",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "44--59",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "The use of FPGA based custom computing platforms is
                 proposed for implementing linearly structured Genetic
                 Programs. Such a context enables consideration of micro
                 architectural and instruction design issues not
                 normally possible when using classical Von Neumann
                 machines. More importantly, the desirability of
                 minimising memory management overheads results in the
                 imposition of additional constraints to the crossover
                 operator. Specifically, individuals are described in
                 terms of the number of pages and page length, where the
                 page length is common across individuals of the
                 population. Pairwise crossover therefore results in the
                 swapping of equal length pages, hence minimising memory
                 overheads. Simulation of the approach demonstrates that
                 the method warrants further study.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{Heywood:2000:PBGP,
  author =       "M. I. Heywood and A. N. Zincir-Heywood",
  title =        "Page-based linear genetic programming",
  booktitle =    "Systems, Man, and Cybernetics, 2000 IEEE International
                 Conference",
  year =         "2000",
  volume =       "5",
  pages =        "3823--3828",
  address =      "IEEE Press",
  month =        "8-11 " # oct,
  keywords =     "genetic algorithms, genetic programming, page-based
                 linear genetic programming, evolutionary computation,
                 computational overheads, fitness of individuals,
                 crossover operator, equal length code fragments,
                 register-machine, a priori internal register external
                 output definitions",
  ISBN =         "0-7803-6583-6",
  URL =          "http://ieeexplore.ieee.org/iel5/7099/19140/00886606.pdf?isNumber=19140",
  size =         "6 pages",
  abstract =     "Genetic programming arguably represents the most
                 general form of evolutionary computation. However, such
                 generality is not without significant computational
                 overheads. Particularly, the cost of evaluating the
                 fitness of individuals in any form of evolutionary
                 computation represents the single most significant
                 computational bottleneck. A less widely acknowledged
                 computational overhead in GP involves the
                 implementation of the crossover operator. To this end a
                 page-based definition of individuals is used to
                 restrict crossover to equal length code fragments.
                 Moreover, by using a register-machine context, the
                 significance of a priori internal register external
                 output definitions is emphasized.",
}

@Article{heywood:2002:SMCB,
  author =       "M. I. Heywood and A. N. Zincir-Heywood",
  title =        "Dynamic Page Based Crossover in Linear Genetic
                 Programming",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics:
                 Part B - Cybernetics",
  year =         "2002",
  volume =       "32",
  number =       "3",
  pages =        "380--388",
  month =        jun,
  email =        "mheywood@cs.dal.ca",
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming",
  ISSN =         "1083-4419",
  abstract =     "Page-based Linear Genetic Programming (GP) is proposed
                 in which individuals are described in terms of a number
                 of pages. Pages are expressed in terms of a fixed
                 number of instructions, constant for all individuals in
                 the population. Pairwise crossover results in the
                 swapping of single pages, thus individuals are of a
                 fixed number of instructions. Head-to-head comparison
                 with Tree structured GP and block-based Linear GP
                 indicates that the page-based approach evolves succinct
                 solutions without penalizing generalization ability.",
}

@TechReport{hiden:1996:npcaGP,
  author =       "H. G. Hiden and M. J. Willis and P. Turner and M. T.
                 Tham and G. A. Montague",
  title =        "Non-linear Principal Components Analysis Using Genetic
                 Programming",
  institution =  "Chemical Engineering, Newcastle University",
  year =         "1996",
  address =      "UK",
  note =         "Extended Abstract, ICANNGA '97, Norwick, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper9a.ps",
  abstract =     "

                 The recent explosion of low-cost computing power and
                 information storage has brought with it a corresponding
                 mushrooming in the amount of data on almost any subject
                 conceivable that is available. The philosophy that
                 {"}you can’t have enough information{"} seems to have
                 been applied to every situation with great enthusiasm.
                 By adopting such an approach, much useful data can be
                 gathered, however it is all too frequently swamped by
                 irrelevant information. The distinction must be made
                 between useful information and information for the sake
                 of having it. The chemical industry also has not been
                 immune to the data collection bug. The equipment
                 required to collect, process and store data is more
                 affordable than ever, a fact which the designers of
                 chemical processes are beginning to exploit.
                 Unfortunately, this data is not particularly useful on
                 its own. It is very easy to collect data, but difficult
                 to analyse it productively. It is this situation that
                 has spawned a wide variety of data analysis tools, the
                 objective of which is to determine underlying
                 relationships and structures within large data sets.",
  notes =        "MSword postscript not compatible with unix.

                 ",
}

@InProceedings{hiden:1997:ndddmGP,
  author =       "Hugo Hiden and Mark Willis and  Ben McKay and Gary
                 Montague",
  title =        "Non-Linear And Direction Dependent Dynamic Modelling
                 Using Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "168--173",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{hinden:1997:npcaGAL,
  author =       "Hugo Hiden and Mark Willis and Ming Tham and Paul
                 Turner and Gary Montague",
  title =        "Non-Linear Principal Components Analysis using Genetic
                 Programming",
  booktitle =    "Second International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1997",
  editor =       "Ali Zalzala",
  address =      "University of Strathclyde, Glasgow, UK",
  publisher_address = "Savoy Place, London WC2R 0BL, UK",
  month =        "1-4 " # sep,
  publisher =    "Institution of Electrical Engineers",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper13.ps",
  abstract =     "Principal Components Analysis (PCA) is a standard
                 statistical technique, which is frequently employed in
                 the analysis of large highly correlated data-sets. As
                 it stands, PCA is a linear technique which can limit
                 its relevance to the highly non-linear systems
                 frequently encountered in the chemical process
                 industries. Several attempts to extend linear PCA to
                 cover non-linear data sets have been made, and will be
                 briefly reviewed in this paper. We propose a
                 symbolically oriented technique for non-linear PCA,
                 which is based on the Genetic Programming (GP)
                 paradigm. Its applicability will be demonstrated using
                 two simple non-linear systems and industrial data
                 collected from a distillation column. It is suggested
                 that the use of the GP based non-linear PCA algorithm
                 achieves the objectives of non-linear PCA, while giving
                 high a degree of structural parsimony.",
  notes =        "GALESIA'97",
}

@InProceedings{hiden:1997:GPndmcps,
  author =       "H. G. Hiden and M. J. Willis and G. A. Montague",
  title =        "Using Genetic Programming to Develop Non-Linear
                 Dynamic Models of Chemical Process Systems",
  booktitle =    "IChemE Jubilee Research Event",
  year =         "1997",
  volume =       "2",
  pages =        "789--792",
  address =      "Nottingham, UK",
  month =        "8-9 " # apr,
  organisation = "Institute of Chemical Engineers",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Comparison of GP with feedforward ANN and finite
                 Impulse response model",
}

@InProceedings{hiden:1998:plsGP,
  author =       "Hugo Hiden and Ben McKay and Mark Willis and Gary
                 Montague",
  title =        "Non-Linear Partial Least Squares using Genetic
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "128--133",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{higuchi:1994:evaa,
  author =       "Tetsuya Higuchi and Hitoshi IBA and Bernard
                 Manderick",
  title =        "Applying Evolvable Hardware to Autonomous Agents",
  booktitle =    "Parallel Problem Solving from Nature III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and Reinhard
                 M{\"a}nner",
  series =       "LNCS",
  volume =       "866",
  pages =        "524--533",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, reinforcement learning, Evovable
                 Hardware",
  ISBN =         "3-540-58484-6",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
  abstract =     "Describes software reconfigurable logic device which
                 changes its own hardware to adapt to its environment.",
  notes =        "PPSN3",
}

@InProceedings{hikage:1998:cemse,
  author =       "Tomofumi Hikage and Hitoshi Hemmi and Katsunori
                 Shimohara",
  title =        "Comparison of Evolutionary Methods for Smoother
                 Evolution",
  booktitle =    "Proceedings of the Second International Conference on
                 Evolvable Systems: From Biology to Hardware (ICES 98)",
  year =         "1998",
  editor =       "Moshe Sipper and Daniel Mange and Andrés Pérez-Uribe",
  volume =       "1478",
  series =       "LNCS",
  pages =        "115--124",
  address =      "Lausanne, Switzerland",
  publisher_address = "Berlin",
  month =        "23-25 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming, HDL",
  ISBN =         "3-540-64954-9",
  URL =          "http://link.springer.de/link/service/series/0558/papers/1478/14780115.pdf",
  size =         "8 pages",
  abstract =     "Hardware evolution methodologies come into their own
                 in the construction of real-time adaptive systems. The
                 technological requirements for such systems are not
                 only high-speed evolution, but also steady and smooth
                 evolution. This paper shows that the Progressive
                 Evolution Model (PEM) and Diploid chromosomes
                 contribute toward satisfying these requirements in the
                 hardware evolutionary system AdAM (Adaptive
                 Architecture Methodology). Simulations of an artificial
                 ant problem using four combinations of two wets of
                 variables - PEM vs. non-PEM, and Diploid AdAM vs.
                 Haploid AdAM - show that the Diploid-PEM combination
                 overwhelms the others.",
  notes =        "ICES98 Chromosome is parse-tree for SFL (HDL).
                 Simulation. Dominace recessive tags in trees. Ant is
                 _assumed_ to be able to solve problem entirely from its
                 current sensor readings, ie no memort",
}

@InCollection{alife92:hillis,
  author =       "W. Daniel Hillis",
  title =        "Co-evolving Parasites Improve Simulated Evolution as
                 an Optimization Procedure",
  booktitle =    "Artificial Life II",
  publisher =    "Addison-Wesley",
  year =         "1992",
  pages =        "313--324",
  month =        feb # " 1990",
  address =      "Santa Fe Institute, New Mexico, USA",
  editor =       "Christopher G. Langton and Charles Taylor and J. Doyne
                 Farmer and Steen Rasmussen",
  volume =       "X",
  keywords =     "genetic algorithms",
  series =       "Santa Fe Institute Studies in the Sciences of
                 Complexity",
  abstract =     "Evolves sorting networks. Tests evolved at same time
                 lead to better solutions. Also aim to reduced testing
                 effort.",
  notes =        "Not in index, see page 313-324",
  size =         "12 pages",
}

@TechReport{hinchcliffe:1996:c2GPcpsm,
  author =       "Mark Hinchliffe and Mark Willis and Hugo Hiden and
                 Ming Tham",
  title =        "A comparison of two Genetic Programming Algorithms
                 Applied to Chemical Process Systems Modelling",
  institution =  "Chemical Engineering, Newcastle University",
  year =         "1996",
  address =      "UK",
  note =         "Extended Abstract, submitted to: ICANNGA '97, Norwick,
                 UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper10a.ps",
  abstract =     "Previous work by Mckay et al (1996a,b,c) has shown
                 that the Genetic programming (GP) methodology can be
                 successfully applied to the development of non-linear
                 steady state models of industrial chemical processes.
                 Although a GP algorithm can identify the relevant input
                 variables and evolve parsimonious system
                 representations, the resulting model structures tend to
                 contain little or no information relating to the
                 mechanisms of the process itself. In this respect, the
                 performance of the GP methodology is comparable to that
                 of other ‘black-box’ modelling techniques such as
                 neural networks. Chemical process systems are often
                 extremely complex and non-linear in nature.
                 Phenomenological models are time consuming to develop
                 and can be subject to inaccuracies caused by any
                 simplifying assumptions made. Consequently, mechanistic
                 models are costly to construct; an aspect which would
                 make an automated procedure highly desirable.
                 Phenomenological models are usually derived by applying
                 the laws of conservation of mass, energy and momentum
                 to the system. An examination of a number of
                 steady-state mechanistic models shows that they tend to
                 be made up of distinct sub-groups which, when added
                 together, give the overall model structure. In the
                 search for an automatic model generating algorithm, it
                 would be extremely useful if the GP methodology could
                 be utilised to identify these sub-groups. This could
                 potentially enhance the GP algorithm’s ability to
                 evolve accurate chemical process models and also help
                 to reveal hidden process knowledge. To achieve this
                 goal, the standard GP algorithm used by McKay et al
                 (1996a) was modified to accommodate the multiple gene
                 model structure. The multiple gene structure was
                 introduced by Altenberg (1994) in an attempt to enhance
                 the learning capabilities of GA and GP algorithms. The
                 work was inspired by the observation that, in nature,
                 genetic information is stored on more than one gene. To
                 demonstrate the feasibility of this new approach, ‘real
                 world’ examples are used to compare the performance of
                 the algorithm with that of the standard GP algorithm.",
  notes =        "MSword postscript not camptible with unix",
  size =         "7 pages",
}

@InProceedings{hinchliffe:1996:mcpsm-g,
  author =       "Mark Hinchliffe and Hugo Hiden and Ben McKay and Mark
                 Willis and Ming Tham and Geoffery Barton",
  title =        "Modelling Chemical Process Systems Using a Multi-Gene
                 Genetic Programming Algorithm",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "56--65",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper7.ps",
  abstract =     "In this contribution a multi-gene Genetic Programming
                 (Gp) Algorithm is used to evolve input output models of
                 chemical process systems. Three case studies are used
                 to demonstrate the performance of the method when
                 compared to a standard GP algorithm. A statistical
                 analysis procedure is used to aid in the assessment of
                 the results and suggest the number of independent runs
                 required to obtain a successful result. It is concluded
                 that the multi-gene algorithm provides superior
                 performance, as partitioning the problem into
                 sub-groups incorporates basic heuristic knowledge of
                 the search space.",
  notes =        "GP-96LB MSword .ps file not compatible with unix The
                 email address for the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@InProceedings{hinchliffe:1998:cpsmumoGP,
  author =       "Mark Hinchliffe and Mark Willis and Ming Tham",
  title =        "Chemical Process Sytems Modelling Using
                 Multi-objective Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "134--139",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{hinchliffe:1999:DCPMUMBFGPA,
  author =       "Mark Hinchliffe and Mark Willis and Ming Tham",
  title =        "Dynamic Chemical Process Modelling Using a Multiple
                 Basis Function Genetic Programming Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1782",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, poster papers, NARMAX",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hirasawa:2001:cgnpgp,
  author =       "Kotaro Hirasawa and M. Okubo and J. Hu and J. Murata",
  title =        "Comparison between Genetic Network Programming ({GNP})
                 and Genetic Programming ({GP})",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1276--1282",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, genetic
                 programming Network, Evolution, Ant behaviors",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 GNP directed graph: judgement, time delay, processing
                 nodes. Network genome. subnet swapping crossover. Ant
                 pheremone square 32 by 32 grid world, food gathering.",
}

@Article{hirsh:2000:GP,
  author =       "Haym Hirsh and Wolfgang Banzhaf and John R. Koza and
                 Conor Ryan and Lee Spector and Christian Jacob",
  title =        "Genetic Programming",
  journal =      "IEEE Intelligent Systems",
  year =         "2000",
  volume =       "15",
  number =       "3",
  pages =        "74--84",
  month =        may # "-" # jun,
  keywords =     "genetic algorithms, genetic programming, artificial
                 computer code evolution, machine intelligence,
                 automatic programming, arbitrary computational
                 processes",
  ISSN =         "1094-7167",
  URL =          "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf",
  size =         "11 pages",
  abstract =     "The paper presents essays on genetic programming which
                 involve topics such as: the artificial evolution of
                 computer code, human-competitive machine intelligence
                 by means of genetic programming, GP as automatic
                 programming, GP application, the evolution of arbitrary
                 computational processes and the art of genetic
                 programming.",
  notes =        "Collection of essays by each author with introduction
                 by Hirsh. See banzhaf:2000:IS, koza:2000:IS,
                 ryan:2000:IS, spector:2000:IS, jacob:2000:IS.",
}

@InCollection{ho:1994:gqo,
  author =       "Alex Ho and George Lumpkin",
  title =        "The Genetic Query Optimizer",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "67--76",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, Oracle Corporation, Relational
                 Database Query",
  ISBN =         "0-18-187263-3",
  abstract =     "{"}For complex queries, we find that the genetic
                 algorithm produces more efficient query plans in a
                 running time comparable to that of conventional
                 methods{"}.",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{ho:1999:AEGMEA,
  author =       "Shinn-Ying Ho and Xiao-I Chang",
  title =        "An Efficient Generalized Multiobjective Evolutionary
                 Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "871--878",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ho:1999:IGANICUOA,
  author =       "Shinn-Ying Ho and Li-Sun Shu and Hung-Ming Chen",
  title =        "Intelligent Genetic Algorithm with a New Intelligent
                 Crossover Using Orthogonal Arrays",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "289--296",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ho:1999:SLKBPPUIGA,
  author =       "Shinn-Ying Ho and Hung-Ming Chen and Li-Sun Shu",
  title =        "Solving Large Knowledge Base Partitioning Problems
                 Using an Intelligent Genetic Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1567--1572",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hoai:2001:HIS,
  title =        "Solving Trignometric Identities with Tree Adjunct
                 Grammar Guided Genetic Programming",
  author =       "N. X. Hoai",
  editor =       "Ajith Abraham and Mario Koppen",
  booktitle =    "2001 International Workshop on Hybrid Intelligent
                 Systems",
  series =       "LNCS",
  pages =        "339--352",
  publisher =    "Springer-Verlag",
  address =      "Adelaide, Australia",
  publisher_address = "Berlin",
  month =        "11-12 " # dec,
  year =         "2001",
  email =        "x.nguyen@student.adfa.edu.au",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6",
  ISBN =         "3-7908-1480-6",
  keywords =     "genetic algorithms, genetic programming, Grammar
                 Guided Genetic Progrogramming, Tree-Adjunct Grammars,
                 Trigonometric Identity Discovery",
  abstract =     "Tree-adjunct grammar guided genetic programming
                 (TAG3P) (Hoai and McKay 2001) is a grammar guided
                 genetic programming system that uses context-free
                 grammars along with tree-adjunct grammars as means to
                 set language bias for the genetic programming system.
                 In this paper, we show the result of TAG3P on the
                 problem of discovering trigonometric identities, one of
                 the benchmark problems in genetic programming (Koza
                 1992). The results show that although TAG3P did
                 successfully discover all three popular trigonometric
                 identities of the trigonometric function cos(2x),
                 namely, sin(2x+p /2), sin(p /2 -2x) and 1-2sin 2 (x),
                 it had a tendency to converge towards the first two
                 identities.",
  notes =        "HIS01",
}

@InProceedings{hoai:2002:EuroGP,
  title =        "Some Experimental Results with Tree Adjunct Grammar
                 Guided Genetic Programming",
  author =       "Nguyen Xuan Hoai and R. I. McKay and D. Essam",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "228--237",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "Tree-adjunct grammar guided genetic programming
                 (TAG3P) [5] is a grammar guided genetic programming
                 system that uses context -free grammars along with
                 tree-adjunct grammars as means to set language bias for
                 the genetic programming system. In this paper, we show
                 the experimental results of TAG3P on two problems:
                 symbolic regression and trigonometric identity
                 discovery. The results show that TAG3P works well on
                 those problems.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{hoai:2002:stsrpwtgggptcr,
  author =       "N. X. Hoai and R. I. McKay and D. Essam and R. Chau",
  title =        "Solving the Symbolic Regression Problem with
                 Tree-Adjunct Grammar Guided Genetic Programming: The
                 Comparative Results",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "1326--1331",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We show some experimental results of tree-adjunct
                 grammar guided genetic programming [6] (TAG3P) on the
                 symbolic regression problem, a benchmark problem in
                 genetic programming. We compare the results with
                 genetic programming [9] (GP) and grammar guided genetic
                 programming [14] (GGGP). The results show that TAG3P
                 significantly outperforms GP and GGGP on the target
                 functions attempted in terms of probability of success.
                 Moreover, TAG3P still performed well when the
                 structural complexity of the target function was scaled
                 up.",
}

@InProceedings{hocaoglu:1998:,
  author =       "Cem Hocaoglu and Arthur C. Sanderson",
  title =        "Multi-dimensional Path Planning Evolutionary
                 Computation using Evolutionary Computation",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "165--170",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c029.pdf",
  size =         "6 pages",
  abstract =     "This paper describes a flexible and efficient
                 multi-dimensional path planning algorithm based on
                 evolutionary computation concepts. A novel iterative
                 multi-resolution path representation is used as a basis
                 for the GA coding. The use of a multi-resolution path
                 representation can reduce the expected search length
                 for the path planning problem. If a successful path is
                 found early in the search hierarchy (at a low level of
                 resolution), then further expansion of that portion of
                 the path search is not necessary. This advantage is
                 mapped into the encoded search space and adjusts the
                 string length accordingly. The algorithm is flexible;
                 it handles multi-dimensional path planning problems,
                 accommodates different optimization criteria and
                 changes in these criteria, and it utilizes domain
                 specific knowledge for making decisions. In the
                 evolutionary path planner, the individual candidates
                 are evaluated with respect to the workspace so that
                 computation of the configuration space is not required.
                 The algorithm can be applied for planning paths for
                 mobile robots, assembly, pianomovers problems and
                 articulated manipulators. The effectiveness of the
                 algorithm is demonstrated on a number of
                 multi-dimensional path planning problems.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@InProceedings{hoehn:1999:PCMGAAII,
  author =       "Theodore P. Hoehn and Chrisila C. Pettey",
  title =        "Parental and Cyclic-Rate Mutation in Genetic
                 Algorithms: An Initial Investigation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "297--304",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{hoffman:1999:UGADCDHCEP,
  author =       "Don Hoffman",
  title =        "Using Genetic Algorithms for Data Compression:
                 Discovering Huffman Codes as Efficiently as Possible",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "58--67",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{hoffmann:1998:itfcES,
  author =       "Frank Hoffmann",
  title =        "Incremental Tuning of Fuzzy Controllers by Means of an
                 Evolution Strategy",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "843--851",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Evolutionary Strategies",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{Hoffmann:2001:IS,
  author =       "Frank Hoffmann and Oliver Nelles",
  title =        "Genetic programming for model selection of {TSK}-fuzzy
                 systems",
  journal =      "Information Sciences",
  year =         "2001",
  volume =       "136",
  number =       "1-4",
  pages =        "7--28",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Fuzzy
                 modeling, Neuro-fuzzy system",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-43DDW06-2/1/69cfc0ce8977ebea74cb8cec74efa722",
  abstract =     "This paper compares a genetic programming (GP)
                 approach with a greedy partition algorithm (LOLIMOT)
                 for structure identification of local linear
                 neuro-fuzzy models. The crisp linear conclusion part of
                 a Takagi-Sugeno-Kang (TSK) fuzzy rule describes the
                 underlying model in the local region specified in the
                 premise. The objective of structure identification is
                 to identify an optimal partition of the input space
                 into Gaussian, axis-orthogonal fuzzy sets. The linear
                 parameters in the rule consequent are then estimated by
                 means of a local weighted least-squares algorithm.
                 LOLIMOT is an incremental tree-construction algorithm
                 that partitions the input space by axis-orthogonal
                 splits. In each iteration it greedily adds the new
                 model that minimizes the classification error. GP
                 performs a global search for the optimal partition tree
                 and is therefore able to backtrack in case of
                 sub-optimal intermediate split decisions. We compare
                 the performance of both methods for function
                 approximation of a highly non-linear two-dimensional
                 test function and an engine characteristic map.",
}

@InProceedings{hofmeyr:1999:IDAAIS,
  author =       "Steven A. Hofmeyr and Stephanie Forrest",
  title =        "Immunity by Design: An Artificial Immune System",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1289--1296",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{holmes:1995:odin,
  author =       "Paul Holmes",
  title =        "The Odin Genetic Programming System",
  institution =  "Computer Studies, Napier University",
  year =         "1995",
  type =         "Tech Report",
  number =       "RR-95-3",
  address =      "Craiglockhart, 216 Colinton Road, Edinburgh, EH14
                 1DJ",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.dcs.napier.ac.uk/pub/papers/rr-95-3.ps",
  abstract =     "A new paradigm for Genetic Programming (GP) is
                 proposed. In the new paradigm the genetic
                 representation is separated from the tree structure of
                 the program with a layer of abstraction, and it is
                 argued that this will allow more efficient evolution of
                 large programs. A GP system which can evolve
                 Turing-complete programs has been developed and is
                 presented. Emphasis is placed on the evolution of
                 real-time functional programs which handle input and
                 output using lazy streams.
                 http://docs.dcs.napier.ac.uk/DOCS/GET/holmes95a/document.html",
  notes =        "Fixed length chromosome, 8 bytes per line of code,
                 Initial population seeded by individual written by user
                 in Odin and compiled to Runes.

                 Functional language, naturally recursive. Domiance bits
                 used to arbitrate order iff conflict between which
                 function to apply. Destructive translocation of genes
                 (desctructive as fixed length) 8byte code interpretted
                 by G-Machine (Antoni Diller) cf Peyton Jones. Standard
                 GA (D-Genesis) crossover and mutation (does it respect
                 opcodes and their boundaries?) Fitness function
                 similarity of output (which may be list of some data
                 type) with user supplied data (ie user also specifies
                 functional language style type of output)

                 page 49 {"}Its [Odin's] relative effectiveness remains
                 to be tested.{"}",
  size =         "56 pages",
}

@InProceedings{holmes:1996:fllc,
  author =       "Paul Holmes and Peter J. Barclay",
  title =        "Functional Languages on Linear Chromosomes",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "427",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96. See also holmes:1995:odin",
}

@InProceedings{holmes:1998:dnricslr2pubr,
  author =       "John H. Holmes",
  title =        "Differential Negative Reinforcement Improves
                 Classifier System Learning Rate in Two-Class Problems
                 with Unequal Base Rates",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "635--642",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers, ROC",
  ISBN =         "1-55860-548-7",
  URL =          "http://cceb.med.upenn.edu/holmes/gp98.ps.gz",
  size =         "8 pages",
  abstract =     "The effect of biasing negative reinforcement levels on
                 learning rate and classification accuracy in a learning
                 classifier system (LCS) was investigated. Simulation
                 data at five prevalences (base rates) were used to
                 train and test the LCS. Erroneous decisions made by the
                 LCS during training were punished differentially
                 according to type: false positive (FP) or false
                 negative (FN), across a range of four FP:FN ratios.
                 Training performance was assessed by learning rate,
                 determined from the number of iterations required to
                 reach 95% of the maximum area under the receiver
                 operating characteristic (ROC) curve obtained during
                 learning. Learning rates were compared across the three
                 biased ratios with those obtained at the unbiased
                 ratio. Classification performance of the LCS at testing
                 was evaluated by means of the area under the ROC curve.
                 During learning, differences were found between the
                 biased and unbiased penalty schemes, but only at
                 unequal base rates. A linear relationship between bias
                 level and base rate was suggested. With unequal base
                 rates, biasing the FP:FN ratio improved the learning
                 rate. Little effect was observed on testing the LCS
                 with novel cases.",
  notes =        "GP-98. My version of ghostview barfs with
                 gp98.ps.gz

                 AUC=probability classifier is correct on
                 postive-negative test (Green and Swets, 1966). Wilcoxon
                 statistic (Hanley and McNeil, 1982).",
}

@InProceedings{holmes:1999:ELCSPITDTALMT,
  author =       "John H. Holmes",
  title =        "Evaluating Learning Classifier System Performance In
                 Two-Choice Decision Tasks: An {LCS} Metric Toolkit",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "789",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{Homaifar1995,
  author =       "Abdollah Homaifar and Ed McCormick",
  title =        "Simultaneous Design of Membership Functions and Rule
                 Sets for Fuzzy Controllers Using Genetic Algorithms",
  journal =      "IEEE Transactions on Fuzzy Systems",
  volume =       "3",
  number =       "2",
  year =         "1995",
  pages =        "129--139",
  month =        may,
  keywords =     "genetic algorithms, fuzzy control, control system
                 synthesis, membership function design, fuzzy
                 controllers, high-performance membership functions,
                 simultaneous design procedure, rule set design, cart
                 controller, truck controller",
  ISSN =         "1063-6706",
  URL =          "http://ieeexplore.ieee.org/iel4/91/8807/00388168.pdf?isNumber=8807",
  size =         "11 pages",
  abstract =     "This paper examines the applicability of genetic
                 algorithms (GA's) in the simultaneous design of
                 membership functions and rule sets for fuzzy logic
                 controllers. Previous work using genetic algorithms has
                 focused on the development of rule sets or high
                 performance membership functions; however, the
                 interdependence between these two components suggests a
                 simultaneous design procedure would be a more
                 appropriate methodology. When GA's have been used to
                 develop both, it has been done serially, e.g., design
                 the membership functions and then use them in the
                 design of the rule set. This, however, means that the
                 membership functions were optimized for the initial
                 rule set and not the rule set designed subsequently.
                 GA's are fully capable of creating complete fuzzy
                 controllers given the equations of motion of the
                 system, eliminating the need for human input in the
                 design loop. This new method has been applied to two
                 problems, a cart controller and a truck controller.
                 Beyond the development of these controllers, we also
                 examine the design of a robust controller for the cart
                 problem and its ability to overcome faulty rules.",
}

@InProceedings{Homaifar:1999:CIRA,
  author =       "Abdollah Homaifar and Daryl Battle and Edward
                 Tunstel",
  title =        "Soft computing-based design and control for mobile
                 robot path tracking",
  booktitle =    "Computational Intelligence in Robotics and Automation,
                 CIRA '99. Proceedings. 1999 IEEE International
                 Symposium on",
  year =         "1999",
  pages =        "35--40",
  month =        "8-9 " # nov,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, soft computing-based design, mobile robot,
                 robot path tracking, evolutionary algorithms, Darwinian
                 concepts, automatic learning, nonlinear mappings,
                 genetic programming, fuzzy control rules, autonomous
                 vehicle, steering control problem, membership
                 functions, rule bases, robustness, sensor measurement
                 noise, nominal forward velocity",
  ISBN =         "0-7803-5806-6",
  URL =          "http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587",
  size =         "6 pages",
  abstract =     "A variety of evolutionary algorithms, operating
                 according to Darwinian concepts, have been proposed to
                 approximately solve problems of common engineering
                 applications. Increasingly common applications involve
                 automatic learning of nonlinear mappings that govern
                 the behavior of control systems. In many cases where
                 robot control is of primary concern, the systems used
                 to demonstrate the effectiveness of evolutionary
                 algorithms often do not represent practical robotic
                 systems. In this paper, genetic programming (GP) is the
                 evolutionary strategy of interest. It is applied to
                 learn fuzzy control rules for a practical autonomous
                 vehicle steering control problem, namely, path
                 tracking. GP handles the simultaneous evolution of
                 membership functions and rule bases for the fuzzy path
                 tracker. As a matter of practicality, robustness of the
                 genetically evolved fuzzy controller is demonstrated by
                 examining the effects of sensor measurement noise and
                 an increase in the robot's nominal forward velocity.",
  notes =        "CIRA'99 http://web.nps.navy.mil/~yun/cira99/",
}

@InProceedings{hondo:1996:srrs,
  author =       "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu",
  title =        "Sharing and Refinement for Reusable Subroutines of
                 Genetic Programming",
  booktitle =    "Proceedings of the 1996 {IEEE} International
                 Conference on Evolutionary Computation",
  year =         "1996",
  volume =       "1",
  pages =        "565--570",
  address =      "Nagoya, Japan",
  month =        "20-22 " # may,
  organisation = "IEEE Neural Network Council",
  keywords =     "genetic algorithms, genetic programming, COAST",
  ISBN =         "0-7803-2902-3",
  notes =        "ICEC-96",
}

@InProceedings{hondo:1996:COASTgp96,
  author =       "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu",
  title =        "{COAST}: An Approach to Robustness and Reusability in
                 Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "429",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@InProceedings{hondo:1996:rGPrl,
  author =       "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu",
  title =        "Robost {GP} in Robot Learning",
  booktitle =    "Parallel Problem Solving from Nature IV, Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  series =       "LNCS",
  volume =       "1141",
  pages =        "751--760",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
                 COAST, Wall following problem",
}

@InProceedings{hondo:1998:mapssrc,
  author =       "Naohiro Hondo and Koji Nishikawa and Hiroshi Yokoi and
                 Yukinori Kakazu",
  title =        "Multi-Agent Programming System for Starfish Robot
                 Control",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "140--145",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{hong:1999:SAMCMO,
  author =       "Tzung-Pei Hong and Hong-Shung Wang and Wei-Chou Chen",
  title =        "Simultaneously Applying Multiple Crossover and
                 Mutation Operators",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "790",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{hong:1999:DIRUGP,
  author =       "Hong S. Hong",
  title =        "Digbital Image Restoration Using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "68--75",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{hooper:1996:iarGPes,
  author =       "Dale Hooper and Nicholas S. Flann",
  title =        "Improving the Accuracy and Robustness of Genetic
                 Programming through Expression Simplification",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "428",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96. Occam's razor, bloat, introns, editing",
}

@InProceedings{Hooper:1997:rhc,
  author =       "Dale C. Hooper and Nicholas S. Flann and Stephanie R.
                 Fuller",
  title =        "Recombinative Hill-Climbing: {A} Stronger Search
                 Method for Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "174--179",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97 Artificial Ant Santa Fe trail (only 400 time
                 steps p177), Symbolic regression pi*x**2+ex+x**0.5
                 {"}RHC is shown to run about ten times faster than
                 traditional GP for the same population size{"} p175 NB
                 only on symbolic regression. Optional program
                 simplification. 0.5% mutation. Overfitting.",
}

@InProceedings{horn:1996:nnclCS,
  author =       "Jeffrey Horn and David E. Goldberg",
  title =        "Natural Niching for Cooperative Learning in Classifier
                 Systems",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Classifier Systems, Genetic Algorithms",
  pages =        "553--564",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 Classifier paper",
}

@InProceedings{horn:1999:CCBNGA,
  author =       "Jeffrey Horn",
  title =        "Controlling the Cooperative-Competitive Boundary in
                 Niched Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "305--312",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hornby:1999:AEGSQR,
  author =       "G. S. Hornby and M. Fujita and S. Takamura and T.
                 Yamamoto and O. Hanagata",
  title =        "Autonomous Evolution of Gaits with the Sony Quadruped
                 Robot",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1297--1304",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hornby:1999:DTCPW,
  author =       "Gregory S. Hornby and Brian Mirtich",
  title =        "Diffuse versus True Coevolution in a Physics-based
                 World",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1305--1312",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{hornby:2001:taggepd,
  author =       "Gregory S. Hornby and Jordan B. Pollack",
  title =        "The Advantages of Generative Grammatical Encodings for
                 Physical Design",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "600--607",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, lindenmayer
                 system",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@Misc{horner-class,
  author =       "Helmut Horner",
  title =        "A {C}++ Class Library for Genetic Programming: The
                 Vienna University of Economics Genetic Programming
                 Kernel",
  howpublished = "citeseer",
  year =         "1996",
  month =        "29 " # may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "citeseer.nj.nec.com/horner96class.html",
  size =         "69 pages",
  abstract =     "This article gives a brief introduction in a variant
                 of genetic programming (namely simple genetic
                 algorithms over k-bounded context-free languages) and
                 presents the most important genetic operators. A C++
                 class-library for genetic programming with context-free
                 languages - the Vienna University of Economics Genetic
                 Programming Kernel - is presented within this article.
                 This program is flexible and includes the most
                 important genetic operators. It is able to interpret
                 every grammar in its Backus-NaurForm provided it is
                 available in a file. In addition, this article deals
                 with the problems of search-space-size calculations in
                 connection with depth-bounded derivation trees.",
  notes =        "Only appears to be available via citeseer (oct 2001)",
}

@InProceedings{horng:1999:A,
  author =       "Jorng-Tzong Horng and Yu-Jan Chang and Cheng-Yen Kao",
  title =        "Applying evolutionary algorithms to materialized view
                 selection in a data warehouse",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "107--115",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@InProceedings{horng:1999:R,
  author =       "Jorng-Tzong Horng and Chien-Chin Chen and Cheng-Yen
                 Kao",
  title =        "Resolution of quadratic assignment problems using an
                 evolutionary algorithm",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "116--124",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, Evolutionary Strategies",
  notes =        "GECCO-99LB",
}

@InCollection{houlette:1998:ECGPCFP,
  author =       "Ryan Houlette",
  title =        "Evolving Communication using Genetic Programming in
                 the Central-Place Foraging Problem",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "29--38",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{howard:1998:wGPpde,
  author =       "Daniel Howard",
  title =        "Why Genetic Programming for solution of partial
                 differential equations?",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{howard:1998:tdSARiGP,
  author =       "Daniel Howard and Simon C. Roberts and Richard
                 Brankin",
  title =        "Target Detection in {SAR} Imagery by Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{howard:1999:esdsSARi,
  author =       "Daniel Howard and Simon C. Roberts and Richard
                 Brankin",
  title =        "Evolution of Ship Detectors for Satellite {SAR}
                 Imagery",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "135--148",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@InProceedings{howard:1999:EuroGEN,
  author =       "Daniel Howard and Simon C. Roberts",
  title =        "Evolving object detectors for infrared imagery: a
                 comparison of texture analysis against simple
                 statistics",
  booktitle =    "Evolutionary Algorithms in Engineering and Computer
                 Science",
  year =         "1999",
  editor =       "Kaisa Miettinen and Marko M. Mäkelä and Pekka
                 Neittaanmäki and Jacques Periaux",
  pages =        "79--86",
  address =      "Jyväskylä, Finland",
  publisher_address = "Chichester, UK",
  month =        "30 " # may # " - 3 " # jun,
  publisher =    "John Wiley \& Sons",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.mit.jyu.fi/eurogen99/papers/howard.ps",
  notes =        "EUROGEN'99 Multi-stage GP terminals based on fourier
                 transforms found to be (marginally?) better than those
                 based on simple stats (mean, standard deviation).
                 Looking for parked cars from 300 feet.",
}

@InProceedings{howard:1999:ASGPSIA,
  author =       "Daniel Howard and Simon C. Roberts",
  title =        "A Staged Genetic Programming Strategy for Image
                 Analysis",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1047--1052",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{Howard:1999:AES,
  author =       "Daniel Howard and Simon C. Roberts and Richard
                 Brankin",
  title =        "Target detection in imagery by genetic programming",
  journal =      "Advances in Engineering Software",
  volume =       "30",
  pages =        "303--311",
  year =         "1999",
  number =       "5",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V1P-3W1XV4H-1/1/6e7aee809f33757d0326c62a21824411",
  abstract =     "The automatic detection of ships in low-resolution
                 synthetic aperture radar (SAR) imagery is investigated
                 in this article. The detector design objectives are to
                 maximise detection accuracy across multiple images, to
                 minimise the computational effort during image
                 processing, and to minimise the effort during the
                 design stage. The results of an extensive numerical
                 study show that a novel approach, using genetic
                 programming (GP), successfully evolves detectors which
                 satisfy the earlier objectives. Each detector
                 represents an algebraic formula and thus the principles
                 of detection can be discovered and reused. This is a
                 major advantage over artificial intelligence techniques
                 which use more complicated representations, e.g. neural
                 networks.",
}

@InProceedings{howard:2000:EMRRID,
  author =       "Daniel Howard and Simon C. Roberts",
  title =        "Evolution of Mesh Refinement Rules for Impact
                 Dynamics",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1297--1303",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic Programming, novel
                 applications i,mpact (mechanical), evolutionary
                 computation, learning (artificial intelligence),
                 mechanical engineering computing, partial differential
                 equations, mesh refinement rule evolution, impact
                 dynamics, rule learning, adaptive mesh refinement, mesh
                 cells, material densities, high speed impact, spherical
                 ball, metal plate",
  ISBN =         "0-7803-6375-2",
  URL =          "http://ieeexplore.ieee.org/iel5/6997/18853/00870801.pdf?isNumber=18853&prod=CNF&arnumber=870801&arSt=1297&ared=1303+vol.2&arAuthor=Howard%2C+D.%3B+Roberts%2C+S.C.",
  abstract =     "Genetic programming (GP) was used in an experiment to
                 investigate the possibility of learning rules that
                 trigger adaptive mesh refinement. GP detected mesh
                 cells that required refinement by evolving a formula
                 involving cell quantities such as material densities.
                 Various cell variable combinations were investigated in
                 order to identify the optimal ones for indicating mesh
                 refinement. The problem studied was the high speed
                 impact of a spherical ball on a metal plate.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@InProceedings{howard:2001:gecco,
  title =        "Genetic Programming solution of the
                 convection-diffusion equation",
  author =       "Daniel Howard and Simon C. Roberts",
  pages =        "34--41",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming,
                 convection-diffusion, differential equations, WRM, FEM,
                 numerical method",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO

                 linear one dimensional second order partial
                 differential equation, comparison of GP with known
                 analytic solution. Evolve single polynomial
                 approximation. Fitness based on analytical integration
                 of differentials of polynomial. Polynomial is phenotype
                 created by GP ADD, BACK WRITE functions on variable
                 length vector of polynomial co-efficients. read and
                 write memory (two cells).

                 Peclet numbers. p37 GP with ADFs {"}did not
                 significantly improve performance{"}. Weighted residues
                 method, WRM. p39 {"}This method cannot be
                 recommended{"}",
}

@InProceedings{Howard11:2002:EvoWorkshops,
  author =       "Daniel Howard and Simon C. Roberts",
  title =        "The Prediction of Journey Times on Motorways using
                 Genetic Programming",
  booktitle =    "Applications of Evolutionary Computing, Proceedings of
                 EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN",
  year =         "2002",
  editor =       "Stefano Cagnoni and Jens Gottlieb and Emma Hart and
                 Martin Middendorf and G{"}unther Raidl",
  volume =       "2279",
  series =       "LNCS",
  pages =        "210--211",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-4 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, applications, MIDAS, London orbital
                 motorway M25",
  ISBN =         "3-540-43432-1",
  size =         "12 pages",
  abstract =     "Considered is the problem of reliably predicting
                 motorway journey times for the purpose of providing
                 accurate information to drivers. This proof of concept
                 experiment investigates: (a) the practicalities of
                 using a Genetic Programming (GP) method to
                 model/forecast motorway journey times; and (b)
                 different ways of obtaining a journey time predictor.
                 Predictions are compared with known times and are also
                 judged against a collection of naive prediction
                 formulae. A journey time formula discovered by GP is
                 analysed to determine its structure, demonstrating that
                 GP can indeed discover compact formulae for different
                 traffic situations and associated insights. GP's
                 felxibility allows it to self-determine the required
                 level of modelling complexity.",
  notes =        "EvoWorkshops2002, part of cagnoni:2002:ews

                 Counter clockwise (ie south bound) between junction 15
                 (M4) and Junction 11 (Chertsey) Sepetember 1999.
                 (Covered by variable speed limits)",
}

@InProceedings{Howard13:2002:EvoWorkshops,
  author =       "Daniel Howard and Simon C. Roberts and Conor Ryan",
  title =        "The Boru Data Crawler for Object Detection Tasks in
                 Machine Vision",
  booktitle =    "Applications of Evolutionary Computing, Proceedings of
                 EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN",
  year =         "2002",
  editor =       "Stefano Cagnoni and Jens Gottlieb and Emma Hart and
                 Martin Middendorf and G{"}unther Raidl",
  volume =       "2279",
  series =       "LNCS",
  pages =        "222--232",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-4 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, applications",
  ISBN =         "3-540-43432-1",
  size =         "11 pages",
  abstract =     "A 'data crawler' is allowed to meander around an image
                 deciding what it considers to be interesting and laying
                 down flags in areas where its interest has been
                 aroused. These flags can be analysed statistically as
                 if the image was being viewed from afar to achieve
                 object recognition. The guidance program for the
                 crawler, the program which excites it to deposit a flag
                 and how the flags are combined statistically, are
                 driven by an evolutionary process which has as
                 objective the minimisation of misses and false alarms.
                 The crawler is represented by a tree-based Genetic
                 Programming (GP) method with fixed architecture
                 Automatically Defined Functions (ADFs). The crawler was
                 used as a post-processor to the object detection
                 obtained by a Staged GP method, and it managed to
                 appreciably reduce the number of false alarms on a
                 real-world application of vehicle detection in infrared
                 imagery.",
  notes =        "EvoWorkshops2002, part of cagnoni:2002:ews

                 READMEM WRITEMEM working memory. Mark decisions branch.
                 Flags. Second results branch. Looking for cars

                 ",
}

@InProceedings{howard2:2002:gecco,
  author =       "Daniel Howard and Simon C. Roberts",
  title =        "Application Of Genetic Programming To Motorway Traffic
                 Modelling",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1097--1104",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, forecasting, incident detection, motorway
                 traffic modelling, time series prediction",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{howard:2002:gecco,
  author =       "Daniel Howard and Simon C. Roberts and Conor Ryan",
  title =        "Machine Vision: Exploring Context With Genetic
                 Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "756--763",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, automatically
                 defined functions, data crawler, image analysis,
                 machine vision, target detection",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@Article{howard:1995:GA-P,
  author =       "Les M. Howard and Donna J. D'Angelo",
  title =        "The {GA--P}: {A} Genetic Algorithm and Genetic
                 Programming hybrid",
  journal =      "IEEE Expert",
  year =         "1995",
  volume =       "10",
  number =       "3",
  pages =        "11--15",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  size =         "5 pages",
  notes =        "University of Georgia. IEEE Expert Special Track on
                 Evolutionary Programming (P. J. Angeline editor)
                 angeline:1995:er",
}

@InProceedings{howley:1996:GPsam,
  author =       "Brian Howley",
  title =        "Genetic Programming of Near-Minimum-Time Spacecraft
                 Attitude Maneuvers",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "98--106",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96 see also howley:1996:samAIAA",
}

@InProceedings{howley:1996:samAIAA,
  author =       "Brian Howley",
  title =        "Genetic Programming of Spacecraft Attitude Maneuvers
                 Under Reaction Wheel Control",
  booktitle =    "AIAA Guidance Navigation and Control Conference",
  year =         "1996",
  month =        "29--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  address =      "San Diego, CA, USA",
  publisher_address = "1801 Alexander Bell Crive, Suite 500, Reston, VA
                 22091, USA",
  organisation = "American Institute of Aeronautics and Astronautics",
  size =         "11 pages",
  abstract =     "A general solution for maneuvers with non-zero initial
                 and final rates was not found, however, the GP solution
                 out performs a hand crafted solution to the problem",
  notes =        "AIAA 96-3849 see also howley:1996:GPsam",
}

@InProceedings{Howley:1997:GPps,
  author =       "Brian Howley",
  title =        "Genetic Programming and Parametric Sensitivity: a Case
                 Study In Dynamic Control of a Two Link Manipulator",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "180--185",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InCollection{howley:1995:GPNMTSAM,
  author =       "Brian Howley",
  title =        "Genetic Programming of Near Minimum Time Spacecraft
                 Attitude Maneuvers",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "96--106",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{hsu:1999:GAASLDM,
  author =       "William H. Hsu and William M. Pottenger and Michael
                 Welge and Jie Wu and Ting-Hao Yang",
  title =        "Genetic Algorithms for Attribute Synthesis in
                 Large-Scale Data Mining",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1783",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 See also freitas:1999:AAGR Freitas {"}Data Mining with
                 Evolutionary Algorithms{"} AAAI tech report WS-99-06",
}

@InProceedings{hsu:2001:waptmaoGP,
  author =       "William H. Hsu and Steven M. Gustafson",
  title =        "Wrappers for Automatic Parameter Tuning in Multi-Agent
                 Optimization by Genetic Programming",
  booktitle =    "IJCAI-2001 Workshop on Wrappers for Performance
                 Enhancement in Knowledge Discovery in Databases (KDD)",
  year =         "2001",
  address =      "Seattle, Washington, USA",
  month =        "4 " # aug,
  keywords =     "genetic algorithms, genetic programming, robotic
                 soccer",
  abstract =     "We present an adaptation of the standard genetic
                 program (GP) to hierarchically decomposable,
                 multi-agent learning problems. To break down a problem
                 that requires cooperation of multiple agents, we use
                 the team objective function to derive a simpler,
                 intermediate objective function for pairs of
                 cooperating agents. We apply GP to optimize first for
                 the intermediate, then for the team objective function,
                 using the final population from the earlier GP as the
                 initial seed population for the next. This layered
                 learning approach facilitates the discovery of
                 primitive behaviors that can be reused and adapted
                 towards complex objectives based on a shared team goal.
                 We use this method to evolve agents to play a
                 subproblem of robotic soccer (keep-away soccer).
                 Finally, we show how layered learning GP evolves better
                 agents than standard GP, including GP with
                 automatically defined functions, and how the problem
                 decomposition results in a significant learning-speed
                 increase.",
  notes =        "http://www.kddresearch.org/KDD/Workshops/IJCAI-2001/
                 Paper from author 19 Jul 2001. Also available as
                 GECCO'2001 late breaking paper Coaching, seeding, LLGP,
                 keep-away soccer (minimize number of turnovers), MAS,
                 RoboCup, passing agents and keep-away soccer agents,
                 ADF. Simple GP and ADFGP trained with one shot fitness
                 function, ie not layered. Popsize 2000 {"}yeilded good
                 results{"}. Luke's ECJ. SoccerServer, TeamBots.

                 See also gustafson:mastersthesis",
}

@InProceedings{hsu:2001:gpllmt,
  author =       "William H. Hsu and Steven M. Gustafson",
  title =        "Genetic Programming for Layered Learning of
                 Multi-Agent Tasks",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "176--182",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, soccer,
                 RoboCup",
  notes =        "GECCO-2001LB. Luke's ECJ, teambots. See also
                 gustafson:mastersthesis",
}

@InProceedings{hsu3:2002:gecco,
  author =       "William H. Hsu and Steven M. Gustafson",
  title =        "Genetic Programming And Multi-agent Layered Learning
                 By Reinforcements",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "764--771",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)

                 Nominated for best at GECCO award",
}

@InProceedings{hu2:2002:gecco,
  author =       "Jianjun Hu and Kisung Seo and Shaobo Li and Zhun Fan
                 and Ronald C. Rosenberg and Erik D. Goodman",
  title =        "Structure Fitness Sharing ({SFS}) For Evolutionary
                 Design By Genetic Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "780--787",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 design, fitness sharing, mechatronic system, premature
                 convergence, topology and parameter search",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{hu:2002:gecco,
  author =       "Jianjun Hu and Erik D. Goodman and Kisung Seo and Min
                 Pei",
  title =        "Adaptive Hierarchical Fair Competition ({AHFC}) Model
                 For Parallel Evolutionary Algorithms",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "772--779",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, adaptive
                 evolutionary algorithm, fair competition principle,
                 hierarchical topology, parallel evolutionary
                 algorithms, premature convergence",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{hu:1998:GPci,
  author =       "Yuh-Jyh Hu",
  title =        "A Genetic Programming Approach to Constructive
                 Induction",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "146--151",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{hu:1998:bdGP,
  author =       "Yuh-Jyh Hu",
  title =        "Biopattern Discovery by Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "152--157",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{Hu:2000:GECCO,
  author =       "Yuh-Jyh Hu",
  title =        "Global Gene Expression Analysis with Genetic
                 Programming",
  pages =        "753",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{hu:2002:thfcmfpea,
  author =       "Jianjun Hu and Erik D. Goodman",
  title =        "The Hierarchical Fair Competition ({HFC}) Model for
                 Parallel Evolutionary Algorithms",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "49--54",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The HFC model for evolutionary computation is inspired
                 by the stratified competition often seen in society and
                 biology. Subpopulations are stratified by fitness.
                 Individuals move from low-fitness subpopulations to
                 higher-fitness subpopulations if and only if they
                 exceed the fitness-based admission threshold of the
                 receiving subpopulation, but not of a higher one. HFC's
                 balanced exploration and exploitation, while avoiding
                 premature convergence, is shown on a genetic
                 programming example.",
}

@InProceedings{huang:1999:AESSSSP,
  author =       "Hsien-Da Huang and Jih Tsung Yang and Shu Fong Shen
                 and Jorng-Tzong Horng",
  title =        "An Evolution Strategy to Solve Sports Scheduling
                 Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "943",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming,
                 poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Huelsbergen:1996:tsemli,
  author =       "Lorenz Huelsbergen",
  title =        "Toward Simulated Evolution of Machine Language
                 Iteration",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "315--320",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp96.ps",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{Huelsbergen:1997:lrsemlp,
  author =       "Lorenz Huelsbergen",
  title =        "Learning Recursive Sequences via Evolution of
                 Machine-Language Programs",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "186--194",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp97.ps",
  notes =        "GP-97. Comparison with random search",
}

@InProceedings{huelsbergen:1998:fgsppemlr,
  author =       "Lorenz Huelsbergen",
  title =        "Finding General Solutions to the Parity Problem by
                 Evolving Machine-Language Representations",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "158--166",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  email =        "lorenz@research.bell-labs.com",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp98.ps",
  notes =        "GP-98",
}

@InProceedings{Hulse:1997:dgpj,
  author =       "Richard Gerber Paul Hulse and Jenanne Price",
  title =        "Distributed Genetic Programming In {Java}",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "81--86",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670

                 TARA",
}

@InProceedings{hunter:2002:ECAI,
  author =       "Andrew Hunter",
  title =        "Using multiobjective genetic programming to infer
                 logistic polynomial regression models",
  booktitle =    "15th European Conference on Artificial Intelligence",
  year =         "2002",
  editor =       "Frank {Van Harmelen}",
  address =      "Lyon, France",
  month =        "21-26 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ECAI 2002
                 http://ecai2002.univ-lyon1.fr/show_en.pl?page=en/program/ecai.html",
}

@InProceedings{Hussain:2000:GECCO,
  author =       "Daniar Hussain and Steven Malliaris",
  title =        "Evolutionary Techniques Applied to Hashing: An
                 efficient data retrieval method",
  pages =        "760",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming, hashing",
  ISBN =         "1-55860-708-0",
  URL =          "http://www.insanemath.com/hash/",
  size =         "1 page",
  notes =        "Poster. A joint meeting of the ninth International
                 Conference on Genetic Algorithms (ICGA-2000) and the
                 fifth Annual Genetic Programming Conference (GP-2000)
                 Part of whitley:2000:GECCO",
}

@InProceedings{hussain:1998:bpage,
  author =       "Talib S. Hussain and Roger A. Browse",
  title =        "Basic Properties of Attribute Grammar Encoding",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ai.mit.edu/people/unamay/phd-ws-abstracts/gp-workshop-hussain.ps",
  notes =        "GP-98LB, GP-98PhD Student Workshop",
}

@InProceedings{hussian:2000:mwmugp,
  author =       "Abo El-Abbass Hussian and Alaa Sheta and Mahmoud Kamel
                 and Mohamed Telbaney and Ashraf Abdelwahab",
  title =        "Modeling of a Winding Machine Using Genetic
                 Programming",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "398--402",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, control
                 system design",
  ISBN =         "0-7803-6375-2",
  abstract =     "In this paper, we present a new method for modeling
                 the dynamics of a winding process using genetic
                 programming and compare it with traditional modeling
                 approaches. Data sets collected from an actual
                 industrial process was used throughout the experiments.
                 Three models were developed to describe the dynamics of
                 the winding process. Experimental results are presented
                 and discussed.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@InProceedings{Iba:1993:elpbsc,
  author =       "H. Iba and H. {de Garis} and T. Higuchi",
  title =        "Evolutionary learning of predatory behaviors based on
                 structured classifiers",
  booktitle =    "From Animals to Animats 2: Proceedings of the Second
                 International Conference on Simulation of Adaptive
                 Behavior",
  year =         "1993",
  editor =       "J. A. Meyer and H. L. Roitblat and S. W. Wilson",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
}

@TechReport{Iba:1993:sipsGA,
  author =       "H. Iba and H. {de Garis} and T. Sato",
  title =        "Solving identification problems by structured genetic
                 algorithms",
  institution =  "Electrotechnical Laboratory",
  year =         "1993",
  type =         "Technical report",
  number =       "ETL-TR-93-17",
  address =      "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{icga93:iba,
  author =       "Hitoshi Iba and Takio Karita and Hugo {de Garis} and
                 Taisuke Sato",
  title =        "System Identification Using Structured Genetic
                 Algorithms",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "279--286",
  size =         "8 pages",
  notes =        "Hierarchical tree GA, used for learning sequence of
                 multiple variables and then predicting, STOGANOFF",
}

@InCollection{kinnear:iba,
  title =        "Genetic Programming Using a Minimum Description Length
                 Principle",
  author =       "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "265--284",
  chapter =      "12",
  size =         "15 pages",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Describes MDL; Work on both decision trees and GMDH
                 symbolic regression trees (STROGANOFF). Nature of trees
                 (ie never worse than component trees) more important
                 than MDL?

                 ",
}

@InProceedings{Iba:1992:mlslsGA,
  author =       "H. Iba and T. Sato",
  title =        "Meta-level strategy learning for {GA} based on
                 structured representation",
  booktitle =    "Proceedings of the Second Pacific Rim International
                 Conference on Artificial Intelligence",
  year =         "1992",
  organisation = "Center for Artificial Intelligence Research, Kaist",
  keywords =     "genetic algorithms, genetic programming",
}

@TechReport{Iba:1992:eSsp,
  author =       "H. Iba and T. Sato",
  title =        "Extension of {STROGANOFF} for symbolic problems",
  institution =  "Electrotechnical Laboratory",
  year =         "1992",
  type =         "Technical report",
  number =       "ETL-TR-94-1",
  address =      "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Iba:1994:siGP,
  author =       "H. Iba and T. Sato and H. {de Garis}",
  title =        "System identification approach to genetic
                 programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "401--406",
  volume =       "1",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
}

@TechReport{Iba:1994:GPlHC,
  author =       "Hitoshi Iba and Taisuke Sato",
  title =        "Genetic Programming with Local Hill-Climbing",
  institution =  "Electrotechnical Laboratory",
  year =         "1994",
  number =       "ETL-TR-94-4",
  address =      "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Also published in PPSN-94, see iba:1994:GPlHCppsn3

                 ",
  size =         "16 pages",
}

@InProceedings{iba:1994:GPlHCppsn3,
  author =       "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
  title =        "Genetic Programming with Local Hill-Climbing",
  booktitle =    "Parallel Problem Solving from Nature III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and Reinhard
                 M{\"a}nner",
  series =       "LNCS",
  volume =       "866",
  pages =        "334--343",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58484-6",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
  abstract =     "{"}We demonstrate the superior effectiveness of
                 GP+local Hill Climbing with experiments in Boolean
                 concept formation and symbolic regression{"}. Boolean
                 GP combines GP with Adaptive Logic Network trees.
                 Combination can evove to cope with time varying fitness
                 functions. Numerical GP combines GP with GMDH (Group
                 Method of Data Handling, Ivakhnenko)",
  notes =        "PPSN3 see also technical note Iba:1994:GPlHC",
}

@Book{iba:1994:GA,
  author =       "Hitoshi Iba",
  title =        "Introduction to Genetic Algorithms",
  publisher =    "Ohm-sha",
  year =         "1994",
  keywords =     "genetic algorithms",
  notes =        "in Japanese",
  size =         "pages",
}

@InProceedings{iba:1995:nGPsi,
  author =       "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}",
  title =        "Numerical Genetic Programming for System
                 Identification",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "64--75",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  size =         "12 pages",
  notes =        "This paper based on earlier results (icga93:iba
                 Iba:1994:siGP and ETL-TR-94-20 1994 (submitted to
                 ICEC-95, see iba:1885:rgn)). part of rosca:1995:ml",
}

@InProceedings{Iba:1995:tdpGP,
  author =       "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
  title =        "Temporal Data Processing Using Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "279--286",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-370-0",
  abstract =     "This paper reports an extension of STROGANOFF called
                 R-STROGANOFF which uses special memory terminal nodes
                 to provide a form of recurrancy to process time ordered
                 events.

                 All functions are polynomials (quadratics in the
                 examples), terminals are either inputs or memories.
                 Each memory terminals hold the value of a function node
                 on the previous time step.

                 The coeffients of the polynomials are learnt by trying
                 to match the training data using a {"}Generalised Error
                 Proporgation Algorithm{"}. This is determinstic. Seems
                 like STROGANOFF's (but different?), time sequence
                 based, based on back-propagation. The coefficients are
                 recalculated each generation (assuming tree has
                 changed).

                 Fitness function used {"}minimum description length{"}
                 (MDL).

                 Quadratic coefficients mya be limited to 0<=x<=1 to
                 avoid divergence.

                 Examples: 2 step 0-1 oscilator, 4 Tomita languages (on
                 binary alphabet).

                 Tree could be converted to finite state automata, which
                 was more general than tree, ie works in all cases
                 including those not in the training set.

                 On the tomita languages problems {"}R-STROGANOFF works
                 almost as well as (the best) best recurrent
                 networks{"}",
}

@InProceedings{iba:1885:rgn,
  author =       "Hitoshi Iba and Hugo {de Garis} and Taisuka Sato",
  title =        "Recombination Guidance for Numerical Genetic
                 Programming",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "1",
  pages =        "97",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In our earlier papers, we introduced our adaptive
                 program called {"}STROGANOFF{"} (i.e. STructured
                 Representation On Genetic Algorithms for Non-linear
                 Function Fitting), which integrated a multiple
                 regression analysis method and a GA-based search
                 strategy. The effectiveness of STROGANOFF was
                 demonstrated by solving several system identification
                 problems. This paper proposes an {"}adaptive
                 recombination{"} mechanism for STROGANOFF. Our
                 intention is to exploit already built structures by
                 {"}adaptive recombination{"}, in which GP recombination
                 is guided by a certain measure. The effectiveness of
                 our approach is shown by the experiment in predicting a
                 chaotic time series. Thereafter we describe real-world
                 applications of STROGANOFF to computer vision.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html

                 ",
}

@InCollection{iba:1996:aigp2,
  author =       "Hitoshi Iba and Hugo {de Garis}",
  title =        "Extending Genetic Programming with Recombinative
                 Guidance",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "69--88",
  chapter =      "4",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "This chapter introduces a recombinative guidance
                 mechanism for GP (Genetic Programming), and shows the
                 effectiveness of our approach using various
                 experiments. Traditional GP blindly combines subtrees,
                 by applying crossover operations. This blind
                 replacement, in general, can often disrupt beneficial
                 building-blocks in tree structures. Randomly chosen
                 crossover points ignore the semantics of the parent
                 trees. Our goal is to exploit already built structures
                 by adaptive recombination, in which GP recombination is
                 guided by ``S-value'' measures. We present various
                 S-value definitions, and show that the performance
                 depends upon the definition.",
}

@InProceedings{iba:1996:ecma,
  author =       "Hitoshi Iba",
  title =        "Emergent Cooperation for Multiple Agents using Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "66--74",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670 see also
                 iba:1996:ecmaPPSN",
}

@TechReport{iba:1995:rtgTR,
  author =       "Hitoshi Iba",
  title =        "Random Tree Generation for Genetic Programming",
  institution =  "ElectroTechnical Laboratory (ETL)",
  year =         "1995",
  number =       "ETL-TR-95-35",
  address =      "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
  month =        "14 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
  size =         "24 pages",
}

@InProceedings{iba:1996:rtg,
  author =       "Hitoshi Iba",
  title =        "Random Tree Generation for Genetic Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "75--82",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{iba:1996:rtgGP,
  author =       "Hitoshi Iba",
  title =        "Random Tree Generation for Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature IV, Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  series =       "LNCS",
  volume =       "1141",
  pages =        "144--153",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html
                 PPSN4

                 bijection, tree_by_dyck

                 Demonstrated on Mackey-Glass compared to {"}grow{"}
                 method (not ramped half-and-half)",
}

@InProceedings{iba:1996:ecmaPPSN,
  author =       "Hitoshi Iba",
  title =        "Emergent Cooperation for Multiple Agents Using Genetic
                 Programming",
  booktitle =    "Parallel Problem Solving from Nature IV, Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  series =       "LNCS",
  volume =       "1141",
  pages =        "32--41",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
                 Comparison of homogeneous, hetrogeneous and
                 co-evolutionary breeding on {"}Tile world{"} simulated
                 environment problem.",
}

@Book{iba:1996:GP,
  author =       "Hitoshi Iba",
  title =        "Genetic Programming",
  publisher =    "Tokyo Denki University Press",
  year =         "1996",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "in Japanese",
  size =         "pages",
}

@InProceedings{iba:1997:eca,
  author =       "Hitoshi Iba and Tishihide Nozoe and Kanji Ueda",
  title =        "Evolving Communicating Agents based on Genetic
                 Programming",
  booktitle =    "Proceedings of the 1997 {IEEE} International
                 Conference on Evolutionary Computation",
  year =         "1997",
  address =      "Indianapolis",
  publisher_address = "Piscataway, NJ, USA",
  month =        "13-16 " # apr,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ICEC-97",
}

@InProceedings{iba:1997:malrntGP,
  author =       "Hitoshi Iba",
  title =        "Multiple-Agent Learning for a Robot Navigation Task by
                 Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "195--200",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@Unpublished{iba:1997:cfevlr,
  author =       "Hitoshi Iba",
  title =        "Complexity-based Fitness Evaluation for Variable
                 Length Representation",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 variable size representation",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "3 pages",
}

@InProceedings{iba:1998:marlGP,
  author =       "Hitoshi Iba",
  title =        "Multi-Agent Reinforcement Learning with Genetic
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "167--172",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{Iba:1998:ISJ,
  author =       "Hitoshi Iba",
  title =        "Evolutionary learning of communicating agents",
  journal =      "Information Sciences",
  year =         "1998",
  volume =       "108",
  number =       "1-4",
  pages =        "181--205",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Multi-agent
                 system, Distributed artificial intelligence",
  ISSN =         "0020-0255",
  abstract =     "This paper presents the emergence of the cooperative
                 behavior for communicating agents by means of Genetic
                 Programming (GP). Our experimental domains are the
                 pursuit game and the robot navigation task. We conduct
                 experiments with the evolution of the communicating
                 agents and show the effectiveness of the emergent
                 communication in terms of the robustness of generated
                 GP programs. The performance of GP-based multi-agent
                 learning is discussed with comparative experiments by
                 using different breeding strategies, i.e., homogenous
                 breeding and heterogeneous breeding.",
  notes =        "Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}

@InCollection{iba:1999:aigp3,
  author =       "Hitoshi Iba",
  title =        "Evolving Multiple Agents by Genetic Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "19",
  pages =        "447--466",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@Book{iba:1999:EC,
  author =       "Hitoshi Iba",
  title =        "Evolutionary Computing",
  publisher =    "Tokyo University Press",
  year =         "1999",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "in Japanese",
  size =         "pages",
}

@InProceedings{iba:1999:BBBGP,
  author =       "Hitoshi Iba",
  title =        "Bagging, Boosting, and Bloating in Genetic
                 Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1053--1060",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming, classifier ensembles",
  ISBN =         "1-55860-611-4",
  abstract =     "subpopulations",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)

                 10 Subpopulations each has its own training data
                 (produced using the boosting or bagging methods. Best
                 of each subpopulation has vote in final result. Do we
                 actually need subpopulations, could not the whole
                 algorithm be split into T entirely separate GP runs?
                 SGPC1.1

                 p1054 {"}controlling the bloating effect is closely
                 related to the performance improvement...{"}

                 noisy cos(2x)=1-sin(x)**2, Mackey-Glass chaotic time
                 series, 6MUX, symbolic regression, nikkei225
                 Description of boosting weight adjustment algorithm
                 (p1054) seems to be wrong?

                 p1056 BagGP, BoostGP > GP, BagGP=BoostGP But only in
                 the case of noisy cos(2x) does difference (table 2)
                 seem big. Mention of DSS and PADO.

                 p1059 Says Bagging and Boosting yield lower bloat.
                 (does not explain why) Little supporting data (Fig 5).
                 Boosting v co-evolution",
}

@InProceedings{iba:1999:UGPPFD,
  author =       "Hitoshi Iba and Takashi Sasaki",
  title =        "Using Genetic Programming to Predict Financial Data",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "244--251",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{iba:2000:CEF,
  author =       "Hitoshi Iba and Nikolay Nikolaev",
  title =        "Financial data prediction by means of genetic
                 programming",
  booktitle =    "Computing in Economics and Finance",
  year =         "2000",
  address =      "Universitat Pompeu Fabra, Barcelona, Spain",
  month =        "6-8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://enginy.upf.es/SCE/papers/paper330.ps.gz",
  notes =        "http://enginy.upf.es/SCE/index2.html",
}

@InProceedings{Iba:2000:GECCO,
  author =       "Hitoshi Iba and Makoto Terao",
  title =        "Controlling Effective Introns for Multi-Agent Learning
                 by Genetic Programming",
  pages =        "419--426",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{iba:2000:gppmfds,
  author =       "Hitoshi Iba and Nikolay Nikolaev",
  title =        "Genetic Programming Polynomial Models of Financial
                 Data Series",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1459--1466",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series,
                 stroganoff",
  ISBN =         "0-7803-6375-2",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@InProceedings{iba:2002:gecco,
  author =       "Hitoshi Iba and Erina Sakamoto",
  title =        "Inference Of Differential Equation Moels By Genetic
                 Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "788--795",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming,
                 bioinformatics, differential equation, E-cell, genome
                 informatics, Lotka-Volterra model, S-systems",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{ibarra:2002:EuroGP,
  title =        "Transformation of Equational Specification by Means of
                 Genetic Programming",
  author =       "Aitor Ibarra and J. Lanchares and J. Mendias and J. I.
                 Hidalgo and R. Hermida",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "248--257",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "High Level Synthesis (HLS) is a designing methodology
                 aimed to the synthesis of hardware devices from
                 behavioural specifications. One of the techniques used
                 in HLS is formal verification. In this work we present
                 an evolutionary algorithm in order to optimize circuit
                 equational specifications by means of a special type of
                 genetic operator. We have named this operator algebraic
                 mutation, carried out with the help of the equations
                 that Formal Verification Synthesis offers. This work
                 can be classified within the development of an
                 automatic tool of Formal Verification Synthesis by
                 using genetic techniques. We have applied this
                 technique to a simple circuit equational specification
                 and to a much more complex algebraic equation. In the
                 first case our algorithm simplifies the equation until
                 the best specification is found and in the second a
                 solution improving the former is always obtained.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{ichise:1998:ilpGP,
  author =       "R. Ichise",
  title =        "Inductive Logic Programming and Genetic Programming",
  booktitle =    "European Conference on Artificial Intelligence",
  year =         "1998",
  editor =       "Henri Prade",
  address =      "Brighton",
  month =        "23-28 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ECAI-98 young researcher paper

                 ",
}

@InProceedings{igel:98,
  author =       "Christian Igel",
  title =        "Causality of Hierarchical Variable Length
                 Representations",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "324--329",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@InCollection{igel:1999:aigp3,
  author =       "Christian Igel and Kumar Chellapilla",
  title =        "Fitness Distributions: Tools for Designing Efficient
                 Evolutionary Computations",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "9",
  pages =        "191--216",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{igel:1999:IIDDGCFGP,
  author =       "Christian Igel and Kumar Chellapilla",
  title =        "Investigating the Influence of Depth and Degree of
                 Genotypic Change on Fitness in Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1061--1068",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  abstract =     "In this paper we investigate the influence of (a) the
                 amount of variation generated in the genotype and (b)
                 the depth of application of variation operators on the
                 offspring fitness in genetic programming. Simulation
                 results on three common test problems indicate that for
                 certain features of the fitness distribution the
                 location of the variation may play as important a role
                 as the choice of the applied operators.",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 Errata: We thought of binary trees when the second
                 paragraph on the second page (i.e. 1062) was
                 written...",
}

@InProceedings{iima:1999:GALSPEWPP,
  author =       "Hitoshi Iima and Nobuo Sannomiya",
  title =        "Genetic Algorithm for a Large-Scale Scheduling Problem
                 in an Electric Wire Production Process",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1784",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{PDPTA96b,
  author =       "I. M. Ikram",
  title =        "An occam Library for Genetic Programming on Transputer
                 Networks",
  booktitle =    "Proceedings of the International Conference on
                 Parallel and Distributed Processing Techniques and
                 Applications",
  year =         "1996",
  editor =       "Hamid R. Arabnia",
  pages =        "1186--1189",
  address =      "Sunnyvale, California",
  month =        "9-11 " # aug,
  publisher =    "CSREA",
  keywords =     "genetic algorithms, genetic programming, occam,
                 Transputers",
  abstract =     "This paper describes the contents of a library of
                 occam procedures used to implement parallel versions of
                 the Genetic Programming (GP) machine learning paradigm.
                 GP attempts to evolve solutions to machine learning
                 problems, in the form of trees encoding programs or
                 expressions. As occam lacks recursion and both higher
                 order functions and function pointers, the
                 implementation of a generic tree evaluation procedure
                 for trees containing arbitrary functions is not
                 trivial. We present a concurrent algorithm used to
                 alleviate this problem.",
  notes =        "Ismail Ikram http://cs.ru.ac.za/homes/g93i0527/",
}

@InProceedings{ilakovac:1996:GANNrsvp,
  author =       "Tin Ilakovac and Zeljka Perkovic and Strahil Ristov",
  title =        "The Use of Genetic Algorithms in the Optimization of
                 Competitive Neural Networks which Resolve the Stuck
                 Vectors Problem",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "499",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96 GA paper",
}

@PhdThesis{ilich:2000:thesis,
  author =       "Nesa Ilich",
  title =        "A Strongly Feasible Evolution Program for non-linear
                 optimization of Network Flows",
  school =       "Department of Civil and Geological Sciences,
                 University of Manitoba",
  year =         "2000",
  address =      "Winnipeg, Canada",
  month =        oct,
  email =        "NIlich@mail.com",
  keywords =     "genetic algorithms, genetic programming, Evolution
                 Programs, Network Flows, Non-Linear Constraints",
  size =         "pages",
  abstract =     "This thesis describes the main features of a Strongly
                 Feasible Evolution Program (SFEP) for solving network
                 flow programs that can be non-linear both in the
                 constraints and in the objective function. The approach
                 is a hybrid of a network flow algorithm and an
                 evolution program. Network flow theory is used to help
                 conduct the search exclusively within the feasible
                 region, while progress towards optimal points in the
                 search space is achieved using evolution programming
                 mechanisms such as recombination and mutation. The
                 solution procedure is based on a recombination operator
                 in which all parents in a small mating pool have equal
                 chance of contributing their genetic material to an
                 offspring. When an offspring is created with better
                 fitness value than that of the worst parent, the worst
                 parent is discarded from the mating pool while the
                 offspring is placed in it. The main contributions are
                 in the massive parallel initialization procedure which
                 creates only feasible solutions with simple heuristic
                 rules that increase chances of creating solutions with
                 good fitness values for the initial mating pool, and
                 the gene therapy procedure which fixes {"}defective
                 genes{"} ensuring that the offspring resulting from
                 recombination is always feasible. Both procedures
                 utilize the properties of network flows. Tests were
                 conducted on a number of previously published
                 transportation problems with 49 and 100 decision
                 variables, and on two problems involving water
                 resources networks with complex non-linear constraints
                 with up to 1500 variables. Convergence to equal or
                 better solutions was achieved with often less than one
                 tenth of the previous computational efforts.",
  notes =        "

                 ",
}

@InProceedings{imamura:2001:gecco,
  title =        "Fault-Tolerant Computing with {N}-Version Genetic
                 Programming",
  author =       "Kosuke Imamura and James A. Foster",
  pages =        "178",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 Fault-Tolerant N-Version Genetic Programming",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{imamura:2002:EuroGP,
  title =        "{$N$}-version Genetic Programming via Fault Masking",
  author =       "Kosuke Imamura and Robert B. Heckendorn and Terence
                 Soule and James A. Foster",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "172--181",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "We introduce a new method, N-Version Genetic
                 Programming (NVGP), for building fault tolerant
                 software by building an ensemble of automatically
                 generated modules in such a way as to maximize their
                 collective fault masking ability. The ensemble itself
                 is an example of n-version modular redundancy for fault
                 tolerance, where the output of the ensemble is the most
                 frequent output of n independent modules. By maximising
                 collective fault masking, NVGP approaches the fault
                 tolerance expected from n version modular redundancy
                 with independent faults in component modules. The
                 ensemble comprises individual modules from a large pool
                 generated with genetic programming, using operators
                 that increase the diversity of the population. Our
                 experimental test problem classified promoter regions
                 in Escherichia coli DNA sequences. For this problem,
                 NVGP reduced the number and variance of errors over
                 single modules produced by GP, with statistical
                 significance.",
  notes =        "EuroGP'2002, part of lutton:2002:GP, UCI ML e.coli
                 benchmark (balanced training 35 positives, 35
                 negatives). beowulf. 2-gram (16 possible). linear gp
                 (MIPS like). max length 80. 4 read/write registers
                 (memory). 5 crossover types. Inversion (!). 2 mutation
                 operators, tournament fitness=correlation coefficient.
                 40 isolated islands (demes) each 100 individuals.
                 ensemble = composition from (randomly chosen)
                 island.

                 ensemble is qualified if number of its errors <= number
                 of errors expected if its components were _independent_
                 14% to 58% improvement in error rate for ensemble (of
                 30) compared to single GP (pop 100).

                 ",
}

@InProceedings{imamura:2002:gecco,
  author =       "Kosuke Imamura and Robert B. Heckendorn and Terence
                 Soule and James A. Foster",
  title =        "Abstention Reduces Errors--decision Abstaining
                 {N}-version Genetic Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "796--803",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{imamura:2002:gecco:workshop,
  title =        "Abstention Reduces Errors - Decision Abstaining
                 {N-version} Genetic Programming",
  author =       "Kosuke Imamura",
  pages =        "284--287",
  booktitle =    "Graduate Student Workshop",
  editor =       "Sean Luke and Conor Ryan and Una-May O'Reilly",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{Irani:1997:gaoijt,
  author =       "Zahir Irani and Amir Shari",
  title =        "Genetic Algorithm Optimization of Investment
                 Justification Theory",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "87--92",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{irani:1998:rpeIea,
  author =       "Zahir Irani and Amir M. Sharif",
  title =        "A Revised Perspective on the Evaluation of {IT}/{IS}
                 Investments using an Evolutionary Approach",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{Isaka:1997:esfife,
  author =       "Satoru Isaka",
  title =        "An Empirical Study of Facial Image Feature Extraction
                 by Genetic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "93--99",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{ishida:2002:gmsgpidm,
  author =       "Celso Yoshikazu Ishida and Aurora Trinidad Ramirez
                 Pozo",
  title =        "{GPSQL} Miner: {SQL}-Grammar Genetic Programming in
                 Data Mining",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "1226--1231",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, SQL",
  abstract =     "The present work describes GPSQL Miner, a Genetic
                 Programming system for mining relational databases.
                 This system uses Grammar Genetic Programming for
                 classification task and one of its main features is the
                 representation of the classifiers. The system uses SQL
                 grammar, which facilitates the evaluation process, once
                 the data are in relational databases. The tool was
                 tested with some databases and the results were
                 compared with other algorithms. These first experiments
                 had shown promising results for the classification
                 task.",
}

@InProceedings{ito:1995:pd,
  author =       "Akira Ito and Hiroyuki Yano",
  title =        "The Emergence of Cooperation in a Society of
                 Autonomous Agents -- The Prisoner's Dilemma Game Under
                 the Disclosure of Contract Histories --",
  booktitle =    "ICMAS-95 Proceedings First International Conference on
                 Multi-Agent Systems",
  year =         "1995",
  editor =       "Victor Lesser",
  pages =        "201--208",
  address =      "San Francisco, California, USA",
  month =        "12--14 " # jun,
  publisher =    "AAAI Press/MIT Press",
  keywords =     "multi-agent",
  ISBN =         "0-262-62102-9",
  notes =        "Society of agents play PD against each other according
                 to inherited strategy. Strategies are specified by
                 (possibly recursive) programs written in a small
                 language. Programs are mutated but no crossover.",
}

@InProceedings{ito:1996:rrpgGP,
  author =       "Takuya Ito and Hitoshi Iba and Masayuki Kimura",
  title =        "Robustness of Robot Programs Generated by Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  note =         "321--326",
  size =         "6 pages",
  notes =        "GP-96. Moderatly complex robot simulation",
}

@InProceedings{ito:1998:nddx,
  author =       "Takuya Ito and Hitoshi Iba and Satoshi Sato",
  title =        "Non-Destructive Depth-Dependent Crossover for Genetic
                 Programming",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "71--82",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "In our previous paper [Ito et al., 1998], a
                 depth-dependent crossover was proposed for GP. The
                 purpose was to solve the difficulty of the blind
                 application of the normal crossover, i.e., building
                 blocks are broken unexpectedly. In the depth-dependent
                 crossover, the depth selection ratio was varied
                 according to the depth of a node. However, the
                 depth-dependent crossover did not work very effectively
                 as generated programs became larger. To overcome this,
                 we introduce a non-destructive depth-dependent
                 crossover, in which each offspring is kept only if its
                 fitness is better than that of its parent. We compare
                 GP performance with the depth-dependent crossover and
                 that with the non-destructive depth-dependent crossover
                 to show the effectiveness of our approach. Our
                 experimental results clarify that the non-destructive
                 depth-dependent crossover produces smaller programs
                 than the depth-dependent crossover.",
  notes =        "EuroGP'98. Santa Fe artificial ant",
}

@InProceedings{ito:1998:ddx,
  author =       "Takuya Ito and Hitoshi Iba and Satoshi Sato",
  title =        "Depth-Dependent Crossover for Genetic Programming",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "775--780",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c135.pdf",
  size =         "6 pages",
  abstract =     "It is known that selection and crossover operators
                 contribute to generate solutions in GP. Traditionally,
                 crossover points are selected randomly by a normal
                 (canonical) crossover. However, the traditional method
                 has several difficulties that building blocks (i.e.
                 effective partial programs) are broken because of blind
                 application of the normal crossover. This paper
                 proposes a depth-dependent crossover for GP, in which
                 the depth selection ratio is varied according to the
                 depth of a node. This proposed method is to accumulate
                 building blocks via the encapsulation of the
                 depth-dependent crossover. We compare GP performance
                 with the depth-dependent crossover and that with the
                 normal crossover. Our experimental results clarify that
                 the superiority of the proposed crossover to the
                 normal.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@InCollection{ito:1999:aigp3,
  author =       "Takuya Ito and Hitoshi Iba and Satoshi Sato",
  title =        "A Self-Tuning Mechanism for Depth-Dependent
                 Crossover",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "16",
  pages =        "377--399",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3 11 mux, santa fe ant, 4-even parity, simulated
                 robot",
}

@PhdThesis{TakuyaIto:thesis,
  author =       "Takuya Ito",
  title =        "Efficient program generation by genetic programming",
  school =       "Japan Advanced Instutute of Science and Technology",
  year =         "1999",
  address =      "Ishikawa",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@InCollection{ito:2000:RMUGA,
  author =       "Choshu Ito",
  title =        "{RF}-{LDMOSFET} Modeling Using Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "221--227",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{ivan:1998:aplspGP,
  author =       "Laur Ivan",
  title =        "Automatic Parallelization of Loops in Sequential
                 Programs using Genetic Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{iwashita:2002:imgwiaadc,
  author =       "Makoto Iwashita and Hitoshi Iba",
  title =        "Island Model {GP} with Immigrants Aging and
                 Depth-Dependent Crossover",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "267--272",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, Deme,
                 Migration",
  abstract =     "This paper proposes a new method for island model GP.
                 The proposed method applies a traditional genetic
                 operator to an aborigine and a depth-dependent
                 crossover to the immigrants according to their ages,
                 which show how long they survive in the island.This
                 method can provide both local and global search
                 strategies. The experimental results have shown that
                 our approach works effectively.",
}

@InProceedings{jacob:1994:glp,
  author =       "Christian Jacob",
  title =        "Genetic {L}-System Programming",
  booktitle =    "Parallel Problem Solving from Nature III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and Reinhard
                 M{\"a}nner",
  series =       "LNCS",
  volume =       "866",
  pages =        "334--343",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58484-6",
  URL =          "http://www2.informatik.uni-erlangen.de/IMMD-II/Persons/jacob/Publications/GeneticLSystemProgramming.ps.gz",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
  abstract =     "GLP combines simulated evolution and simulated
                 structure formation (based on Lindenmayer systems)",
  notes =        "PPSN3 L-systems difficult for human programmers to
                 use, presents simple example where L-system is evolved
                 using a GP.

                 Initial population created from pool of pre-defined
                 patterns (subtrees, building blocks?) rather than GP
                 functions or terminals. Such patterns and genetic
                 operators have a rank (like a fitness) which is used to
                 bias the choice of pattern. A pattern is a gramtical
                 rule and specifies (a number of possible) types for
                 each of its arguments.

                 Genetic operators include copying templates (sub
                 trees?) into the pattern pool (or genetic library).",
}

@InProceedings{jacob:1996:GPls,
  author =       "Christian Jacob",
  title =        "Evolving Evolution Programs: Genetic Programming and
                 {L}-Systems",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "107--115",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{jacob:1996:epe,
  author =       "Christian Jacob",
  title =        "Evolution Programs Evolved",
  booktitle =    "Parallel Problem Solving from Nature IV, Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  series =       "LNCS",
  volume =       "1141",
  pages =        "42--51",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming, L-Systems,
                 Growth Grammars, morphogenesis",
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
                 {"}3.2 Stochastic generation of L-system encodings by
                 template{"} Lindenmayer rewrite grammars",
}

@PhdThesis{jacob:thesis,
  author =       "Christian Jacob",
  title =        "MathEvolvica - Simulated Evolution of Development
                 Programs in Nature",
  school =       "Arbeitsberichte des Instituts fur Mathematische
                 Maschinen und Datenverarbeitung (IMMD), Informatik,
                 Band 28(10), Erlangen",
  year =         "1995",
  address =      "Germany",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  notes =        "in German

                 ",
}

@Book{jacob:1997:deutsch,
  author =       "Christian Jacob",
  title =        "Principia Evolvica -- Simulierte Evolution mit
                 Mathematica",
  publisher =    "dpunkt.verlag",
  year =         "1997",
  address =      "Heidelberg, Germany",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-920993-48-9",
  notes =        "The book has 712 pages and comes with a CD that
                 contains a lot of Mathematica notebooks with
                 explanatory text, graphics, and animations. I just
                 started some web pages to make part of this material
                 available:
                 http://www2.informatik.uni-erlangen.de/~jacob/Evolvica/EA-Mathematica.html

                 The publishers web site is: http://www.dpunkt.de

                 For English translation see jacob:2001:iecm",
  size =         "712 pages",
}

@Book{jacob:2001:iecm,
  author =       "Christian Jacob",
  title =        "Illustrating Evolutionary Computation with
                 Mathematica",
  publisher =    "Morgan Kaufmann",
  year =         "2001",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-637-8",
  URL =          "http://www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-637-8",
  abstract =     "An essential capacity of intelligence is the ability
                 to learn. An artificially intelligent system that could
                 learn would not have to be programmed for every
                 eventuality; it could adapt to its changing environment
                 and conditions just as biological systems do.
                 Illustrating Evolutionary Computation with Mathematica
                 introduces evolutionary computation to the technically
                 savvy reader who wishes to explore this fascinating and
                 increasingly important field. Unique among books on
                 evolutionary computation, the book also explores the
                 application of evolution to developmental processes in
                 nature, such as the growth processes in cells and
                 plants. If you are a newcomer to the evolutionary
                 computation field, an engineer, a programmer, or even a
                 biologist wanting to learn how to model the evolution
                 and coevolution of plants, this book will provide you
                 with a visually rich and engaging account of this
                 complex subject. Features:

                 Introduces the major mechanisms of biological
                 evolution.

                 Demonstrates many fascinating aspects of evolution in
                 nature with simple, yet illustrative examples. Explains
                 each of the major branches of evolutionary computation:
                 genetic algorithms, genetic programming, evolutionary
                 programming, and evolution strategies. Demonstrates the
                 programming of computers by evolutionary principles
                 using Evolvica, a genetic programming system designed
                 by the author. Shows in detail how to evolve
                 developmental programs modeled by cellular automata and
                 Lindenmayer systems. Provides Mathematica notebooks on
                 the Web that include all the programs in the book and
                 supporting animations, movies, and graphics. Christian
                 Jacob is assistant professor in the Department of
                 Computer Science at the University of Calgary. His
                 areas of interest include evolutionary algorithms,
                 Lindenmayer systems, ecosystems modeling, distributed
                 computing, alternative programming paradigms,
                 biocomputing, and bioinformatics. He is the author of
                 the German edition of this book, Principia Evolvica
                 Simulierte Evolution mit Mathematica
                 jacob:1997:deutsch

                 Part 1: Fascinating Evolution

                 Part 2: Evolutionary Computation

                 Part 3: If Darwin was a Programmer

                 Part 4: Evolution of Developmental Programs",
  notes =        "English version of jacob:1997:deutsch",
  size =         "578 pages",
}

@Article{jacob:2000:IS,
  author =       "Christian Jacob",
  title =        "The art of genetic programming",
  journal =      "IEEE Intelligent Systems",
  year =         "2000",
  volume =       "15",
  number =       "3",
  pages =        "83--84",
  month =        may # "-" # jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1094-7167",
  URL =          "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf",
  size =         "2 pages",
  notes =        "part of hirsh:2000:GP",
}

@InProceedings{jakobi:1998:rsete,
  author =       "Nick Jakobi and Phil Husbands and Tom Smith",
  title =        "Robot Space Exploration by Trial and Error",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "807--815",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Evolutionary Robotics",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{Jamshidi:2001:AMC,
  author =       "Mohammad Jamshidi",
  title =        "Autonomous control of complex systems: robotic
                 applications",
  journal =      "Applied Mathematics and Computation",
  volume =       "120",
  pages =        "15--29",
  year =         "2001",
  number =       "1-3",
  month =        "10 " # may,
  keywords =     "genetic algorithms, genetic programming, Autonomy,
                 Control systems, Complex systems, Robotics, Behavior
                 control",
  URL =          "http://www.sciencedirect.com/science/article/B6TY8-42RVSF8-3/1/d9087f02589b85a2c6ef556307f7c0a8",
  abstract =     "One of the biggest challenges of any control paradigm
                 is being able to handle large complex systems under
                 unforeseen uncertainties. A system may be called
                 complex here if its dimension (order) is too high and
                 its model (if available) is nonlinear, interconnected,
                 and information on the system is uncertain such that
                 classical techniques cannot easily handle the problem.
                 Soft computing, a collection of fuzzy logic,
                 neuro-computing, genetic algorithms and genetic
                 programming, has proven to be a powerful tool for
                 adding autonomy to many complex systems. For such
                 systems the size soft computing control architecture
                 will be nearly infinite. Examples of complex systems
                 are power networks, national air traffic control
                 system, an integrated manufacturing plant, etc. In this
                 paper a new rule base reduction approach is suggested
                 to manage large inference engines. Notions of rule
                 hierarchy and sensor data fusion are introduced and
                 combined to achieve desirable goals. New paradigms
                 using soft computing approaches are utilized to design
                 autonomous controllers for a number of robotic
                 applications at the ACE Center are also presented
                 briefly.",
}

@Article{janikow:1996:CGP,
  author =       "Cezary Z. Janikow",
  title =        "A Methodology for Processing Problem Constraints in
                 Genetic Programming",
  journal =      "Computers and Mathematics with Applications",
  year =         "1996",
  volume =       "32",
  number =       "8",
  pages =        "97--113",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.umsl.edu/Faculty/janikow/psdocs/cgp.CMwA.ps",
  URL =          "http://www.sciencedirect.com/science/article/B6TYJ-41GX54B-9/1/5a1e263b86597ce90a6eec429c357ce5",
  abstract =     "Search mechanisms of artificial intelligence combine
                 two elements: representation, which determines the
                 search space, and a search mechanism, which actually
                 explores the space. Unfortunately, many searches may
                 explore redundant and/or invalid solutions. Genetic
                 programming refers to a class of evolutionary
                 algorithms based on genetic algorithms, but utilizing a
                 parameterized representation in the form of trees.
                 These algorithms perform searches based on simulation
                 of nature. They face the same problems of
                 redundant/invalid subspaces. These problems have just
                 recently been addressed in a systematic manner. This
                 paper presents a methodology devised for the public
                 domain genetic programming tool lil-gp. This
                 methodology uses data typing and semantic information
                 to constrain the representation space so that only
                 valid, and possibly unique, solutions will be explored.
                 The user enters problem-specific constraints, which are
                 transformed into a normal set. This set is checked for
                 feasibility, and subsequently, it is used to limit the
                 space being explored. The constraints can determine
                 valid, possibly unique spaces. Moreover, they can also
                 be used to exclude subspaces the user considers
                 uninteresting, using some problem-specific knowledge. A
                 simple example is followed thoroughly to illustrate the
                 constraint language, transformations, and the normal
                 set. Experiments with Boolean 11-multiplexer illustrate
                 practical applications of the method to limit redundant
                 space exploration by utilizing problem-specific
                 knowledge.",
  notes =        "http://laplace.cs.umsl.edu/~janikow/cgp-lilgp/ CGP
                 uses GP [Koza] to evolve programs (or trees in
                 general). It extends GP by allowing syntactic and
                 sematical constraints on function calls (the
                 constraints can be weighted rather than strict), plus
                 function overloading. In future releases, evolution of
                 representation (i.e., constraints), ADFs, and recursive
                 functions are planned.

                 lil-gp comparison of solving 11-multiplexor problem
                 nine different ways with different type systems. Some
                 tighter (than Koza) type systems (eg different address
                 and data bits, different function sets) are worse than
                 Koza GP and some are better. Problem dependant reasons
                 for this suggested. Comparison with GIL. STGP",
}

@InProceedings{janikow:1998:pcCGP2.1,
  author =       "Cezary Z. Janikow and Scott DeWeese",
  title =        "Processing Constraints in Genetic Programming with
                 {CGP2.1}",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "173--180",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{kinnear:jannink,
  author =       "Jan Jannink",
  title =        "Cracking and Co-Evolving Randomizers",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "20",
  pages =        "425--443",
  size =         "19 pages",
  keywords =     "genetic algorithms, genetic programming, memory",
  notes =        "Uses protect mod. But doesnt give details.Uses
                 Teller's READ and WRITE Uses IFGEN macro to evolve two
                 separate functions in same tree Ref Kolmogorov (1965) =
                 on density of information packing Ref James (1990) = on
                 very long (10**170) sequence random numbers Initilises
                 so store[x]=x+1 rather than zero.",
}

@InProceedings{ga96aJaske,
  annote =       "*on,*FIN,genetic programming,astronomy /sunspots,time
                 series sunspots",
  author =       "Harri J{\"a}ske",
  title =        "One-step-ahead prediction of sunspots with genetic
                 programming",
  pages =        "79--88",
  year =         "1996",
  editor =       "Jarmo T. Alander",
  booktitle =    "Proceedings of the Second Nordic Workshop on Genetic
                 Algorithms and their Applications (2NWGA)",
  series =       "Proceedings of the University of Vaasa, Nro. 13",
  publisher =    "University of Vaasa",
  address =      "Vaasa (Finland)",
  month =        "19.-23.~" # aug,
  organisation = "Finnish Artificial Intelligence Society",
  keywords =     "genetic algorithms, genetic programming, time series
                 prediction , sunspots",
  URL =          "ftp://ftp.uwasa.fi/cs/2NWGA/Jaske.ps.Z",
  URL =          "http://www.uwasa.fi/cs/publications/2NWGA/node70.html#SECTION04700000000000000000",
  abstract =     "Timeinvariant nonlinear one-step-ahead prediction
                 models were developed by genetic programming. As a test
                 case benchmark sunspot series was used. Functional form
                 and numerical parameters of the models were optimized.
                 The generalisation ability, i.e. final suitability, of
                 the predictors was assessed through crossvalidation.
                 The results were compared to those of threshold
                 autoregression and neural network -based predictors of
                 the sunspot benchmarks found in literature. Standard
                 GP-approach is shown not to be sufficient to solve this
                 prediction problem as well as the methods in comparison
                 do.",
  notes =        "lil-gp, non standard GP parameters? {"}evolved models
                 might not be numerically stable{"} page 86

                 ",
}

@InProceedings{Jaske:1997:crGP,
  author =       "Harri Jaske",
  title =        "On code reuse in genetic programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "201--206",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{jelasity:1999:TASEC,
  author =       "Mark Jelasity",
  title =        "The Adaptationist Stance and Evolutionary
                 Computation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1859--1864",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "methodology, pedagogy and philosophy",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{jezic:1998:GAstitcm,
  author =       "Gordan Jezic and Robert Kostelac and Ignac Lovrek and
                 Vjekoslav Sinkovic",
  title =        "Genetic Algorithms for Scheduling Tasks with
                 Non-negligible Intertask Communication onto
                 Multiprocessors",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "518",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@PhdThesis{Jiang:1992:thesis,
  author =       "M. Jiang",
  title =        "A hierarchical genetic system for symbolic function
                 identification",
  school =       "University of Montana",
  year =         "1992",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@InProceedings{Jiang:1993:afis,
  author =       "M. Jiang",
  title =        "An adaptive function identification system",
  booktitle =    "Proceedings of the IEEE/ACM Conference on Developing
                 and Managing Intelligent System Projects, Vienna,
                 Virginia, USA",
  year =         "1993",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Jiang:1992:hGPsfi,
  author =       "Minga Jiang and Alden H. Wright",
  title =        "A Hierarchical Genetic System for Symbolic Function
                 Identification",
  institution =  "University of Montana, Missoula, MT 59812",
  booktitle =    "Proceedings of the 24th Symposium on the Interface:
                 Computing Science and Statistics, College Station,
                 Texas",
  year =         "1992",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Also available as techincal report, 26 pages. Does
                 Symbolic regression but uses Levenberg-Marquadt
                 statistical technique to adjust parameters to get
                 closer (equivalent of local hill climbing?) Some case
                 GP don't work on. Mentions Permutation but don't say
                 how usefully it is

                 ",
}

@InProceedings{jiang:1996:aGAidc,
  author =       "J. Jiang and D. Butler",
  title =        "An Adaptive Genetic Algorithm for Image Data
                 Compression",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "83--87",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{jiang:1998:eaosesMSTsd,
  author =       "Tianzi Jiang",
  title =        "An Evolutionary Approach to Optimal Structuring
                 Element Extraction for {MST}-Based Shapes Description",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{jo:1999:ECAOPPMR,
  author =       "Yong-Gun Jo and Hoon Kang",
  title =        "Evolutionary Cellular Automata for Optimal Path
                 Planning of Mobile Robots",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1443",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{joffe:1995:AGASNPP,
  author =       "David Joffe",
  title =        "A Genetic Algorithm for a Stochastic Network Planning
                 Problem",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "107--116",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@MastersThesis{johanson:1997:masters,
  author =       "Brad Johanson",
  title =        "Automated Fitness Raters for {GP}-Music System",
  school =       "School of Computer Science, University of Birmingham",
  year =         "1997",
  address =      "Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~rmp/eebic/WSC2/gp-music/gp_music.html/gp-music-auto-raters.ps.gz",
  size =         "pages",
}

@TechReport{Johanson98,
  author =       "Bradley E Johanson and Riccardo Poli",
  title =        "{GP}-Music: An Interactive Genetic Programming System
                 for Music Generation with Automated Fitness Raters",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-98-13",
  month =        may,
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  email =        "bjohanso@stanford.edu, R.Poli@cs.bham.ac.uk",
  file =         "/1998/CSRP-98-13.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-13.ps.gz",
  url_2 =        "http://graphics.stanford.edu/~bjohanso/gp-music/tech-report",
  abstract =     "In this paper we present the GP-Music System, an
                 interactive system which allows users to evolve short
                 musical sequences using interactive genetic
                 programming, and its extensions aimed at making the
                 system fully automated. The basic GP system works by
                 using a genetic programming algorithm, a small set of
                 functions for creating musical sequences, and a user
                 interface which allows the user to rate individual
                 sequences. With this user interactive technique it was
                 possible to generate pleasant tunes over runs of 20
                 individuals over 10 generations. As the user is the
                 bottleneck in interactive systems, the system takes
                 rating data from a users run and uses it to train a
                 neural network based automatic rater, or {"}auto
                 rater{"}, which can replace the user in bigger runs.
                 Using this auto rater we were able to make runs of up
                 to 50 generations with 500 individuals per generation.
                 The best of run pieces generated by the auto raters
                 were pleasant but were not, in general, as nice as
                 those generated in user interactive runs.",
}

@InProceedings{johanson:1998:GP-Music,
  author =       "Brad Johanson and Riccardo Poli",
  title =        "{GP}-Music: An Interactive Genetic Programming System
                 for Music Generation with Automated Fitness Raters",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "181--186",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98, see also Johanson98",
}

@TechReport{johansson:1996:rfbcGPtr,
  author =       "S. J. Johansson",
  title =        "Evolving integer recurrences using genetic
                 programming",
  institution =  "Faculteit der Wiskunde en Informatica, VU Amsterdam",
  year =         "1996",
  number =       "IR 402",
  address =      "Holland",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Shelf mark A-26012 CWI library 2-12-98",
  size =         "38 pages",
}

@InProceedings{johansson:1996:rfbcGP,
  author =       "Stefan J. Johansson",
  title =        "Recurrences with Fixed Base Cases in Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "430",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@InProceedings{john:1999:GARS,
  author =       "Maria John and David Panton and Kevin White",
  title =        "Genetic Algorithm for Regional Surveillance",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1573--1579",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{johnson:1999:eurogp,
  author =       "Helen Johnson",
  title =        "Euro{GP} {A} biologist's persepective",
  journal =      "EvoNEWS",
  year =         "1999",
  volume =       "11",
  pages =        "11",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dcs.napier.ac.uk/evonet/Coordinator/evonews/evonews11.pdf
                 ?",
  size =         "0.5 page",
}

@Article{Johnson:2000:eamGPsir,
  author =       "Helen E. Johnson and Richard J. Gilbert and Michael K.
                 Winson and Royston Goodacre and Aileen R. Smith and Jem
                 J. Rowland and Michael A. Hall and Douglas B. Kell",
  title =        "Explanatory Analysis of the Metabolome Using Genetic
                 Programming of Simple, Interpretable Rules",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "3",
  pages =        "243--258",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, metabolome,
                 tomato fruit, salinity, Fourier transform
                 infra-spectroscopy (FTIR), chemometrics",
  ISSN =         "1389-2576",
  abstract =     "Genetic programming, in conjunction with advanced
                 analytical instruments, is a novel tool for the
                 investigation of complex biological systems at the
                 whole-tissue level. In this study, samples from tomato
                 fruit grown hydroponically under both high- and
                 low-salt conditions were analysed using
                 Fourier-transform infrared spectroscopy (FTIR), with
                 the aim of identifying spectral and biochemical
                 features linked to salinity in the growth environment.
                 FTIR spectra of whole tissue extracts are not amenable
                 to direct visual analysis, so numerical modelling
                 methods were used to generate models capable of
                 classifying the samples based on their spectral
                 characteristics. Genetic programming (GP) provided
                 models with a better prediction accuracy to the
                 conventional data modelling methods used, whilst being
                 much easier to interpret in terms of the variables
                 used. Examination of the GP-derived models showed that
                 there were a small number of spectral regions that were
                 consistently being used. In particular, the spectral
                 region containing absorbances potentially due to a
                 cyanide/nitrile functional group was identified as
                 discriminatory. The explanatory power of the GP models
                 enabled a chemical interpretation of the biochemical
                 differences to be proposed. The combination of FTIR and
                 GP is therefore a powerful and novel analytical tool
                 that, in this study, improves our understanding of the
                 biochemistry of salt tolerance in tomato plants.",
}

@InProceedings{JJohnson:2000:GECCOlb,
  author =       "Judy Johnson and Soundar Kumara",
  title =        "Coadaptation of Cooperative Players in an Iterated
                 Prisoners Dilemma Game using an {XML} Based {GA}",
  pages =        "147--154",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Part of whitley:2000:GECCOlb",
}

@Article{johnson:1996:GPadac,
  author =       "R. Colin Johnson",
  title =        "Genetic program auto-designs analog circuits",
  journal =      "Electronic Engineering Times",
  year =         "1996",
  number =       "904",
  month =        "30 " # may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.eet.com/news/96/hr903.html#genetic",
  notes =        "short On-line publication",
}

@InProceedings{johnson:1994:EVR,
  author =       "Michael Patrick Johnson and Pattie Maes and Trevor
                 Darrell",
  title =        "Evolving Visual Routines",
  booktitle =    "ARTIFICIAL LIFE IV, Proceedings of the fourth
                 International Workshop on the Synthesis and Simulation
                 of Living Systems",
  year =         "1994",
  editor =       "Rodney A. Brooks and Pattie Maes",
  pages =        "198--209",
  address =      "MIT, Cambridge, MA, USA",
  month =        "6-8 " # jul,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://media.mit.edu/pub/agents/autonomous-agents/alife-iv.ps.Z",
  abstract =     "Traditional machine vision assumes that the vision
                 system recovers a complete, labeled description of the
                 world [Marr]. Recently, several researchers have
                 criticized this model and proposed an alternative model
                 which considers perception as a distributed collection
                 of task-specific, task-driven visual routines
                 [Aloimonos, Ullman]. Some of these researchers have
                 argued that in natural living systems these visual
                 routines are the product of natural selection
                 [ramachandran]. So far, researchers have hand-coded
                 task-specific visual routines for actual
                 implementations (e.g. [Chapman]). In this paper we
                 propose an alternative approach in which visual
                 routines for simple tasks are evolved using an
                 artificial evolution approach. We present results from
                 a series of runs on actual camera images, in which
                 simple routines were evolved using Genetic Programming
                 techniques [Koza]. The results obtained are promising:
                 the evolved routines are able to correctly classify up
                 to 93% of the images, which is better than the best
                 algorithm we were able to write by hand.",
  notes =        "alife-4

                 ",
}

@InCollection{johnson:1995:AAECASESGP,
  author =       "Bryan H. Johnson",
  title =        "An Attempt to Evolve Cooperation Among Separately
                 Evolved Structure in Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "117--126",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InCollection{johnson:1999:SPUGATS,
  author =       "Soren Johnson",
  title =        "Swords vs. Plowshares: Using Genetic Algorithms in
                 Turn-Based Strategy",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "76--85",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{johnson:2002:EuroGP,
  title =        "Deriving genetic programming fitness properties by
                 static analysis",
  author =       "Colin Johnson",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "298--307",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  URL =          "http://www.cs.ukc.ac.uk/pubs/2002/1351",
  abstract =     "The aim of this paper is to introduce the idea of
                 using static analysis of computer programs as a way of
                 measuring fitness in genetic programming. Such
                 techniques extract information about the programs
                 without explicitly running them, and in particular they
                 infer properties which hold across the whole of the
                 input space of a program. This can be applied to
                 measure fitness, and has a number of advantages over
                 measuring fitness by running members of the population
                 on test cases. The most important advantage is that if
                 a solution is found then it is possible to formally
                 trust that solution to be correct across all inputs.
                 This paper introduces these ideas, discusses various
                 ways in which they could be applied, discusses the type
                 of problems for which they are appropriate, and ends by
                 giving a simple test example and some questions for
                 future research.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@MastersThesis{Jones:1991:masters,
  author =       "A. Jones",
  title =        "Writing Programs Using Genetic Algorithms",
  school =       "Department of Computer Science, University of
                 Manchester, United Kingdom",
  year =         "1991",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@Misc{jones:1993:GPreview,
  author =       "A. J. Jones",
  title =        "Genetic Programming - on the Programming of Computers
                 by Means of Natural Selection - Koza, {J}.{R}.",
  howpublished = "Nature",
  year =         "1993",
  volume =       "363",
  number =       "6426",
  pages =        "222",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  notes =        "review of koza:book

                 ",
}

@Article{Jones:1998:qmpmalssl,
  author =       "Alun Jones and Daniella Young and Janet Taylor and
                 Douglas B. Kell and Jem J Rowland",
  title =        "Quantification of microbial productivity via
                 multi-angle light scattering and supervised learning",
  journal =      "Biotechnology and Bioengineering",
  year =         "1998",
  volume =       "59",
  number =       "2",
  pages =        "131--143",
  month =        "20 " # jul,
  publisher =    "John Wiley and Sons",
  keywords =     "genetic algorithms, genetic programming, chemometrics,
                 light scattering. microbial productivity",
  abstract =     "This article describes the use of chemometric methods
                 for prediction of biological parameters of cell
                 suspensions on the basis of their light scattering
                 profiles. Laser light is directed into a vial or flow
                 cell containing media from the suspension. The
                 intensity of the scattered light is recorded at 18
                 angles. Supervised learning methods are then used to
                 calibrate a model relating the parameter of interest to
                 the intensity values. Using such models opens up the
                 possibility of estimating the biological properties of
                 fermentor broths extremely rapidly (typically every 4
                 sec), and, using the flow cell, without user
                 interaction. Our work has demonstrated the usefulness
                 of this approach for estimation of yeast cell counts
                 over a wide range of values (10(5)-10(9) cells mL-1),
                 although it was less successful in predicting cell
                 viability in such suspensions.",
}

@InProceedings{jones:1999:Gdec,
  author =       "Eric A. Jones and William T. Joines",
  title =        "Genetic design of electronic circuits",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "125--133",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, Genetic Programming, low-pass
                 filter design",
  notes =        "GECCO-99LB. Grammatical Evolution",
}

@InProceedings{jong:2001:gecco,
  title =        "Reducing Bloat and Promoting Diversity using
                 Multi-Objective Methods",
  author =       "Edwin D. de Jong and Richard A. Watson and Jordan B.
                 Pollack",
  pages =        "11--18",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, code growth,
                 bloat, introns, diversity maintenance, evolutionary
                 multi-objective optimization, Pareto, optimality",
  ISBN =         "1-55860-774-9",
  URL =          "http://www.demo.cs.brandeis.edu/papers/long.html#rbpd_gecco01",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@MastersThesis{jonhson:1995:mscthesis,
  author =       "Michael Patrick Johnson",
  title =        "Evolving Visual Routines",
  school =       "School or Architecture and Planning, MIT, USA",
  year =         "1995",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, visual
                 routines, active vision, machine learning",
  URL =          "http://lcs.www.media.mit.edu/people/aries/ms-thesis.ps.gz",
  size =         "117 pages",
  notes =        "Extension of johnson:1994:EVR Applies Genetic
                 Programming to the problem of Active Vision",
}

@InCollection{jonsson:1996:csb,
  author =       "Per Jonsson and Jonas Barklund",
  title =        "Characterizing Signal Behaviour Using Genetic
                 Programming",
  booktitle =    "Evolutionary Computing",
  publisher =    "Springer-Verlag",
  year =         "1996",
  editor =       "T. C. Fogarty",
  number =       "1143",
  series =       "Lecture Notes in Computer Science",
  pages =        "62--72",
  address =      "University of Sussex, UK",
  month =        "1-2 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61749-3",
  notes =        "The post-workshop proceedings of the 1996 AISB
                 workshop on evolutionary computing.",
  size =         "11 pages",
}

@TechReport{juels:1995:shceGA,
  author =       "Ari Juels and Martin Wattenberg",
  title =        "Stochastic Hillclimbing as a Baseline Method for
                 Evaluating Genetic Algorithms",
  institution =  "Department of Computer Science, University of
                 California at Berkeley",
  year =         "1995",
  type =         "Technical Report",
  number =       "CSD-94-834",
  address =      "USA",
  month =        "18 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://citeseer.nj.nec.com/juels94stochastic.html",
  notes =        "{"}We demonstate that simple stochastic hillcliming
                 methods are able to achieve results comparable or
                 superior to those obtained by the GAs{"}. 4 GAs one is
                 Koza's
                 11-multiplexor.

                 citeseer.nj.nec.com/juels94stochastic.html may be
                 slightly different from CSD-94-834",
}

@InProceedings{juille:1995:fgSIMD,
  author =       "Hugues Juille and Jordan B. Pollack",
  title =        "Parallel Genetic Programming and Fine-Grained {SIMD}
                 Architecture",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "31--37",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.brandeis.edu/~hugues/papers/AAAI_GP_95.ps.gz",
  notes =        "AAAI-95f GP {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/

                 tic-tak-toe coevolution",
}

@InCollection{pollack:1996:aigp2,
  author =       "Hugues Juille and Jordan B. Pollack",
  title =        "Massively Parallel Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "339--358",
  chapter =      "17",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "ftp://ftp.cs.brandeis.edu/pub/faculty/pollack/gp2.ps.Z",
  abstract =     "As the field of Genetic Programming (GP) matures and
                 its breadth of application increases, the need for
                 parallel implementations becomes absolutely necessary.
                 The transputer-based system presented in [Koza95] is
                 one of the rare such parallel implementations. Until
                 today, no implementation has been proposed for parallel
                 GP using a SIMD architecture, except for a
                 data-parallel approach [tufts95], although others have
                 exploited workstation farms and pipelined
                 supercomputers. One reason is certainly the apparent
                 difficulty of dealing with the parallel evaluation of
                 different S-expressions when only a single instruction
                 can be executed at the same time on every processor.
                 The aim of this chapter is to present such an
                 implementation of parallel GP on a SIMD system, where
                 each processor can efficiently evaluate a different
                 S-expression. We have implemented this approach on a
                 MasPar MP-2 computer, and will present some timing
                 results. To the extent that SIMD machines, like the
                 MasPar are available to offer cost-effective cycles for
                 scientific experimentation, this is a useful
                 approach.

                 ",
  notes =        "tic-tak-toe, intertwined spirals, coevolution",
  size =         "21 pages",
}

@InProceedings{juile:1996:dcl,
  author =       "Hugues Juille and Jordan B. Pollack",
  title =        "Dynamics of Co-evolutionary Learning",
  booktitle =    "Proceedings of the Fourth International Conference on
                 Simulation of Adaptive Behavior: From animals to
                 animats 4",
  year =         "1996",
  editor =       "Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer
                 and Jordan Pollack and Stewart W. Wilson",
  pages =        "526--534",
  address =      "Cape Code, USA",
  publisher_address = "Cambridge, MA, USA",
  month =        "9-13 " # sep,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-63178-4",
  URL =          "http://www.cs.brandeis.edu/~hugues/papers/SAB_96.ps.gz",
  notes =        "SAB-96",
}

@InProceedings{juille:1996:cis,
  author =       "Hugues Juille and Jordan B Pollack",
  title =        "Co-evolving Intertwined Spirals",
  booktitle =    "Evolutionary Programming V: Proceedings of the Fifth
                 Annual Conference on Evolutionary Programming",
  year =         "1996",
  editor =       "Lawrence J. Fogel and Peter J. Angeline and Thomas
                 Baeck",
  pages =        "461--467",
  address =      "San Diego",
  publisher_address = "Cambridge, MA, USA",
  month =        feb # " 29-" # mar # " 3",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-06190-2",
  URL =          "http://www.cs.brandeis.edu/~hugues/papers/EP_96.ps.gz",
  notes =        "EP-96
                 http://www.natural-selection.com/eps/EP96.html

                 Massively Parallel Genetic Programming MPGP on SIMD
                 machine of 4096 processors, the Maspar MP-2",
}

@InProceedings{juille:1998:cit:adCAr,
  author =       "Hugues Juille and Jordan B. Pollack",
  title =        "Coevolving the Ideal Trainer: Application to the
                 Discovery of Cellular Automata Rules",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "519--527",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  URL =          "http://www.cs.brandeis.edu/~hugues/papers/GP_98.ps.gz",
  notes =        "SGA-98",
}

@InProceedings{juille:1998:carig,
  author =       "Hugues Juille and Jordan Pollack",
  title =        "Coevolutionary Arms Race Improves Generalization",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{juille:1998:shtsgi,
  author =       "Hugues Juille and Jordan B. Pollack",
  title =        "A Sampling-Based Heuristic for Tree Search Applied to
                 Grammar Induction",
  booktitle =    "Proceedings of the Fifteenth National Conference on
                 Artificial Intelligence (AAAI-98) Tenth Conference on
                 Innovative Applications of Artificial Intelligence
                 (IAAI-98)",
  year =         "1998",
  address =      "Madison, Wisconsin, USA",
  month =        "26-30 " # jul,
  publisher =    "AAAI Press Books",
  keywords =     "genetic algorithms, genetic programming, search,
                 massively parallel systems, inductive learning",
  URL =          "http://www.cs.brandeis.edu/~hugues/papers/AAAI_98.ps.gz",
  abstract =     "In the field of Operation Research and Artificial
                 Intelligence, several stoch astic search algorithms
                 have been designed based on the theory of global random
                 search (Zhigljavsky, 1991). Basically, those techniques
                 iteratively sample the s earch space with respect to a
                 probability distribution which is updated accordin g to
                 the result of previous samples and some predefined
                 strategy. Genetic Algori thms (GAs) (Goldberg, 1989) or
                 Greedy Randomized Adaptive Search Procedures (GRA SP)
                 (Feo &amp; Resende, 1995) are two particular instances
                 of this paradigm. In this paper, we present SAGE, a
                 search algorithm based on the same fundamental me
                 chanisms as those techniques. However, it addresses a
                 class of problems for whic h it is difficult to design
                 transformation operators to perform local search bec
                 ause of intrinsic constraints in the definition of the
                 problem itself. For those problems, a procedural
                 approach is the natural way to construct solutions,
                 resu lting in a state space represented as a tree or a
                 DAG. The aim of this paper is to describe the
                 underlying heuristics used by SAGE to address problems
                 belonging to that class. The performance of SAGE is
                 analyzed on the problem of grammar in duction and its
                 successful application to problems from the recent
                 Abbadingo DFA learning competition is presented.",
}

@InProceedings{julstrom:1996:clnra,
  author =       "Bryant A. Julstrom",
  title =        "Contest Length, Noise, and Reciprocal Altruism in the
                 Population of a Genetic Algorithm for the Iterated
                 Prisoner's Dilemma",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "88--93",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{Julstrom:1997:swcga,
  author =       "Bryant A. Julstrom",
  title =        "Strings of Weights as Chromosomes in Genetic
                 Algorithms for the Traveling Salesman Problem",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "100--106",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{julstrom:1998:idaitwGATSP,
  author =       "Bryant A. Julstrom",
  title =        "Insertion Decoding Algorithms and Initial Tours in a
                 Weight-Coded {GA} for {TSP}",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "528--534",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{julstrom:1998:mwpwcTSP,
  author =       "Bryant A. Julstrom",
  title =        "The Maximum Weight Parameter in a Weight-Coded {GA}
                 for {TSP}",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms",
  notes =        "GP-98LB",
}

@InProceedings{julstrom:1999:RGEMNBH,
  author =       "Bryant A. Julstrom",
  title =        "Redundant Genetic Encodings May Not Be Harmful",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "791",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{julstrom:1999:CDBL,
  author =       "Bryant A. Julstrom",
  title =        "Comparing Darwinian, Baldwinian, and Lamarckian search
                 in a genetic algorithm for the 4-Cycle problem",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "134--138",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@InProceedings{julstrom:2002:gecco:lbp,
  title =        "Manipulating Valid Solutions in a Genetic Algorithm
                 for the Bounded-Diameter Minimum Spanning Tree
                 Problem",
  author =       "Bryant A. Julstrom",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "247--254",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

@InProceedings{kaboudan:1998:sesrfts,
  author =       "M. Kaboudan and M. Vance",
  title =        "Statistical Evaluation of Symbolic Regression
                 Forecasting of Time-Series",
  booktitle =    "Proceedings of the International Federation of
                 Automatic Control Symposium on Computation in
                 Economics, Finance and Engineering: Economic Systems",
  year =         "1998",
  address =      "Cambridge, UK",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{kaboudan:1998:fsrGPC,
  author =       "M. Kaboudan",
  title =        "Forecasting Stock Returns Using Genetic Programming in
                 {C}++",
  booktitle =    "Proceedings of 11th Annual Florida Artificial
                 Intelligence International Research Symposium",
  year =         "1998",
  address =      "Florida",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{kaboudan:1998:GPadcns,
  author =       "M. A. Kaboudan",
  title =        "A {GP} Approach to Distinguish Chaotic from Noisy
                 Signals",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "187--191",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{kaboudan:1999:seGP,
  author =       "M. A. Kaboudan",
  title =        "Statistical Evaluation of Genetic Programming",
  booktitle =    "Fifth International Conference: Computing in Economics
                 and Finance",
  year =         "1999",
  editor =       "David A. Belsley and Christopher F. Baum",
  pages =        "148",
  address =      "Boston College, MA, USA",
  month =        "24-26 " # jun,
  note =         "Book of Abstracts",
  keywords =     "genetic algorithms, genetic programming, GP-QUICK",
  URL =          "http://www.lv.psu.edu/mak7/GP-Stat.htm",
  size =         "1 page",
  abstract =     "A recent advance in genetic computations is the
                 heuristic prediction model (symbolic regression), which
                 have received little statistical scrutiny. Diagnostic
                 checks of genetically evolved models (GEMs) as a
                 forecasting method are therefore essential. This
                 requires assessing the statistical properties of errors
                 produced by GEMs. Since the predicted models and their
                 forecasts are produced artificially by a computer
                 program, little controls the final model specification.
                 However, it is of interest to understand the final
                 specification and to know the statistical
                 characteristics of its errors, particularly if
                 artificially produced models furnish better forecasts
                 than humanly conceived ones. This paper's main concern
                 is the statistical analysis of errors from genetically
                 evolved models. Genetic programming (GP) is one of two
                 computational algorithms for evolving regression
                 models, the other being evolutionary programming (EP).
                 GP-QUICK computer code written in C ++ evolves the
                 regression models for this study. GP-QUICK replicates
                 an original GP program in LISP by Koza. Both are
                 designed to evolve regression models randomly, finding
                 one that replicates the series' data-generating process
                 best. Prediction errors from GP evolved regression
                 models are tested for whiteness (or autocorrelation)
                 and for normality. Well-established diagnostic tools
                 for linear time-series modeling apply also to nonlinear
                 models. Only diagnostic methods using errors without
                 having to replicate the models that produced them are
                 selected and applied to series. This restriction is
                 avoids reproducing the resulting genetically evolved
                 equations. These equations are generated by a random
                 selection mechanism almost impossible to replicate with
                 GP unless the process is deterministic, and they are
                 usually too complex for standard statistical software
                 to reproduce and analyze. The diagnostic methods are
                 selected for their simplicity and speed of execution
                 without sacrificing reliability. This paper contains
                 four other sections. One presents the diagnostic tools
                 to determine the statistical properties of residuals
                 produced by GEMs. Residuals from evolved models
                 representing systems with known characteristics are
                 used to evaluate the statistical performance of GEMs.
                 Another furnishes six data-generating processes
                 representing linear, linear-stochastic, nonlinear,
                 nonlinear-stochastic, and pseudo-random systems for
                 which models are evolved and residuals computed. The
                 final contains those residuals' diagnostics. Diagnostic
                 tools include the Kolmogorov-Smirnov test for whiteness
                 developed by Durbin (1969) in addition to statistical
                 testing of the null hypotheses that the fitted
                 residuals' mean, skewness, and kurtosis are
                 independently equal to zero. Conclusions and future
                 research are given.",
  notes =        "CEF'99 RePEc:sce:scecf9:1031 23 Nov 1999: Our printers
                 barf if given GP-Stat.prn",
}

@Article{Kaboudan:1999:GPpsp,
  author =       "M. A. Kaboudan",
  title =        "Genetic Programming Prediction of Stock Prices",
  journal =      "Computational Economics",
  year =         "2000",
  volume =       "6",
  number =       "3",
  pages =        "207--236",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
}

@Article{Kaboudan:1999:mtspGP,
  author =       "M. Kaboudan",
  title =        "A Measure of Time Series Predictability Using Genetic
                 Programming Applied to Stock Returns",
  journal =      "Journal of Forecasting",
  year =         "1999",
  volume =       "18",
  pages =        "345--357",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{kaboudan:1999:GERMBEF,
  author =       "M. A. Kaboudan",
  title =        "Genetic Evolution of Regression Models for Business
                 and Economic Forecasting",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1260--1268",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, forecasting",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@Article{kaboudan:2000:gemnfr,
  author =       "M. A. Kaboudan",
  title =        "Genetically evolved models and normality of their
                 fitted residuals",
  journal =      "Journal of Economic Dynamics and Control",
  year =         "2001",
  volume =       "25",
  number =       "11",
  pages =        "1719--1749",
  month =        "1 " # nov,
  email =        "Mahmoud_Kaboudan@Redlands.edu",
  keywords =     "genetic algorithms, genetic programming, Model
                 evaluation, Sunspot numbers, Canadian lynx data",
  URL =          "http://www.sciencedirect.com/science/article/B6V85-43DKSHS-2/1/814779519703b0e20b2ed476f932e7e5",
  size =         "31 pages",
  abstract =     "This paper evaluates performance of genetically
                 evolved models. GPQuick, a genetic programming software
                 written in C++, is used to evolve best-fit regression
                 models for simulated and real world data. Simulated
                 data are twelve time series with different but known
                 dynamical structures. Predicted values from best models
                 are compared with originally simulated data and the
                 residuals are statistically evaluated. The results
                 suggest that genetic programming approximates less
                 complex and less noisy data better than it does more
                 complex and noisy data. GPQuick is then used to evolve
                 models of real world data extracted from Canadian lynx
                 and sunspot numbers.",
  notes =        "JEL Classification: C63; C45; C52. cf. CEF'2000.",
}

@InProceedings{kaboudan:2001:cfcop,
  author =       "M. A. Kaboudan",
  title =        "Compumetric Forecasting of Crude Oil Prices",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "283--287",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, ANN",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@InCollection{kalanithi:1999:CPPBSEGP,
  author =       "Jeevan J. Kalanithi",
  title =        "Co-Evolution of Predator and Prey Behaviors in a
                 Simulated Environment using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "86--94",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{kalganova:1999:EDCVL,
  author =       "Tatiana Kalganova and Julian F. Miller and Terence C.
                 Fogarty",
  title =        "Evolution of the Digital Circuits with Variable
                 Layouts",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1235",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Kalmykov:1996:WSC,
  author =       "Vyacheslav Kalmykov",
  title =        "The Integral Algorithm of Organization and Evolution
                 of the Living Up to Culture - the Possible Instrument
                 for Genetic Programming",
  booktitle =    "The 1st Online Workshop on Soft Computing (WSC1)",
  year =         "1996",
  address =      "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/",
  month =        "19--30 " # aug,
  organisation = "Research Group on ECOmp of the Society of Fuzzy Theory
                 and Systems (SOFT)",
  publisher =    "Nagoya University, Japan",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/files/kalmykov.txt",
  abstract =     "The paper present correct physicomathematical
                 formulating the invariant operational scheme of
                 organization (space correlations) and evolution (time
                 correlations) of the living, including a new
                 generalized conception of information. This new
                 methodological innovation would permit the creation of
                 a programs that solve problems in the full sense (in
                 essence and integrally), and not only as imitation. The
                 chief elements of the proposed operational schemes are
                 as follows: - elementary operations on information,
                 energy and matter; life is realization of some
                 combinations of these operations; the combinations form
                 a mathematical group; - general definitions of control,
                 reproduction and creation operations; interdependence
                 of these integrative operations (on elementary
                 operations) in the organism; - generalized conception
                 of information; - consecutive stages of arising and
                 evolution of the organisms; - the generalized criterion
                 of the life evolution direction; - the generalized
                 definition of life, culture, functional elements of
                 culture ...",
  notes =        "Artificial life Alife ? See discussion page at WSC1
                 email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp",
}

@InCollection{kalyur:1995:EDPGP,
  author =       "Sesha Kalyur",
  title =        "Error Driven Parallelization of a Genetic Program",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "127--134",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InCollection{kamani:1995:BLICAARIDE,
  author =       "Sejal Kamani",
  title =        "Behavior Learning and Individual Cooperation in
                 Autonomous Agents as a Result of Interaction Dynamics
                 with the Environment",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "135--144",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{kammeyer:1996:SCFG,
  author =       "Thomas Kammeyer and R. K. Belew",
  title =        "Stochastic Context-Free Grammar Induction with a
                 Genetic Algorithm Using Local Search",
  booktitle =    "Foundations of Genetic Algorithms IV",
  year =         "1996",
  editor =       "Richard K. Belew and Michael Vose",
  address =      "University of San Diego, CA, USA",
  month =        "3--5 " # aug,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, CFG",
  ISBN =         "1-55860-460-X",
  notes =        "FOGA-4 Variable length chromosome with introns used to
                 specify stochastic grammar with BNF like syntax",
}

@Article{Young-MinKang:1999:echraephm,
  author =       "Young-Min Kang and Hwan-Gue Cho and Ee-Taek Lee",
  title =        "An efficient control over human running animation with
                 extension of planar hopper model",
  journal =      "The Journal of Visualization and Computer Animation",
  year =         "1999",
  volume =       "10",
  number =       "4",
  pages =        "215--224",
  keywords =     "genetic algorithms, genetic programming, animation,
                 human gait, energy control",
  ISSN =         "1049-8907",
  URL =          "http://www3.interscience.wiley.com/cgi-bin/abstract/68501003/START",
  abstract =     "The most important goal of character animation is to
                 efficiently control the motions of a character. Until
                 now, many techniques have been proposed for human gait
                 animation. Some techniques have been created to control
                 the emotions in gaits such as tired walking and brisk
                 walking by using parameter interpolation or motion data
                 mapping. Since it is very difficult to automate the
                 control over the emotion of a motion, the emotions of a
                 character model have been generated by creative
                 animators. This paper proposes a human running model
                 based on a one-legged planar hopper with a
                 self-balancing mechanism. The proposed technique
                 exploits genetic programming to optimize movement and
                 can be easily adapted to various character models. We
                 extend the energy minimization technique to generate
                 various motions in accordance with emotional
                 specifications. Copyright © 1999 John Wiley & Sons,
                 Ltd.",
}

@InProceedings{Kang:2002:IJCNN,
  author =       "Zhou Kang and Yan Li and Hugo {de Garis} and Li-Shan
                 Kang",
  title =        "A Multi-Level And Multi-Scale Evolutionary Modeling
                 System For Scientific Data",
  booktitle =    "Proceedings of the 2002 International Joint Conference
                 on Neural Networks IJCNN'02",
  pages =        "737--742",
  year =         "2002",
  month =        "12-17 " # may,
  address =      "Hilton Hawaiian Village Hotel, Honolulu, Hawaii",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE",
  ISBN =         "0-7803-7278-6",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The discovery of scientific laws is always built on
                 the basis of scientific experiments and observed data.
                 Any real world complex system must be controlled by
                 some basic laws, including macroscopic level,
                 submicroscopic level and microscopic level laws. How to
                 discover its necessity-laws from these observed data is
                 the most important task of data mining (DM) and KDD.
                 Based on the evolutionary computation, this paper
                 proposes a multi-level and multi -scale evolutionary
                 modeling system which models the macro-behavior of the
                 system by ordinary differential equations while models
                 the micro- behavior of the system by natural fractals.
                 This system can be used to model and predict the
                 scientific observed time series, such as observed data
                 of sunspot and precipitation of flood season, and
                 always get good results.",
  notes =        "IJCNN 2002 Held in connection with the World Congress
                 on Computational Intelligence (WCCI 2002)",
}

@InCollection{kanok:1995:TGDDT,
  author =       "Mark Kanok",
  title =        "The Genetically Determined Dream Team",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "145--152",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{kantschik:1999:m-egGP,
  author =       "Wolfgang Kantschik and Peter Dittrich and Markus
                 Brameier and Wolfgang Banzhaf",
  title =        "Meta­Evolution in Graph {GP}",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "15--28",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Genome is a graph. Evolves genetic operators (also
                 represented as graphs) which act on the graphs.",
}

@InProceedings{kantschik:2001:EuroGP,
  author =       "Wolfgang Kantschik and Wolfgang Banzhaf",
  title =        "Linear-Tree {GP} and its comparison with other {GP}
                 structures",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "302--312",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Linear tree
                 structure, GP representation, Crossover",
  ISBN =         "3-540-41899-7",
  size =         "11 pages",
  abstract =     "In recent years different genetic programming (GP)
                 structures have emerged. Today, the basic forms of
                 representation for genetic programs are tree, linear
                 and graph structures. In this contribution we introduce
                 a new kind of GP structure which we call linear-tree.
                 We describe the linear-tree-structure, as well as
                 crossover and mutation for this new GP structure in
                 detail. We compare linear-tree programs with linear and
                 tree programs by analyzing their structure and results
                 on different test problems.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{kantschik:2002:EuroGP,
  title =        "Linear-Graph {GP}---{A} new {GP} Structure",
  author =       "Wolfgang Kantschik and Wolfgang Banzhaf",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "83--92",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "In recent years different genetic programming (GP)
                 structures have emerged. Today, the basic forms of
                 representation for genetic programs are tree, linear
                 and graph structures. In this contribution we introduce
                 a new kind of GP structure which we call linear-graph,
                 it is a further development of the linear-Tree
                 structure. We describe the linear-graph structure, as
                 well as crossover and mutation for this new GP
                 structure in detail. We compare linear-graph programs
                 with linear and tree programs by analyzing their
                 structure and results on different test problems.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InCollection{kapoor:1995:AVCGAJA,
  author =       "Sanjay Kapoor",
  title =        "A Variable Complexity Genetic Algorithm for Job
                 Allocation",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "153--160",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{karanta:1999:SWCPGA,
  author =       "Ilkka Karanta and Topi Mikkola and Catherine
                 Bounsaythip and Olli Jokinen and Juha Savola",
  title =        "Solving Wood Collection Problem using Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1787",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Kargupta:1997:kdpgeMGA,
  author =       "Hillol Kargupta and David E. Goldberg and Liwei Wang",
  title =        "Extending The Class of Order-k Delineable Problems For
                 The Gene Expression Messy Genetic Algorithm",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Algorithms",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@Unpublished{kargupta:1997:rlge,
  author =       "Hillol Kargupta",
  title =        "Relation learning in gene expression: Introns,
                 variable length representation, and all that",
  note =         "Position paper at the Workshop on Exploring Non-coding
                 Segments and Genetics-based Encodings at ICGA-97",
  month =        "21 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, introns",
  URL =          "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/hillol.ps",
  notes =        "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/",
  size =         "3 pages",
}

@InProceedings{kargupta:1999:FIGEAERC,
  author =       "Hillol Kargupta and Kakali Sarkar",
  title =        "Function Induction, Gene Expression, And Evolutionary
                 Representation Construction",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "313--320",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{kargupta:1999:F,
  author =       "Hillol Kargupta and B. H. Park",
  title =        "Fast construction of distributed and decomposed
                 evolutionary representation",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "139--148",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Walsh analysis",
  notes =        "GECCO-99LB",
}

@Article{Kargupta:2002:GPEM,
  author =       "Hillol Kargupta",
  title =        "Editorial: Computation in Gene Expression",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "2",
  pages =        "111--112",
  month =        jun,
  keywords =     "genetic algorithms",
  ISSN =         "1389-2576",
  notes =        "Special issue on Gene Expression",
}

@Article{Kargupta+ghosh:2002:GPEM,
  author =       "Hillol Kargupta and Samiran Ghosh",
  title =        "Toward Machine Learning Through Genetic Code-like
                 Transformations",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "3",
  pages =        "231--258",
  month =        sep,
  keywords =     "genetic algorithms, genetic code, gene expression,
                 representation construction, machine learning",
  ISSN =         "1389-2576",
  abstract =     "The gene expression process in nature involves several
                 representation transformations of the genome.
                 Translation is one among them; it constructs the amino
                 acid sequence in proteins from the nucleic acid-based
                 mRNA sequence. Translation is defined by a code book,
                 known as the universal genetic code. This paper
                 explores the role of genetic code and similar
                 representation transformations for enhancing the
                 performance of inductive machine learning algorithms.
                 It considers an abstract model of genetic code-like
                 transformations (GCTs) introduced elsewhere [21] and
                 develops the notion of randomised GCTs. It shows that
                 randomized GCTs can construct a representation of the
                 learning problem where the mean-square-error surface is
                 almost convex quadratic and therefore easier to
                 minimise. It considers the functionally complete
                 Fourier representation of Boolean functions to analyse
                 this effect of such representation transformations. It
                 offers experimental results to substantiate this claim.
                 It shows that a linear classifier like the Perceptron
                 [38] can learn non-linear XOR and DNF functions using a
                 gradient-descent algorithm in a representation
                 constructed by randomized GCTs. The paper also
                 discusses the immediate challenges that must be solved
                 before the proposed technique can be used as a viable
                 approach for representation construction in machine
                 learning.",
  notes =        "Article ID: 5091790",
}

@MastersThesis{Karlsson:mastersthesis,
  author =       "Rikard Karlsson",
  title =        "Sound localization for a humanoid robot by means of
                 Genetic Programming",
  school =       "Complex Systems Group, Chalmers University of
                 Technology",
  year =         "1998",
  address =      "S-41296, G{\"{o}}teborg, Sweden",
  month =        dec,
  email =        "nordin,tfemn@fy.chalmers.se",
  keywords =     "genetic algorithms, genetic programming, Elvis",
  size =         "pages",
  abstract =     "A linear GP system has been used to solve the problem
                 of sound localization for an autonomous humanoid robot,
                 with two microphones functioning as ears. To determine
                 the angle to a sound source a genetically evolved
                 program was used in a loop over a stereo sample stream,
                 where the genetic program gets the latest sample pair
                 plus feedback from the previous run as input. The
                 precision of the evolved genetic programs was largely
                 dependent on the experimental setup. When training on a
                 sawtooth wave from a fixed distance the smallest
                 standard deviation of the error was 8 degrees. After
                 letting the distance to the same sound source vary the
                 standard deviation of the error was 23 degrees. With a
                 human voice as sound source at varying distances the
                 standard deviation of the error was up to 41 degrees.",
}

@InProceedings{karlsson:2000:slhrGP,
  author =       "Rikard Karlsson and Peter Nordin and Mats Nordahl",
  title =        "Sound Localization for a Humanoid Robot Using Genetic
                 Programming",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and George D. Smith
                 and David Corne and Martin Oates and Emma Hart and Pier
                 Luca Lanzi and Egbert Jan Willem and Yun Li and Ben
                 Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "65--76",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, memory,
                 demes",
  ISBN =         "3-540-67353-9",
  notes =        "GP individual run iteratively, reading current inputs
                 and its previous outputs (saved in two memories) as
                 well as generating two real valued outputs. Training
                 data presented in as time series one sample (8 or
                 16bits at 16kHz). 40 samples.

                 Pop 40000 split into ten demes. Homologous crossover.
                 Ears important.

                 EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM,
                 EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April
                 17, 2000
                 Proceedings

                 http://evonet.dcs.napier.ac.uk/evoworkshops/

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67353-9",
}

@InProceedings{karr:1999:MAGCUGP,
  author =       "Charles L. Karr and Ken Borgelt",
  title =        "Modeling {A} Grinding Circuit Using Genetic
                 Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1785",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{karr:1999:SSNEVGA,
  author =       "Charles L. Karr and Barry Weck",
  title =        "Solutions to Systems of Nonlinear Equations Via
                 Genetic Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1786",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@MastersThesis{Karr:stage96,
  author =       "F. Karr",
  title =        "Programmation g\'en\'etique pour un probl\`eme de
                 contr\^ole, Interfa\c{c}age avec Maple",
  school =       "l'Ecole Polytechnique. Palaiseau",
  year =         "1996",
  month =        "Juin",
  key =          "Theses et Stage",
  editor =       "M. Schoenauer",
  email =        "Marc Schoenauer <marc@cmapx.polytechnique.fr>",
  howpublished = "Rapport de stage d'option de l'Ecole Polytechnique.
                 Palaise au",
  keywords =     "genetic algorithms, genetic programming, Maple",
  size =         "pages",
  notes =        "in French - English title would be: Genetic
                 Programming for optimal control. Interface with
                 Maple

                 ",
}

@InProceedings{kaschel:1999:GAADGJSSP,
  author =       "J. Kaschel and Gunnar Kobernik and Bernd Meier and
                 Tobias Teich",
  title =        "Genetic Algorithm, Avoiding of Deadlocks and
                 Gantt-Chart-Generation for the Job Shop Scheduling
                 Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "792",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{katagiri:2001:gecco,
  title =        "Network Structure Oriented Evolutionary Model --
                 Genetic Network Programming--and Its Comparison with",
  author =       "Hironobu Katagiri and Kotaro Hirasawa and Jinglu Hu
                 and Junichi Murata",
  pages =        "179",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster, GP,
                 Evolutionary Computation, Network Structure, Planning,
                 Tileworld",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{katagiri:2001:nsoemnpcgp,
  author =       "Hironobu Katagiri and Kotaro Hirasawa and Jinglu Hu
                 and Junichi Murata",
  title =        "Network Structure Oriented Evolutionary Model-Genetic
                 Network Programming-and its Comparison with Genetic
                 Programming",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "219--226",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, GNP,
                 tileworld",
  notes =        "GECCO-2001LB GNP form network structures. Judgement
                 node, processing node. (predefined number of nodes,
                 p221, data flow)",
}

@InProceedings{katagiri:2002:gecco,
  author =       "Hironobu Katagiri and Kotaro Hirasawa and Jinglu Hu
                 and Junichi Murata",
  title =        "A New Model To Realize Variable Size Genetic Network
                 Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "890",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, poster paper,
                 GNP, network structure, program size, Tileworld",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{katagiri:2002:gecco:lbp,
  title =        "A New Model to Realize Variable Size Genetic Network
                 Programming - {A} Case Study with the Tileworld
                 Problem",
  author =       "Hironobu Katagiri and Kotaro Hirasawa and Jinglu Hu
                 and Junichi Murata",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "279--286",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming, GNP",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

@InProceedings{katayama:1999:ILSAGTTSP,
  author =       "Kengo Katayama and Hiroyuki Narihisa",
  title =        "Iterated Local Search Approach using Genetic
                 Transformation to the Traveling Salesman Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "321--328",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Unpublished{katirai99,
  author =       "Hooman Katirai",
  title =        "Filtering Junk {E}-Mail: {A} Performance Comparison
                 between Genetic Programming and Naive Bayes",
  year =         "1999",
  month =        "10 " # sep,
  note =         "4A Year student project",
  URL =          "http://citeseer.nj.nec.com/katirai99filtering.html",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper describes the application of genetic
                 programming as a novel approach to the problem of
                 filtering junk e-mail. We benchmark our results against
                 the common standard: the Naïve Bayes classifier. While
                 the genetically programmed classifier demonstrated a
                 precision comparable to that of Naïve Bayes, it was
                 slightly outperformed in recall. Since both learning
                 methods gave similar results, it is recommended that a
                 larger study be undertaken to ascertain whether these
                 differences are indeed statistically significant.
                 Further it is recommended that the performance of these
                 classifiers be tested in a richer feature space more
                 typical of real-world classifiers. Although the
                 genetically programming classifier greatly outperformed
                 the Naïve Bayes classifier in speed, it is concluded
                 that a more efficient implementation of Naïve Bayes
                 needs to be used in order to provide a fair comparison.
                 We show that when left unabated, e-mail signatures also
                 known as taglines reduce the value of several important
                 features in junk e-mail detection; however it is also
                 shown that these e-mail signatures may be harvested as
                 advantageous features if some of their components are
                 removed and noted as a feature. We therefore recommend
                 that a better parser capable of meeting this criteria
                 be implemented. To aid the reader in the theoretical
                 aspects of our work, we have included introductory
                 background for both approaches, including a full
                 derivation of the generative Naïve Bayes model.",
  size =         "27 pages",
}

@InCollection{kato:1994:daoc,
  author =       "Saul Kato",
  title =        "A Discrete Artificial Organic Chemistry and Search for
                 Autocatalysis",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "54--63",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "Three diemnsional finite state cellular automata

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{Kazimierczak:1997:aehrKB,
  author =       "Jan Kazimierczak",
  title =        "An Approach to Evolvable Hardware representing the
                 Knowledge Base in an Automatic Programming System",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Evolvable Hardware",
  pages =        "492--497",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{keane:1993:firf,
  author =       "Martin A. Keane and John R. Koza and James P. Rice",
  title =        "Finding an impulse response function using genetic
                 programming",
  booktitle =    "Proceedings of the 1993 American Control Conference",
  year =         "1993",
  volume =       "III",
  pages =        "2345--2350",
  address =      "Evanston, IL, USA",
  organisation = "American Automatic Control Council",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "For many practical problems of control engineering, it
                 is desirable to find a function, such as the impulse
                 response function or transfer function, for a system
                 for which one does not have an analytical model. The
                 finding of the function, in symbolic form, that
                 satisfies the requirements of the problem (rather than
                 merely finding a single point) is usually not possible
                 when one does not have an analytical model of the
                 system. This paper illustrates how the recently
                 developed genetic programming paradigm, can be used to
                 find an approximation to the impulse response, in
                 symbolic form, for a linear time-invariant system using
                 only the observed response of the system to a
                 particular known forcing function. The method
                 illustrated can then be applied to other problems in
                 control engineering that require the finding of a
                 function in symbolic form.",
}

@InProceedings{Keane:2000:GECCO,
  author =       "Martin A. Keane and Jessen Yu and John R. Koza",
  title =        "Automatic Synthesis of Both Topology and Tuning of a
                 Common Parameterized Controller for Two Families of
                 Plants using Genetic Programming",
  pages =        "496--504",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  URL =          "http://www.genetic-programming.com/gecco2000paircontrollers.ps",
  abstract =     "This paper demonstrates that genetic programming can
                 be used to automatically create the design for both the
                 topology and parameter values (tuning) for a common
                 parameterized controller for all the plants in two
                 families of plants that are representative of typical
                 industrial processes. The genetically evolved
                 controller is {"}general{"} in the sense that it
                 contains free variables representing the
                 characteristics of the particular plant. The
                 genetically evolved controller outperforms the
                 controller designed with conventional techniques. In
                 addition, the genetically evolved controller infringes
                 on an early patented invention in the field of
                 control",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{keedwell:1999:UGAERFTNN,
  author =       "Edward Keedwell and Ajit Narayanan and Dragan Savic",
  title =        "Using Genetic Algorithms to Extract Rules From Trained
                 Neural Networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "793",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Unpublished{icga93-gp:keenan,
  author =       "Nick Keenan",
  title =        "Statistical Investigations of Genetic Algorithms and
                 Genetic Programming",
  note =         "Notes from Genetic Programming Workshop at ICGA-93

                 ",
  year =         "1993",
  pages =        "22",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/ICGA-93-GP-Abstracts.ps.Z",
  notes =        "{"}This analysis implies that there is a finite limit
                 to the effectiveness of genetic programming.{"}",
  size =         "1 pages",
}

@InCollection{keijzer:1996:aigp2,
  author =       "Maarten Keijzer",
  title =        "Efficiently Representing Populations in Genetic
                 Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "259--278",
  chapter =      "13",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "The chapter compares two representations for genetic
                 programming. One is the commonly used Lisp S-Expression
                 which uses the problem specific terminals and functions
                 defined before a run as an alphabet. The other is a
                 minimal Directed Acyclic Graph (DAG) that uses a
                 variable alphabet of complete subtrees. This chapter
                 will show that the DAG representation can replace
                 S-Expression representation without any change in the
                 functionality of a genetic programming system. In
                 certain situations the amount of memory needed to
                 represent a population can be reduced enormously when
                 using a DAG. The implementation of Automatically
                 Defined Functions (ADFs) in a DAG gives rise to the
                 definitions of a divergent ADF, and a compact ADF. The
                 latter can represent huge programs in S-Expression
                 format with a few elements.

                 ",
  size =         "21 pages",
}

@InProceedings{Keijzer:1997:idfas,
  author =       "Maarten Keijzer",
  title =        "Implicitly Defined Functions as an alternative to
                 {GP}-schemata",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "107--111",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{keijzer:1999:DAGP,
  author =       "Maarten Keijzer and Vladan Babovic",
  title =        "Dimensionally Aware Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1069--1076",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{keijzer:1999;GPhe,
  author =       "Maarten Keijzer",
  title =        "Genetic Programming in Hydraulic Engineering",
  booktitle =    "3rd DHI Software Conference \& DHI Software Courses",
  year =         "1999",
  address =      "Helsingør, Denmark",
  month =        "7-11 " # jun,
  organisation = "Danish Hydraulic Institute",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dhi.dk/softcon/abstract/105.doc",
  abstract =     "Genetic Programming (Koza 1993), is a general method
                 for the induction of computer programs by training.
                 Applications of genetic programming include but are not
                 limited by: symbolic regression, decision tree
                 induction, robot control, feature detection and system
                 identifictation. This paper will describe some of the
                 unique aspects of genetic programming in the field of
                 system identification and will give an example in
                 sediment transport",
  notes =        "http://www.dhi.dk/softcon/index.htm",
}

@Misc{keijzer:1999:SDGP,
  author =       "Maarten Keijzer",
  title =        "Scientific Discovery using Genetic Programming",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, data mining,
                 scientific discovery",
  URL =          "http://projects.dhi.dk/d2k/Publications/GPinSD.htm",
  abstract =     "One of the greatest challenges facing organisations
                 and individuals is how to turn their rapidly expanding
                 data stores into accessible, and actionable knowledge
                 (Fayyad et al, 1996). Knowledge Discovery in Databases
                 (KDD) is concerned with extracting such useful
                 information from data stores. We view data mining (DM)
                 as a step in this larger process called the KDD
                 process. In a DM step one can use genetic programming
                 (GP) (Koza, 1992; Babovic 1996).",
}

@InProceedings{keijzer:2000:GPbvt,
  author =       "Maarten Keijzer and Vladan Babovic",
  title =        "Genetic Programming, Ensemble Methods and the
                 Bias/Variance Tradeoff - Introductory Investigations",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "76--90",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{Keijzer:2000:GECCO,
  author =       "Maarten Keijzer and Vladan Babovic",
  title =        "Genetic Programming within a Framework of
                 Computer-Aided Discovery of Scientific Knowledge",
  pages =        "543--550",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{keijzer:2001:EuroGP,
  author =       "Maarten Keijzer and Conor Ryan and Michael O'Neill and
                 Mike Cattolico and Vladin Babovic",
  title =        "Ripple Crossover in Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "74--86",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Context Free Grammars, Crossover, Intrinsic
                 Polymorphism",
  ISBN =         "3-540-41899-7",
  size =         "13 pages",
  abstract =     "This paper isolates and identifies the effects of the
                 crossover operator used in Grammatical Evolution. This
                 crossover operator has already been shown to be adept
                 at combining useful building blocks and to outperform
                 engineered crossover operators such as Homologous
                 Crossover. This crossover operator, Ripple Crossover is
                 described in terms of Genetic Programming and applied
                 to two benchmark problems.

                 Its performance is compared with that of traditional
                 sub-tree crossover on populations employing the
                 standard functions and terminal set, but also against
                 populations of individuals that encode Context Free
                 Grammars. Ripple crossover is more effective in
                 exploring the search space of possible programs than
                 sub-tree crossover. This is shown by examining the rate
                 of premature convergence during the run. Ripple
                 crossover produces populations whose fitness increases
                 gradually over time, slower than, but to an eventual
                 higher level than that of sub-tree crossover.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{keijzer:2001:gecco,
  title =        "Adaptive Logic Programming",
  author =       "M. Keijzer and V. Babovic and C. Ryan and M. O'Neill
                 and M. Cattolico",
  pages =        "42--49",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, logic
                 programming, grammatical evolution, units of,
                 measurement, strong typing",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{keijzer:2002:EuroGP,
  title =        "Grammatical Evolution Rules: The mod and the Bucket
                 Rule",
  author =       "Maarten Keijzer and Michael O'Neill and Conor Ryan and
                 Mike Cattolico",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "123--130",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming, gramatical
                 evolution",
  ISBN =         "3-540-43378-3",
  abstract =     "We present an alternative mapping function called the
                 Bucket Rule, for Grammatical Evolution, that improves
                 upon the standard modulo rule. Grammatical Evolution is
                 applied to a set of standard Genetic Algorithm problem
                 domains using two alternative grammars. Applying GE to
                 GA problems allows us to focus on a simple grammar
                 whose effects are easily analysable.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@Article{keijzer:2002:GPEM,
  author =       "Maarten Keijzer and Vladan Babovic",
  title =        "Declarative and Preferential Bias in {GP}-based
                 Scientific Discovery",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "1",
  pages =        "41--79",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, symbolic
                 regression, strong typing, coercion typing, empirical
                 equations, hydraulics",
  ISSN =         "1389-2576",
  abstract =     "This work examines two methods for evolving
                 dimensionally correct equations on the basis of data.
                 It is demonstrated that the use of units of measurement
                 aids in evolving equations that are amenable to
                 interpretation by domain specialists. One method uses a
                 strong typing approach that implements a declarative
                 bias towards correct equations, the other method uses a
                 coercion mechanism in order to implement a preferential
                 bias towards the same objective. Four experiments using
                 real-world, unsolved scientific problems were performed
                 in order to examine the differences between the
                 approaches and to judge the worth of the induction
                 methods. Not only does the coercion approach perform
                 significantly better on two out of the four problems
                 when compared to the strongly typed approach, but it
                 also regularizes the expressions it induces, resulting
                 in a more reliable search process. A trade-off between
                 type correctness and ability to solve the problem is
                 identified. Due to the preferential bias implemented in
                 the coercion approach, this trade-off does not lead to
                 sub-optimal performance. No evidence is found that the
                 reduction of the search space achieved through
                 declarative bias helps in finding better solutions
                 faster. In fact, for the class of scientific discovery
                 problems the opposite seems to be the case.",
  notes =        "Article ID: 395989",
}

@InProceedings{keijzer:2002:gecco:workshop,
  title =        "An example of the use of context-sensitive constraints
                 in the {ALP} system",
  author =       "Maarten Keijzer and Mike Cattolico",
  pages =        "128--132",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InCollection{kinnear:keith,
  author =       "Mike J. Keith and Martin C. Martin",
  title =        "Genetic Programming in {C}++: Implementation Issues",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "285--310",
  chapter =      "13",
  keywords =     "genetic algorithms, genetic programming",
  size =         "25 pages",
  URL =          "http://www.frc.ri.cmu.edu/~mcm/chapt.html",
  notes =        "Contrasts 5 different genome interpreters (postfix,
                 prefix, Mixfix). Code based on C and C++. Population of
                 trees.

                 check later 1995 for postscript on GP ftp site",
}

@InProceedings{Kell:2000:GECCO,
  author =       "Richard J. Gilbert and Jem J. Rowland and Douglas B.
                 Kell",
  title =        "Genomic computing: explanatory modelling for
                 functional genomics",
  pages =        "551--557",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  abstract =     "Many newly discovered genes are of unknown function.
                 DNA microarrays are a method for determining the
                 expression levels of all genes in an organism for which
                 a complete genome sequence is available. By comparing
                 the expression changes under different conditions it
                 should be possible to assign functions to these genes.
                 However, many hundreds of thousands of data points may
                 be produced over a series of experiments. Genetic
                 programming provided simple explanatory rules for gene
                 function...",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO

                 DNA chip, microarray mRNA, baker's yeast saccharomyces
                 cerevisiae.

                 Protected divide by zero =10**15, range protection

                 6 way classification via 6 classifiers in one
                 individual. Linear GP. iteration (how?), infinite loops
                 trapped. five demes. 5% migration.

                 {"}new insights into biological systems at the genomic
                 level{"}. {"}not been previously reported{"}. Sec 4.",
}

@Article{kell:2002:BIW,
  author =       "Douglas Kell",
  title =        "Defence against the flood",
  journal =      "Bioinformatics World",
  year =         "2002",
  pages =        "16--18",
  month =        jan # "/" # feb,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.abergc.com/biwpp16-18_as_publ.pdf",
  size =         "3 pages",
  notes =        "high level",
}

@InProceedings{keller:1996:gpmlg2lp,
  author =       "Robert E. Keller and Wolfgang Banzhaf",
  title =        "Genetic Programming using Genotype-Phenotype Mapping
                 from Linear Genomes into Linear Phenotypes",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "116--122",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  abstract =     "

                 In common genetic programming approaches, the space of
                 genotypes, that is the search space, is identical to
                 the space of phenotypes, that is the solution space.
                 Facts and theories from molecular biology suggest the
                 introduction of non-identical genospaces and
                 phenospaces, and a generic genotype-phenotype mapping
                 which maps unconstrained genotypes into syntactically
                 correct phenotypes. Neutral variants come into effect
                 due to this mapping. They enhance genetic diversity and
                 allow for escaping local optima in phenospace via
                 high-dimensional saddle surfaces in genospace. We
                 propose a concrete mapping that maps linear binary
                 genotypes into linear phenotypes of an arbitrary
                 context-free programming language. Empirical results
                 are presented which show that the mapping improves the
                 performance of GP under mutation and reproduction.",
  notes =        "GP-96",
}

@InProceedings{keller:1996:gpmlg2lpVIDEO,
  author =       "Robert E. Keller and Wolfgang Banzhaf",
  title =        "Genetic Programming using Genotype-Phenotype Mapping
                 from Linear Genomes into Linear Phenotypes",
  booktitle =    "Genetic Programming 1996: Video Proceedings of the
                 First Annual Conference",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  address =      "Stanford University, CA, USA",
  publisher =    "Sound Photo Synthesis",
  URL =          "http://photosynthesis.com/space/gp96.html",
  size =         "15 mins",
  notes =        "GP-96. Presentation of keller:1996:gpmlg2lp

                 ",
}

@InProceedings{keller:1998:CADsr3pdGP,
  author =       "Robert E. Keller and Wolfgang Banzhaf and Klaus
                 Weinert and Jorn Mehnen",
  title =        "{CAD} Surface Reconstruction from Digitized 3{D} Point
                 Data with Genetic Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InCollection{keller:1999:aigp3,
  author =       "Robert E. Keller and Wolfgang Banzhaf and Jorn Mehnen
                 and Klaus Weinert",
  title =        "{CAD} Surface Reconstruction from Digitized 3{D} Point
                 Data with a Genetic Programming/Evolution Strategy
                 hybrid",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "3",
  pages =        "41--65",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{keller:1999:TEGCGP,
  author =       "Robert E. Keller and Wolfgang Banzhaf",
  title =        "The Evolution of Genetic Code in Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1077--1082",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{keller:2001:gecco,
  title =        "Evolution of Genetic Code on a Hard Problem",
  author =       "Robert E. Keller and Wolfgang Banzhaf",
  pages =        "50--56",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, genetic code,
                 real-world problem, noise filtering, developmental
                 genetic programming, genotype-phenotype mapping,
                 self-adaptation",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{keller:2002:bnaic,
  author =       "Robert E. Keller and Walter A. Kosters and Martijn
                 {van der Vaart} and Martijn D. J. Witsenburg",
  title =        "Genetic Programming Produces Strategies for Agents in
                 a Dynamic Environment",
  booktitle =    "Proceedings of the Fourteenth Belgium/Netherlands
                 Conference on Artificial Intelligence (BNAIC'02)",
  year =         "2002",
  editor =       "Hendrik Blockeel and Marc Denecker",
  pages =        "171--178",
  address =      "Leuven, Belgium",
  month =        "21-22 " # oct,
  organisation = "BNVKI, Dutch and the Belgian AI Association",
  keywords =     "genetic algorithms, genetic programming, DAI, MAS",
  URL =          "http://www.liacs.nl/home/kosters/gpas.ps",
  size =         "pages",
  notes =        "Katholieke Universiteit Leuven and Université Libre de
                 Bruxelles in collaboration with PharmaDM and under the
                 auspices of BNVKI/AIABN (the Belgian-Dutch Association
                 for Artificial Intelligence), SIKS (School for
                 Information and Knowledge Systems), and SNN (the
                 Foundation for Neural Networks).

                 Distributed Agents evolved to play 2 reduced versions
                 of chess {"}poor man's chess{"} and {"}pseudo
                 chess{"}.",
}

@InCollection{kennard:1995:UGADTHCS,
  author =       "D'ondria L. Kennard",
  title =        "Using Genetic Algorithm and Decision Trees to produce
                 a Hybrid Classification System",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "161--170",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{kennedy:1998:ehocl,
  author =       "Claire J. Kennedy",
  title =        "Evolutionary Higher-Order Concept Learning",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{kennedy:1999:ADCSSTEP,
  author =       "Claire J. Kennedy and Christophe Giraud-Carrier",
  title =        "A Depth Controlling Strategy for Strongly Typed
                 Evolutionary Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "879--885",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, evolution
                 strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  abstract =     "problems of bloat in GP, Strongly typed Evolutionary
                 Programming System STEPS, Escher programs, STGP, 6
                 types of mutation, editing.

                 Bench marks tennis (Tom Mitchell), michalski's train,
                 animal",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{kennedy:2001:fstugpsdrfmp,
  author =       "Claire J. Kennedy",
  title =        "First Steps Towards Using Genetic Programming to Solve
                 a Distributed Radio Frequency Management Problem",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "234--238",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, STEPS, STGP,
                 Escher program",
  notes =        "GECCO-2001LB",
}

@PhdThesis{kennedy:thesis,
  author =       "Paul Joseph Kennedy",
  title =        "Simulation of the Evolution of Single Celled Organisms
                 with Genome, Metabolism and Time-Varying Phenotype",
  school =       "University of Technology, Sydney",
  year =         "1999",
  address =      "Australia",
  month =        jul,
  keywords =     "genetic algorithms",
  URL =          "http://zahir.socs.uts.edu.au:9673/Paul/Papers/PhDThesis.ps.gz",
  URL =          "http://zahir.socs.uts.edu.au:9673/Paul/Papers/Thesis.zip",
  size =         "278 250 pages",
  abstract =     "A novel model of a biological cell is presented.
                 Primary features in the cell are a genome and
                 metabolism. Pairs of genome and metabolism coevolve
                 with a genetic algorithm (GA) to produce cells that can
                 survive in simple environments. Evolution of the genome
                 is Darwinian, whereas evolution of the metabolism has
                 Lamarckian features through acquired chemical
                 concentrations being inherited. Fitness is more closely
                 correlated with the mother cell than with the father. A
                 biologically inspired double-strand genome model is
                 presented. Double-stranded genomes admit a large
                 increase in the number of schemata represented by each
                 genome compared to single-strand encodings. This gives
                 GAs more information to use and allows faster search.
                 Simple implementation of a biologically inspired
                 algorithm for inversion also becomes possible, as well
                 as a compression of data on the genome. Increased rates
                 of inversion showed an increase in population
                 convergence. Double-stranded genomes impose constraints
                 between strands that decrease the overall rate of
                 population convergence. Four-bit bases from a parallel
                 genomic language are encoded on the genome. The
                 parallel genomic language, following the operon model
                 of Jacob and Monod, allows genes to be placed on the
                 genome at any loci and allows easy implementation of an
                 inversion operator. The genome and chemical metabolism
                 of a cell in our model have a close relationship.
                 Genomes specify allowable families of enzyme-catalysed
                 chemical reactions and families of chemicals that may
                 diffuse through the cell membrane at increased rate.
                 Chemicals produced from metabolic processes regulate
                 genes and allow expression of proteins from the genome.
                 We introduce the {"}bootstrapping{"} problem: evolution
                 of cells stable in simple environments from random
                 genomes and initial simple metabolic conditions.
                 Experiments show that solution of the
                 {"}bootstrapping{"} problem is much easier with
                 coevolution than when the initial metabolic conditions
                 remain fixed. A gallery of cellular survival strategies
                 is given. Genes in the population are diverse because
                 there is a variety of equally valid solutions to the
                 problem posed by the environment. Solution to the
                 {"}bootstrapping{"} problem is hindered because fitness
                 functions cannot differentiate between cells using
                 myopic solutions rather than long-term strategies.
                 Cells with myopic strategies attain high fitness but
                 produce offspring with high probability of cell death
                 (ie, when the myopic solution begins to fail). A novel
                 solution, where fitness of parents is retroactively
                 modified when the fitness of offspring becomes known,
                 reduces the number of cells exhibiting myopic
                 strategies.",
  notes =        "Fri, 15 Jun 2001 12:16:19 +1000 To:
                 genetic-programming@cs.stanford.edu

                 I applied some more biological notions to GAs in my PhD
                 thesis. In that, I built a model of single-celled
                 organisms and bred populations of them to live in
                 simple environments. The cell models had a
                 double-stranded (DNA inspired) genome and a
                 chemical-kinetic metabolism. Operons on the genome
                 encoded enzymes to control reactions in the metabolism
                 and the metabolism itself instantiated the simulated
                 enzyme molecules from the genome template.

                 Some of the complexities I added from biology were:

                 - operons (for a model of gene regulation)

                 - a gene expression algorithm (transcription and
                 translation algorithms)

                 - a double stranded genome (not diploidy)

                 - the inversion genetic operator

                 - a language that allows genes to appear at any locus
                 on the genome

                 - a phenotype that interacts with the genome for its
                 lifetime rather than just at the start.

                 The simulation was interesting but big and slow. It
                 generated so much information that it was a bit
                 difficult to work out how it was solving a problem.

                 PhD thesis is here:
                 http://zahir.socs.uts.edu.au:9673/Paul/research_html

                 Since then I've looked at abstracting the biological
                 concepts out of the big simulation into simpler models
                 (with no differential equations!). This work has
                 focused on the double-stranded genome and inversion
                 operator. I have a paper at GECCO about this
                 work.

                 Currently I'm interested in the (overly simplified)
                 idea that biological cells exist as systems in
                 isolation to their genome. (That's not to say that a
                 cell can exist without its genome). Without the genome
                 the cell is a sort of {"}default{"} system. As you add
                 genes to the genome you add enzymes to the system which
                 kicks the metabolism into different areas of (chemical)
                 reaction space. I see this kind of phenotype as a
                 {"}tempered{"} phenotype - tempered by genes rather
                 than completely specified. I'm looking forward to
                 discussing some of these ideas at the gene expression
                 workshop at GECCO next month.

                 Cheers, Paul.",
}

@InCollection{Kennelly:1997:Supersonic,
  author =       "Robert A. {Kennelly, Jr.}",
  title =        "Genetic Evolution of Shape-Altering Programs for
                 Supersonic Aerodynamics",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "100--109",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs ADFs, variable number of
                 iterations",
}

@InProceedings{Kennelly:1997:SupersonicLB,
  author =       "Robert A. {Kennelly, Jr.}",
  title =        "Genetic Evolution of Shape-Altering Programs for
                 Supersonic Aerodynamics",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "112--120",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@TechReport{DOCuGP:Kent,
  author =       "Simon Kent",
  title =        "Diagnosis of Oral Cancer using Genetic Programming",
  institution =  "Brunel University",
  year =         "1996",
  number =       "CSTR-96-14 ; CNES-96-04",
  address =      "Uxbridge, Middlesex, UB8 3PH, UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming,
                 classification",
  size =         "20 pages",
}

@TechReport{BSPoGPTR:Kent,
  author =       "Simon Kent and Dimitris Dracopoulos",
  title =        "Bulk Synchronous Parallelisation of Genetic
                 Programming",
  institution =  "Brunel University",
  year =         "1996",
  number =       "CSTR-96-13 ; CNES-96-02",
  address =      "Uxbridge, Middlesex, UB8 3PH, UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, parallel
                 computing",
  notes =        "as BSPoGP:Kent",
}

@InProceedings{BSPoGP:Kent,
  author =       "Dimitris C. Dracopoulos and Simon Kent",
  title =        "Bulk Synchronous Parallelisation of Genetic
                 Programming",
  booktitle =    "Applied parallel computing : industrial strength
                 computation and optimization ; Proceedings of the third
                 International Workshop, PARA '96",
  year =         "1996",
  editor =       "Jerzy Wa\'{s}niewski",
  pages =        "216--226",
  address =      "Berlin, Germany",
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming, parallel
                 computing",
  notes =        "cf BSPoGPTR:Kent",
}

@Article{Kent:GPfPaC,
  author =       "D. C. Dracopoulos and Simon Kent",
  title =        "Genetic Programming for Prediction and Control",
  journal =      "Neural Computing and Applications",
  year =         "1997",
  volume =       "6",
  number =       "4",
  pages =        "214--228",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{kerr:1998:pGAeVLSI,
  author =       "Kevin Kerr",
  title =        "A Parallel Genetic Algorithm to Evolve {VLSI}
                 Circuits",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms",
  notes =        "GP-98LB",
}

@InProceedings{kessler:1998:a2xGP,
  author =       "Matthew W. Kessler",
  title =        "Avoiding Two-Bit Crossovers in Genetic Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "115--119",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.twsu.edu/~haynes/twobit.ps",
  size =         "5 pages",
  notes =        "GP-98LB url refers to joint paper with Thomas D.
                 Haynes",
}

@InProceedings{Kessler:1999:ATC,
  author =       "Matthew Kessler and Thomas Haynes",
  title =        "Avoiding Two-bit Crossovers in Genetic Programming",
  booktitle =    "Proceedings of the 1999 ACM Symposium on Applied
                 Computing",
  year =         "1999",
  editor =       "Janice Carroll and Hisham Haddad and Dave Oppenheim
                 and Barrett Bryant and Gary B. Lamont",
  pages =        "319--323",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://adept.cs.twsu.edu/~thomas/sac99tb.ps",
  abstract =     "We use collective memory to integrate weak and strong
                 search heuristics to find cliques in FC, a family of
                 graphs. We construct FC such that pruning partial
                 solutions will be ineffective. Each weak heuristic
                 maintains a local cache of the collective memory. We
                 examine the impact on the distributed search of the
                 distribution of the collective memory, the search
                 algorithms, and our family of graphs. We find the
                 distributed search performs better than the individual
                 searches, even though the space of partial solutions is
                 combinatorial.",
  notes =        "(GA track)",
}

@InProceedings{key:1999:ENAEPC,
  author =       "Cathy Key",
  title =        "(formerly {ES}-212) Non-reciprocal Altruism and the
                 Evolution of Paternal Care",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1313--1320",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{keymeulen:1998:olmblEHWrts,
  author =       "Didier Keymeulen and Masaya Iwata and Yasuo Kuniyoshi
                 and Tetsuya Higuchi",
  title =        "On-line Model-based Learning using Evolvable Hardware
                 for a Robotics Tracking System",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "816--823",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Evolutionary Robotics",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Proceedings{keymeulen:2001:eh,
  title =        "The Third {NASA}/Do{D} workshop on Evolvable
                 Hardware",
  year =         "2001",
  editor =       "Didier Keymeulen and Adrian Stoica and Jason Lohn and
                 Ricardo S. Zebulum",
  address =      "Long Beach, California",
  publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC,
                 20036-1992, USA",
  month =        "12-14 " # jul,
  organisation = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, evolvable hardware",
  ISBN =         "0-7695-1180-5",
  URL =          "EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/",
  size =         "287 pages",
}

@InCollection{Khopkar:1997:agp,
  author =       "Chirag D. Khopkar",
  title =        "Solving the Art Gallery Problem via Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "110--119",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{kim:1996:emclpDGA,
  author =       "Dae Wook Kim and Sang Kyoon Kim and Hang Joon Kim",
  title =        "An Extraction Method of a Car License Plate using a
                 Distributed Genetic Algorithm",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "500",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96 GA paper",
}

@Proceedings{cec:2001,
  title =        "Proceedings of the 2001 Congress on Evolutionary
                 Computation {CEC2001}",
  year =         "2001",
  key =          "Jong-Wan Kim",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "biological modeling/ breast cancer, biological
                 modelling, classifiers, coevolution, constraint
                 handling, control system design, controlling search,
                 design applications, devices developement and
                 applications, dynamic and parallel ec, ec techniques,
                 ecological modelling and information ecosystems,
                 engineering applications, evolutionary markets,
                 evolutionary scheduling, evolvable hardware, evolving
                 neural networks, fitness, games and game like tasks,
                 genetic algorithms, genetic programming, hybrid
                 systems, image processing applications, image/ signal
                 processing, intelligent agents, learning and search
                 spaces, local search optimization, medical
                 applications, multi-agent systems and cultural
                 algorithms, multi-objective optimization, network
                 applications, new paradigms, novel applications, novel
                 themes, operations research applications,
                 representations, revisiting the fossil record, robotic
                 applications, stroganoff, system modeling and control,
                 theory and foundations, time series",
  ISBN =         "0-7803-6658-1",
  size =         "1441 pages",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@InCollection{psKim:1997:nim,
  author =       "Peter S. Kim",
  title =        "Evolution of a State-Evaluation Function for the Game
                 of Nim via Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "120--127",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{kim:1999:ESSGPTSP,
  author =       "Jung-Jib Kim and Byoung-Tak Zhang",
  title =        "Effects of Selection Schemes in Genetic Programming
                 for Time Series Prediction",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "252--258",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{kim:1999:N,
  author =       "Jungwon Kim and Peter Bentley",
  title =        "Negative selection and niching by an artificial immune
                 system for network intrusion detection",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "149--158",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{ieee94:kinnear,
  author =       "Kenneth E. {Kinnear, Jr.}",
  title =        "Fitness Landscapes and Difficulty in Genetic
                 Programming",
  year =         "1994",
  booktitle =    "Proceedings of the 1994 IEEE World Conference on
                 Computational Intelligence",
  publisher =    "IEEE Press",
  volume =       "1",
  pages =        "142--147",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  size =         "6 pages",
  keywords =     "genetic algorithms, genetic programming, algorithm
                 theory, search problems, learning (artificial
                 intelligence), fitness landscapes, landscape measures,
                 autocorrelation, random walks, landscape basin depths,
                 adaptive walks",
  URL =          "http://ieeexplore.ieee.org/iel2/1125/8059/00350026.pdf?isNumber=8059",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/kinnear.wcci.ps.Z",
  ISBN =         "0-7803-1899-4",
  abstract =     "The structure of the fitness landscape on which
                 genetic programming operates is examined. The
                 landscapes of a range of problems of known difficulty
                 are analyzed in an attempt to determine which landscape
                 measures correlate with the difficulty of the problem.
                 The autocorrelation of the fitness values of random
                 walks, a measure which has been shown to be related to
                 perceived difficulty using other techniques, is only a
                 weak indicator of the difficulty as perceived by
                 genetic programming. All of these problems show
                 unusually low autocorrelation. Comparison of the range
                 of landscape basin depths at the end of adaptive walks
                 on the landscapes shows good correlation with problem
                 difficulty, over the entire range of problems
                 examined.",
  notes =        "Defines difficulty as number of fitness cases/1000.
                 Considers a few parity and sort problems. Fitness
                 landscape investigated by using GP operators (without
                 selection) on gen=0 to give a number of random walks.
                 Look at autocorrelation of fitness along these walks.
                 Essentially none (<0.5) very much worse than published
                 GA. Also little correlation between this and difficulty
                 measure.

                 ",
}

@InProceedings{icga93:kinnear,
  author =       "Kenneth E. {Kinnear, Jr.}",
  title =        "Generality and Difficulty in Genetic Programming:
                 Evolving a Sort",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  pages =        "287--294",
  month =        "17-21 " # jul,
  address =      "University of Illinois at Urbana-Champaign",
  keywords =     "genetic algorithms, genetic programming",
  size =         "8 pages",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/kinnear.icga93.ps.Z",
  abstract =     "application of GP to evolving sorting algorithms and
                 the lessons learned from this. Plus the discovery of a
                 connection between size and generality.",
  notes =        "Adding inverse prog size decreases size of progs and
                 makes them more general.

                 Ref Bickel ICGA-2 Tree structured rules in GAs Kinnear
                 IEEE Press, ICNN U M O'Reilly and F. Oppacher {"}An
                 experimental Perspective on Genetic Programming{"} in
                 {"}Parallel Problem solving from nature{"} R.Manner and
                 B. Manderick (eds) Holland:Elsevier.

                 Loads of references on Steady State v generational
                 (also see kim's own reference)

                 Various fiddling to find balance of size parameters.
                 Various terminal/function sets tried (less powerful ->
                 more difficult) Smaller successful programs more
                 general.",
}

@InCollection{kinnear:kinnear,
  author =       "Kenneth E. {Kinnear, Jr.}",
  institution =  "Adaptive Computing Technology",
  title =        "Alternatives in Automatic Function Definition: {A}
                 Comparison Of Performance",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "119--141",
  chapter =      "6",
  keywords =     "genetic algorithms, genetic programming, Hoist
                 (shrink) mutation, ADF, MA, GLib",
  size =         "22 pages",
}

@InProceedings{icnn93:kinnear,
  author =       "Kenneth E. {Kinnear, Jr.}",
  title =        "Evolving a Sort: Lessons in Genetic Programming",
  booktitle =    "Proceedings of the 1993 International Conference on
                 Neural Networks",
  year =         "1993",
  volume =       "2",
  pages =        "881--888",
  address =      "San Francisco, USA",
  publisher_address = "Piscataway, NJ, USA",
  month =        "28 " # mar # "-1 " # apr,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, iterative
                 sorting algorithms, steady state genetic algorithm,
                 genetic operator, nonfitness single cross-over,
                 iterative methods",
  ISBN =         "0-7803-0999-5",
  size =         "8 Pages",
  abstract =     "In applying the genetic programming paradigm to the
                 task of evolving iterative sorting algorithms, a
                 variety of lessons are learned. With proper selection
                 of the primitives, sorting algorithms are evolved that
                 are both general and non-trivial. The sorting problem
                 is used as a testbed to evaluate the value of several
                 alternative parameters, with some small gains shown.
                 The value of applying steady state genetic algorithm
                 techniques to genetic programming, called steady state
                 genetic programming, is demonstrated. One unusual
                 genetic operator is created, i.e., nonfitness single
                 cross-over. It shows promise in at least this
                 environment.",
  URL =          "http://ieeexplore.ieee.org/iel3/1059/7404/00298674.pdf?isNumber=7404",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/kinnear.icnn93.ps.Z",
  notes =        "dobl (INDEX), SSGP, hoist, mutation, create,
                 non-fitness single crossover, permuation, inversion",
}

@InCollection{kinnear:intro,
  author =       "Kenneth E. {Kinnear, Jr.}",
  title =        "A perspective on the Work in this Book",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "1",
  pages =        "3--19",
  size =         "17 pages",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "A whole load of good advice to try when your GP don't
                 work",
}

@Book{kinnear:book,
  editor =       "Kenneth E. {Kinnear, Jr.}",
  title =        "Advances in Genetic Programming",
  publisher =    "MIT Press",
  year =         "1994",
  address =      "Cambridge, MA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/aigp.html",
  url2 =         "http://mitpress.mit.edu/book-home.tcl?isbn=0262111888",
  notes =        "Hardback 24 chapters, most have entries in this
                 bibliography",
  size =         "525 pages",
}

@InProceedings{kirkam:1997:dsftrr,
  author =       "I. M. A. Kirkwood and S. H. Shami and M. C. Sinclair",
  title =        "Discovering Simple Fault-Tolerant Routing Rules using
                 Genetic Programming",
  booktitle =    "ICANNGA97",
  year =         "1997",
  address =      "University of East Anglia, Norwich, UK",
  email =        "mcs@essex.ac.uk",
  keywords =     "genetic algorithms, genetic
                 programming,telecommunication networks, routing",
  abstract =     "A novel approach to solving network routing and
                 restoration problems using the genetic programming (GP)
                 paradigm is presented, in which a single robust and
                 fault-tolerant program is evolved which determines the
                 near-shortest paths through a network subject to link
                 failures. The approach is then applied to five
                 different test networks. In addition, two
                 multi-population GP techniques are tried and the
                 results compared to simple GP.",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}

@InProceedings{Kirshenbaum:2000:GECCO,
  author =       "Evan Kirshenbaum",
  title =        "Genetic Programming with Statically Scoped Local
                 Variables",
  pages =        "459--468",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{pkdd97*134,
  author =       "Mikhail V. Kiselev and Sergei M. Ananyan and Sergei B.
                 Arseniev",
  title =        "Regression-Based Classification Methods and Their
                 Comparison with Decision Tree Algorithms",
  booktitle =    "Proceedings of the 1st European Symposium on
                 Principles of Data Mining and Knowledge Discovery",
  year =         "1997",
  editor =       "Jan Komorowski and Jan Zytkow",
  volume =       "1263",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "134--144",
  publisher_address = "Berlin",
  month =        "24--27 " # jun,
  publisher =    "Springer-Verlag",
  ISBN =         "3-540-63223-9",
  notes =        "PolyAnalyst",
}

@InProceedings{Kiselev:1998:PDA,
  author =       "Mikhail V. Kiselev and Sergei M. Ananyan and Sergei B.
                 Arseniev",
  title =        "{PolyAnalyst} Data Analysis Technique and Its
                 Specialization for Processing Data Organized as a Set
                 of Attribute Values",
  booktitle =    "Proceedings of the 2nd European Symposium on
                 Principles of Data Mining and Knowledge Discovery
                 ({PKDD}-98)",
  year =         "1998",
  editor =       "Jan M. {\.{Z}}ytkow and Mohamed Quafafou",
  volume =       "1510",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "352--360",
  publisher_address = "Berlin",
  month =        "23-26 " # sep,
  publisher =    "Springer-Verlag",
  ISBN =         "3-540-65068-7",
  notes =        "PolyAnalyst uses a strongly typed functional language
                 with the expressive power of a universal programming
                 language as its respresentation. It searches this with
                 a combination of enumeration and beam search.
                 http://www.megaputer.com/html/skat.html

                 More Information at:
                 http://www.primenet.com/pcai/New_Home_Page/issues/pcai_13_issue_details.html#Data_Mining
                 Theme: Data Mining/Genetic Algorithms - Vol 13 Issue 5
                 (Sept/Oct 1999) Available Aug 25 Future Issue Featuring
                 Articles: AI @ Work Editorial Secret Agent Man The
                 Intelligence File The Book Zone Buyers Guide:Data
                 Mining, Genetic Algorithms, Modeling, Training,
                 Consultingl

                 http://www.nautilus-systems.com/datamine/msg00793.html
                 From: Sergei Ananyan Date: Thu, 21 Jan 1999 11:40:44
                 -0500 (EST) PC AI Magazine, January issue, page 48",
}

@Article{kishore:2000:mpc,
  author =       "J. K. Kishore and L. M. Patnaik and V. Mani and V. K.
                 Agrawal",
  title =        "Application of genetic programming for multicategory
                 pattern classification",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2000",
  volume =       "4",
  number =       "3",
  pages =        "242--258",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, pattern
                 classification, multicategory pattern classification,
                 GP, distribution-free methods, statistical
                 distribution, two-category classification, discriminant
                 function, association strength measure, SA measure,
                 heuristic rules, training sets, incremental learning,
                 function set choice, conflict resolution",
  ISSN =         "1089-778X",
  URL =          "http://ieeexplore.ieee.org/iel5/4235/18897/00873237.pdf",
  size =         "17 pages",
  abstract =     "Explores the feasibility of applying genetic
                 programming (GP) to multicategory pattern
                 classification problem. GP can discover relationships
                 and express them mathematically. GP-based techniques
                 have an advantage over statistical methods because they
                 are distribution-free, i.e., no prior knowledge is
                 needed about the statistical distribution of the data.
                 GP also automatically discovers the discriminant
                 features for a class. GP has been applied for
                 two-category classification. A methodology for GP-based
                 n-class classification is developed. The problem is
                 modeled as n two-class problems, and a genetic
                 programming classifier expression (GPCE) is evolved as
                 a discriminant function for each class. The GPCE is
                 trained to recognize samples belonging to its own class
                 and reject others. A strength of association (SA)
                 measure is computed for each GPCE to indicate the
                 degree to which it can recognize samples of its own
                 class. SA is used for uniquely assigning a class to an
                 input feature vector. Heuristic rules are used to
                 prevent a GPCE with a higher SA from swamping one with
                 a lower SA. Experimental results are presented to
                 demonstrate the applicability of GP for multicategory
                 classification, and they are found to be satisfactory.
                 We also discuss the various issues that arise in our
                 approach to GP-based classification, such as the
                 creation of training sets, the role of incremental
                 learning, and the choice of function set in the
                 evolution of GPCE, as well as conflict resolution for
                 uniquely assigning a class.",
}

@Article{Kishore:2001:ISJ,
  author =       "J. K. Kishore and L. M. Patnaik and V. Mani and V. K.
                 Agrawal",
  title =        "Genetic programming based pattern classification with
                 feature space partitioning",
  journal =      "Information Sciences",
  year =         "2001",
  volume =       "131",
  number =       "1-4",
  pages =        "65--86",
  month =        jan,
  email =        "lalit@micro.iisc.ernet.in",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0020-0255",
  size =         "22 pages",
  abstract =     "Genetic programming (GP) is an evolutionary technique
                 and is gaining attention for its ability to learn the
                 underlying data relationships and express them in a
                 mathematical manner. Although GP uses the same
                 principles as genetic algorithms, it is a symbolic
                 approach to program induction; i.e., it involves the
                 discovery of a highly fit computer program from the
                 space of computer programs that produces a desired
                 output when presented with a particular input. We have
                 successfully applied the GP paradigm for the
                 <i>n</i>-category pattern classification problem. The
                 ability of the GP classifier to learn the data
                 distributions depends upon the number of classes and
                 the spatial spread of data. As the number of classes
                 increases, it increases the difficulty for the GP
                 classifier to resolve between classes. So, there is a
                 need to partition the feature space and identify
                 sub-spaces with reduced number of classes. The basic
                 objective is to divide the feature space into
                 sub-spaces and hence the data set that contains
                 representative samples of n classes into sub-data sets
                 corresponding to the sub-spaces of the feature space,
                 so that some of the sub-data sets/spaces can have data
                 belonging to only p-classes (p<n). The GP classifier is
                 then evolved independently for the sub-data sets/spaces
                 of the feature space. The GP classifier becomes simpler
                 for some of the sub-data sets/spaces as only p classes
                 are present. It also results in localized learning as
                 the GP classifier has to learn the data distribution in
                 only a sub-space of the feature space rather than in
                 the entire feature space. In this paper, we are
                 integrating the GP classifier with feature space
                 partitioning (FSP) for localized learning to improve
                 pattern classification.",
  notes =        "Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt
                 GPQUICK",
}

@InProceedings{kisungseo:2001:gecco,
  title =        "First Steps toward Automated Design of Mechatronic
                 Systems Using Bond Graphs and Genetic Programming",
  author =       "Kisung Seo and Erik D. Goodman and Ronald C.
                 Rosenberg",
  pages =        "189",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 genetic programming; bond graphs; dynamic systems
                 design; mechatronic, systems design",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{klahold:1998:eprGPJb,
  author =       "Stefan Klahold and Steffen Frank and Robert E. Keller
                 and Wolfgang Banzhaf",
  title =        "Exploring the Possibilites and Restrictions of Genetic
                 Programming in {Java} Bytecode",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{klassen:2002:IJCNN,
  author =       " Tim J. Klassen and Malcolm I. Heywood",
  title =        "Towards the On-line Recognition of Arabic Characters",
  booktitle =    "Proceedings of the 2002 International Joint Conference
                 on Neural Networks IJCNN'02",
  pages =        "1900--1905",
  year =         "2002",
  month =        "12-17 " # may,
  address =      "Hilton Hawaiian Village Hotel, Honolulu, Hawaii",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE",
  ISBN =         "0-7803-7278-6",
  keywords =     "genetic algorithms, genetic programming, Arabic
                 cursive handwriting, on-line character recognition,
                 SOM, perceptron, MLP",
  abstract =     "A generic system is proposed for the recognition of
                 on-line handwritten Arabic characters. Automatic
                 extraction of features from on-line data using SOMs
                 avoids heuristic extraction of features. Performance of
                 a perceptron classifier is competitive with MLP and
                 Genetic Programming based approaches and a better fit
                 for handheld computing devices.",
  notes =        "IJCNN 2002 Held in connection with the World Congress
                 on Computational Intelligence (WCCI 2002)",
}

@InCollection{klausner:1994:tad,
  author =       "Mark Klausner",
  title =        "Taking Advantage of Diversity in Genetic Algorithms",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "64--72",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{klyce:1999:IIEPCSP,
  author =       "Brig Klyce",
  title =        "In real or artificial life, Is Evolutionary Progress
                 in a Closed System Possible?",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1444",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{knowles:1999:ANEADCMSTP,
  author =       "Joshua Knowles and David Corne and Martin Oates",
  title =        "A New Evolutionary Approach to the Degree Constrained
                 Minimum Spanning Tree Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "794",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{kochenderfer:2002:ETPBSIGP,
  author =       "Mykel J. Kochenderfer",
  title =        "Evolving Teleo-Reactive Programs for Block Stacking
                 using Indexicals through Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "111--118",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming, stgp",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{koehler:1999:CSGEWT,
  author =       "Gary J. Koehler",
  title =        "Computing Simple {GA} Expected Waiting Times",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "795",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{koeppen:2001:tdgp,
  author =       "Mario Koeppen and Xiufen Liu",
  title =        "Texture Detection by Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "867--872",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, texture
                 analysis, texture detection, evolutionary algorithms,
                 2D-Lookup",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@Article{Kojima:2001:MPT,
  author =       "Fumio Kojima and Naoyuki Kubota and Setsuo Hashimoto",
  title =        "Identification of crack profiles using genetic
                 programming and fuzzy inference",
  journal =      "Journal of Materials Processing Technology",
  volume =       "108",
  pages =        "263--267",
  year =         "2001",
  number =       "2",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6TGJ-41WSC52-10/1/dab41d59bda7ac05c11a7560f11f27ba",
  abstract =     "This paper deals with a quantitative nondestructive
                 evaluation in eddy current testing for steam generator
                 tubes of nuclear power plants by using genetic
                 programming (GP) and fuzzy inference system. Defects
                 can be detected as a probe impedance trajectory by
                 scanning a pancake type probe coil. An inference system
                 is proposed for identifying the defect shape inside
                 and/or outside tubes. GP is applied to extract and
                 select effective features from a probe impedance
                 trajectory. Using the extracted features, a fuzzy
                 inference system detects presence, position, and size
                 of a defect of test sample. The effectiveness of the
                 proposed method is demonstrated through computer
                 simulation studies.",
}

@InProceedings{kokai1998,
  author =       "Gabriella K\'okai and Zolt\'an T\'oth and Robert
                 V\'anyi",
  title =        "Application of Genetic Algorithms with more
                 Populations for {L}indenmayer Systems",
  booktitle =    "Proceedings of the International Symposium on
                 Engineering of Intelligent Systems, EIS'98",
  year =         "1998",
  editor =       "E. Alpaydin and Colin Fyfe",
  pages =        "324--331",
  keywords =     "genetic algorithms, genetic programming, lindenmayer
                 L-systems",
}

@InProceedings{kokai1999-1,
  author =       "Gabriella K\'okai and Zolt\'an T\'oth and Robert
                 V\'anyi",
  title =        "Evolving Artificial Trees Described by Parametric
                 {L}-Systems",
  booktitle =    "Proceedings of the First Canadian Workshop on Soft
                 Computing",
  year =         "1999",
  pages =        "1722--1728",
  address =      "Edmonton, Alberta, Canada",
  month =        "9 " # may,
  keywords =     "genetic algorithms, genetic programming, lindenmayer
                 L-systems",
}

@InProceedings{kokai1999-2,
  author =       "Gabriella K\'okai and Zolt\'an T\'oth and Robert
                 V\'anyi",
  title =        "Modelling Blood Vessel of the Eye with Parametric
                 {L}-Systems Using Evolutionary Algorithms",
  booktitle =    "Artificial Intelligence in Medicine, Proceedings of
                 the Joint European Conference on Artificial
                 Intelligence in Medicine and Medical Decision Making,
                 AIMDM'99",
  year =         "1999",
  editor =       "W. Horn and Y. Shahar and G. Lindberg and S.
                 Andreassen and J. Wyatt",
  volume =       "1620",
  series =       "Lecture Notes of Computer Science",
  pages =        "433--443",
  address =      "Aalborg, Denmark",
  month =        "20-24 " # jun,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, lindenmayer
                 L-systems, Computer vision, image and signal
                 interpretation",
  ISBN =         "3-540-66162-X",
  URL =          "http://link.springer.de/link/service/series/0558/papers/1620/16200433.pdf",
  abstract =     "In this paper the GREDEA system is presented. The main
                 idea behind it is that with the help of evolutionary
                 algorithms a grammatical description of the blood
                 circulation of the human retina can be inferred. The
                 system uses parametric Lindenmayer systems as
                 description language. It can be applied on patients
                 with diabetes who need to be monitored over long
                 periods.",
}

@InProceedings{kokai:1999:GEAPL,
  author =       "Gabriella Kokai and Zoltan Toth and Robert Vanyi",
  title =        "Generic Evolution Algorithms Programming Library",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1867",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, methodology,
                 pedagogy and philosophy, poster paper, lindenmayer
                 L-systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{kokai:1999:PLDRCEO,
  author =       "Gabriella Kokai and Robert Vanyi and Zoltan Toth",
  title =        "Parametric {L}-System Description of the Retina with
                 Combined Evolutionary Operators",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1588--1595",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, evolution strategies, lindenmayer
                 systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{kokai:2002:gecco:lbp,
  title =        "An Experimental Comparison of Genetic and Classical
                 Concept Learning Methods",
  author =       "Gabriella K{\'o}kai and Zolt{\'a}n T{\'o}th and
                 Szilvia Zvada",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "287--294",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 GeLog = GA + Inductive Logic Programming. Prolog. Foil.
                 C4.5. GEA.",
}

@InProceedings{Konstam:1998:gcGPGA,
  author =       "Aaron Konstam",
  title =        "Group Classification Using a Mix of Genetic
                 Programming and Genetic Algorithms",
  booktitle =    "1998 ACM Symposium on Applied Computing",
  year =         "1998",
  editor =       "K. M. George",
  address =      "Marriott Marquis, Atlanta, Georgia, U.S.A.",
  month =        "27 " # feb # "-1 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cogsci.ed.ac.uk/~ceilidh/SAC-Papers/Paper34/index.html",
  notes =        "SAC'98",
}

@InProceedings{kopp:1999:SDSS,
  author =       "Stephan Kopp and Henning S. Mortveit and Christian M.
                 Reidys",
  title =        "Sequential Dynamical Systems and Simulation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1445",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{koppen:1996:dixaGP,
  author =       "M. Koppen and B. Nickolay",
  title =        "Design of Image Exploring Agent using Genetic
                 Programming",
  booktitle =    "Proc. IIZUKA'96",
  year =         "1996",
  pages =        "549--552",
  address =      "Iizuka, Japan",
  keywords =     "genetic algorithms, genetic programming",
  size =         "4 pages",
}

@Article{koppen:1997:dixaGP,
  author =       "Mario Koppen and Bertram Nickolay",
  title =        "Design of Image Exploring Agent using Genetic
                 Programming",
  journal =      "Fuzzy Sets and Systems",
  year =         "1999",
  volume =       "2",
  number =       "103",
  pages =        "303--315",
  note =         "Special Issue on Softcomputing",
  keywords =     "genetic algorithms, genetic programming Pattern
                 recognition; Image processing, Agent design, Image
                 understanding, Decision making",
  URL =          "http://www.elsevier.com/locate/fss",
  abstract =     "This paper presents a new methodology for the design
                 of image processing algorithms. The presented approach
                 is based on the conception and design of {"}image
                 exploring agents{"}. Image exploring agents iteratively
                 run a sense-compute-act loop. While all loop processing
                 is performed within the image on a local base, the
                 trace of the agent, which results from the repetition
                 of this loop, is a global image property. Hence, a
                 given global recognition task is represented by a
                 corresponding task of finding a suitable, local-based
                 computation. The exploitation of genetic programming
                 for finding suitable computational parts of the agent
                 is considered. A case study is included, in which a
                 crack detector is designed and improved by means of an
                 image exploring agent. The functionality of this agent
                 is studied, and it is used for designing an image
                 processing algorithm for crack detection. Also, further
                 improvements of the presented agent concept are given
                 in this paper. © 1999 Elsevier Science B.V. All rights
                 reserved.",
}

@InProceedings{kordon:2001:gecco,
  title =        "Soft Sensor Development Using Genetic Programming",
  author =       "Arthur K. Kordon and Guido F. Smits",
  pages =        "1346--1351",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "real world applications, genetic programming, soft
                 sensors, hybrid intelligent, systems, empirical
                 modeling",
  ISBN =         "1-55860-774-9",
  abstract =     "Dow Chemical Company",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO

                 Preliminary sensitivity analysis based on stacked
                 artificial neural networks (MATLAB).

                 ANN, Support vector machines (SVM), GP. Vacuum
                 distillation column, continuous stirred tank reactor,
                 twin screw extruder. Hybrid intelligent system
                 methodology {"}The GP-generated soft sensor was
                 implemented in Gensym G2{"}... {"}The system is in
                 operation since November 1997 and initially it included
                 a soft sensor for one reactor.{"} p1350.",
}

@InProceedings{kordon:2002:gecco,
  author =       "Arthur Kordon and Hoang Pham and Clive Bosnyak and
                 Mark Kotanchek and Guido Smits",
  title =        "Accelerating Industrial Fundamental Model Building
                 with Symbolic Regression: {A} Case Study with
                 Structure-Property Relationships",
  booktitle =    "GECCO-2002 Presentations in the Evolutionary
                 Computation in Industry Track",
  year =         "2002",
  editor =       "Lawrence {"}Dave{"} Davis and Rajkumar Roy",
  pages =        "111--116",
  address =      "New York, New York",
  month =        "11-13 " # jul,
  organisation = "ISGEC",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "context-free grammars, Arrhenius, G3P dimensionally
                 consistent. large function set, parsimony pressure,
                 little man hours. outlier detection - GP+SVM+PCA,
                 GIGO",
}

@InProceedings{kordon:2002:rssboiogpannasvm,
  author =       "Arthur Kordon and Guido Smits and Elsa Jordaan and Ed
                 Rightor",
  title =        "Robust Soft Sensors Based On Integration of Genetic
                 Programming, Analytical Neural Networks, and Support
                 Vector Machines",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "896--901",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, ANN, SVM",
  abstract =     "A novel approach for development of inferential
                 sensors based on integration of three key computational
                 intelligence approaches (genetic programming,
                 analytical neural networks, and support vector
                 machines) is proposed. The advantages of this type of
                 soft sensors are their good generalization
                 capabilities, increased robustness, explicit
                 input/output relationships, self-assessment
                 capabilities, and low implementation and maintenance
                 cost.",
}

@InProceedings{Korkin:1997:CBM,
  author =       "Michael Korkin and Hugo {de Garis} and Felix Gers and
                 Hitoshi Hemmi",
  title =        "{``}{CBM} ({CAM}-Brain Machine){''}: {A} Hardware Tool
                 which Evolves a Neural Net Module in a Fraction of a
                 Second and Runs a Million Neuron Artificial Brain in
                 Real Time",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Evolvable Hardware",
  pages =        "498--503",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{korkmaz:2001:gecco,
  title =        "Controlling the Genetic Programming Search",
  author =       "Emin Erkan Korkmaz and Gokturk Ucoluk",
  pages =        "180",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 Grammar Induction, Context-free",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{korkmaz:2001:gpgi,
  author =       "Emin Erkan Korkmaz and Gokturk Ucoluk",
  title =        "Genetic Programming for Grammar Induction",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "245--251",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, context free
                 grammar induction, CFG, English NLP, C4.5",
  notes =        "GECCO-2001LB",
}

@InProceedings{korkmaz:2002:gecco,
  author =       "Emin Erkan Korkmaz and G{\"o}kt{\"u}rk
                 {\"U}{\c{c}}oluk",
  title =        "Controlling The Genetic Programming Search",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "891",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, poster
                 paper",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{korovkin:1999:VA,
  author =       "Kostyantyn Korovkin and Robert Richards",
  title =        "Visual auction: {A} classifier system pedagogical and
                 researcher tool",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "159--163",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{kotancheck:2002:gecco,
  author =       "Mark Kotanchek and Arthur Kordon and Guido Smits and
                 Flor Castillo and R. Pell and M. B. Seasholtz and L.
                 Chiang and P. Margl and P. K. Mercure and A. Kalos",
  title =        "Evolutionary Computing in Dow Chemical",
  booktitle =    "GECCO-2002 Presentations in the Evolutionary
                 Computation in Industry Track",
  year =         "2002",
  editor =       "Lawrence {"}Dave{"} Davis and Rajkumar Roy",
  pages =        "101--110",
  address =      "New York, New York",
  month =        "11-13 " # jul,
  organisation = "ISGEC",
  keywords =     "genetic algorithms, genetic programming, particle
                 swarm, PSO, neural networks, support vector machines,
                 SVM",
  notes =        "powerpoint slides? Diverse subsets from chemical
                 libraries. Soft sensors in intelligent alarm
                 processing. Polypropylene structure-property
                 relationships. non-linear DOE (design of experiments)
                 using GP/GENPRO See also kordon:2002:gecco",
}

@InProceedings{kovacs:1999:DSCS,
  author =       "Tim Kovacs",
  title =        "Deletion Schemes for Classifier Systems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "329--336",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Kowalczyk:1998:LAG,
  author =       "Ryszard Kowalczyk",
  title =        "On Linguistic Approximation with Genetic Programming",
  booktitle =    "Methodology and Tools in Knowledge-Based Systems",
  year =         "1998",
  editor =       "Jose Mira and Angel Pasqual {del Pobil} and Moonis
                 Ali",
  volume =       "1415",
  series =       "LNCS",
  pages =        "200--224",
  address =      "Benicassim, Castelln, Sapin",
  month =        jun,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming, artificial
                 intelligence fuzzy sets, natural language processing
                 NLP",
  ISSN =         "0302-9743",
  bibdate =      "Wed Sep 15 17:59:26 MDT 1999",
}

@InProceedings{Koza89,
  author =       "J. R. Koza",
  title =        "Hierarchical genetic algorithms operating on
                 populations of computer programs",
  editor =       "N. S. Sridharan",
  volume =       "1",
  pages =        "768--774",
  booktitle =    "Proceedings of the Eleventh International Joint
                 Conference on Artificial Intelligence IJCAI-89",
  year =         "1989",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Morgan Kaufmann",
  publisher_address = "San Mateo, CA, USA",
  month =        "20-25 " # aug,
  abstract =     "Existing approaches to artificial intelligence
                 problems such as sequence induction, automatic
                 programming, machine learning, planning, and pattern
                 recognition typically require specification in advance
                 of the size and shape of the solution to the problem
                 (often in a unnatural and difficult way). This paper
                 reports on a new approach in which the size and shape
                 of the solution to such problems is dynamically created
                 using Darwinian principles of reproduction and survival
                 of the fittest. Moreover, the resulting solution is
                 inherently hierarchical. The paper describes computer
                 experiments, using the author's 4341 line LISP program,
                 in five areas of artificial intelligence, namely (1)
                 sequence induction (e.g. inducing a computational
                 procedure for the recursive Fibonacci sequence and
                 inducing a computational procedure for a cubic
                 polynomial sequence), (2) automatic programming (e.g.
                 discovering a computational procedure for solving pairs
                 of linear equations, solving quadratic equations for
                 complex roots, and discovering trigonometric
                 identities), (3) machine learning of functions (e.g.
                 learning a Boolean multiplexer function previously
                 studied in neural net and classifier system work and
                 learning the exclusive-or and parity function), (4)
                 planning (e.g. developing a robotic action sequence
                 that can stack an arbitrary initial configuration of
                 blocks into a specified order), and (5) pattern
                 recognition (e.g. translation-invariant recognition of
                 a simple one dimensional shape in a linear retina).",
  notes =        "Held in Detroit, MI, USA?

                 ",
}

@TechReport{koza-90,
  key =          "Koza",
  author =       "J. Koza",
  title =        "Genetic programming: {A} paradigm for genetically
                 breeding populations of computer programs to solve
                 problems",
  type =         "Technical Report",
  number =       "{STAN}-{CS}-90-1314",
  institution =  "Dept. of Computer Science, Stanford University",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://elib.stanford.edu/pub/reports/cs/tr/90/1314/CS-TR-90-1314.OCR.txt",
  URL =          "ftp://elib.stanford.edu/pub/reports/cs/tr/90/1314/CS-TR-90-1314.ps",
  month =        jun,
  year =         "1990",
  abstract =     "Many seemingly different problems in artificial
                 intelligence, symbolic processing, and machine learning
                 can be viewed as requiring discovery of a computer
                 program that produces some desired output for
                 particular inputs. When viewed in this way, the process
                 of solving these problems becomes equivalent to
                 searching a space of possible computer programs for a
                 most fit individual computer program. The new
                 {"}genetic programming{"} paradigm described herein
                 provides a way to search for this most fit individual
                 computer program. In this new {"}genetic programming{"}
                 paradigm, populations of computer programs are
                 genetically bred using the Darwinian principle of
                 survival of the fittest and using a genetic crossover
                 (recombination) operator appropriate for genetically
                 mating computer programs. In this paper, the process of
                 formulating and solving problems using this new
                 paradigm is illustrated using examples from various
                 areas.

                 Examples come from the areas of machine learning of a
                 function; planning; sequence induction; function
                 function identification (including symbolic regression,
                 empirical discovery, {"}data to function{"} symbolic
                 integration, {"}data to function{"} symbolic
                 differentiation); solving equations, including
                 differential equations, integral equations, and
                 functional equations); concept formation; automatic
                 programming; pattern recognition, time-optimal control;
                 playing differential pursuer-evader games; neural
                 network design; and finding a game-playing strategyfor
                 a discrete game in extensive form.",
  size =         "133 pages",
  notes =        "ftp://elib.stanford.edu/pub/reports/cs/tr/90/1314 also
                 contains GIF and MAC versions",
}

@InProceedings{Koza:1990:gem,
  author =       "John R. Koza",
  title =        "A genetic approach to econometric modeling",
  booktitle =    "Sixth World Congress of the Econometric Society",
  year =         "1990",
  address =      "Barcelona, Spain",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "27 August",
}

@InProceedings{Koza:1990:GPai,
  author =       "John R. Koza",
  title =        "Genetically breeding populations of computer programs
                 to solve problems in artificial intelligence",
  booktitle =    "Proceedings of the Second International Conference on
                 Tools for AI, Herndon, Virginia, USA",
  year =         "1990",
  pages =        "819--827",
  month =        "6-9 " # nov,
  publisher =    "IEEE Computer Society Press, Los Alamitos, CA, USA",
  keywords =     "genetic algorithms, genetic programming",
}

@Misc{Koza:1990:pat-GAsp,
  author =       "John R. Koza",
  title =        "Non-Linear Genetic Algorithms for Solving Problems",
  howpublished = "U.S. Patent",
  year =         "1990",
  month =        "19 " # jun,
  note =         "filed may 20, 1988, issued june 19, 1990, 4,935,877.
                 Australian patent 611,350 issued september 21, 1991.
                 Canadian patent 1,311,561 issued december 15, 1992.",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@InProceedings{koza:1990:isp,
  author =       "John R. Koza",
  title =        "Integrating symbolic processing into genetic
                 algorithms",
  booktitle =    "Workshop on Integrating Symbolic and Neural Processes
                 at AAAI-90",
  year =         "1990",
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Although genetic algorithms, like neural networks, are
                 seemingly inappropriate for handling symbolically
                 oriented problems, recent work in the fields of both
                 the genetic algorithm and neural network argues
                 otherwise. This presentation will discuss a group of
                 seemingly different problems from symbolic processing,
                 artificial intelligence, and machine learning which can
                 be solved using genetic algorithms if the appropriate
                 representation scheme and appropriate modifications to
                 the repertoire of genetic operations are adopted.

                 The approaches used in applying genetic algorithms to
                 such symbolic problems may shed light on the problem of
                 applying neural networks to symbolic problems.

                 Many problems from symbolic processing appear to be
                 inappropriate candidates for solution via genetic
                 algorithms because they, in effect, require discovery
                 of a computer program that produces some desired output
                 value when presented with particular inputs. However,
                 with an appropriate representation scheme and
                 appropriate modifications of the traditional genetic
                 operations, it is possible to genetically search the
                 space of possible computer programs for a most fit
                 individual computer program. In this new {"}genetic
                 programming{"} paradigm, populations of computer
                 programs (LISP symbolic expressions) are genetically
                 bred using the Darwinian principle of survival of the
                 fittest and using a genetic crossover (recombination)
                 operator appropriate for genetically mating computer
                 programs.

                 Depending on the terminology of the particular field of
                 interest, the {"}computer program{"} may be called a
                 robotic action plan, an optimal control strategy, a
                 decision tree, an econometric model, a game-playing
                 strategy, the state transition equations, the transfer
                 function, or, perhaps merely, a composition of
                 functions. Similarly, the {"}inputs{"} to the
                 {"}computer program{"} may be called sensor values,
                 state variables, independent variables, attributes of
                 an object, or, perhaps more prosaically, the arguments
                 to a function.

                 The methods for applying genetic algorithms to symbolic
                 problems are illustrated using examples from the areas
                 of function learning, robotic planning, symbolic
                 function identification, symbolic regression, symbolic
                 integration and differentiation, symbolic solution of
                 differential equations, game-playing, sequence
                 induction, empirical discovery and econometric
                 modeling, concept formation, automatic programming ,
                 pattern recognition, time-optimal control. Problems of
                 the type described above can be expeditiously solved
                 only if the flexibility found in computer programs is
                 available. This flexibility includes the ability to
                 perform alternative computations conditioned on the
                 outcome of intermediate calculations, to perform
                 computations on variables of many different types, to
                 perform iterations and recursions to achieve the
                 desired result, and to define and subsequently use
                 computed values and sub-programs.

                 The process of solving these problems can be
                 reformulated as a search for a most fit individual
                 computer program in the space of possible computer
                 programs composed of various terms (atoms) along with
                 standard arithmetic operations, standard programming
                 operations, standard mathematical functions, and
                 various functions peculiar to the given problem domain.
                 Four types of objects are manipulated as we build
                 computer programs, namely, functions of various number
                 of arguments; variable atoms; constant atoms; and
                 control structures such as If-Then-Else, Do-Until,
                 etc.",
  abstract =     "As will be seen, the LISP S-expression required to
                 solve each of the problems described above will emerge
                 from a simulated evolutionary process. This process
                 will start with an initial population of randomly
                 generated LISP S-expressions composed of functions and
                 atoms appropriate to the problem domain. Then, a
                 process based on the Darwinian model of reproduction
                 and survival of the fittest and genetic recombination
                 will be used to create a new population of individuals.
                 In particular, a genetic process of sexual reproduction
                 among two parental S-expressions will be used to create
                 offspring S-expressions. At least one of two
                 participating parental S-expressions will be selected
                 in proportion to fitness. The resulting offspring
                 S-expressions will be composed of sub-expressions
                 ({"}building blocks{"}) from their parents. Finally,
                 the new population of offspring (i.e. the new
                 generation) will replace the old population of parents
                 and the process will continue.

                 As will be seen, this highly parallel, locally
                 controlled, and decentralized process will produce
                 populations which, over a period of generations, tend
                 to exhibit increasing average fitness in dealing with
                 their environment, and which, in addition, can robustly
                 (i.e. rapidly and effectively) adapt to changes in
                 their environment.",
  notes =        "Presented at the Workshop on Integrating Symbolic and
                 Neural Processes at AAAI-90 in Boston, MA, USA, July
                 29, 1990.",
}

@Misc{Koza:1992:GAspfff,
  author =       "John R. Koza",
  title =        "Non-Linear Genetic Algorithms for Solving Problems by
                 Finding a Fit Composition of Functions",
  howpublished = "U.S. Patent",
  year =         "1992",
  month =        "4 " # aug,
  note =         "filed march 28, 1990, issued august 4, 1992,
                 5,136,686",
  size =         "pages",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza:1991:ecpia,
  author =       "John R. Koza",
  title =        "Evolution and co-evolution of computer programs to
                 control independent-acting agents",
  booktitle =    "From Animals to Animats: Proceedings of the First
                 International Conference on Simulation of Adaptive
                 Behavior, 24-28, September 1990",
  year =         "1991",
  editor =       "Jean-Arcady Meyer and Stewart W. Wilson",
  pages =        "366--375",
  address =      "Paris, France",
  publisher_address = "Cambridge, MA",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{Koza:geCP,
  author =       "John R. Koza",
  title =        "Genetic evolution and co-evolution of computer
                 programs",
  booktitle =    "Artificial Life II",
  publisher =    "Addison-Wesley",
  year =         "1991",
  editor =       "Christopher Taylor Charles Langton and J. Doyne Farmer
                 and Steen Rasmussen",
  volume =       "X",
  series =       "SFI Studies in the Sciences of Complexity",
  address =      "Santa Fe Institute, New Mexico, USA",
  publisher_address = "Redwood City, CA, USA",
  month =        feb # " 1990",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "603--629",
}

@InProceedings{Koza:1991:randGP,
  author =       "John R. Koza",
  title =        "Evolving a computer program to generate random numbers
                 using the genetic programming paradigm",
  booktitle =    "Proceedings of the Fourth International Conference on
                 Genetic Algorithms",
  year =         "1991",
  editor =       "Richard K. Belew and Lashon B. Booker",
  pages =        "37--44",
  address =      "University of California - San Diego, La Jolla, CA,
                 USA",
  month =        "13-16 " # jul,
  publisher_address = "San Mateo, CA, USA",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{Koza:1991:gem,
  author =       "John R. Koza",
  title =        "A genetic approach to econometric modeling",
  booktitle =    "Economics and Cognitive Science",
  publisher =    "Pergamon Press",
  year =         "1991",
  editor =       "Paul Bourgine and Bernard Walliser",
  address =      "Oxford, UK",
  pages =        "57--75",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{Koza:1992:GPdgc,
  author =       "John R. Koza",
  title =        "The genetic programming paradigm: Genetically breeding
                 populations of computer programs to solve problems",
  booktitle =    "Dynamic, Genetic, and Chaotic Programming",
  publisher =    "John Wiley",
  year =         "1992",
  editor =       "Branko Soucek and the IRIS Group",
  pages =        "203--321",
  address =      "New York",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza:1992:GAbtt,
  author =       "John R. Koza",
  title =        "A genetic approach to finding a controller to back up
                 a tractor-trailer truck",
  booktitle =    "Proceedings of the 1992 American Control Conference",
  year =         "1992",
  volume =       "III",
  pages =        "2307--2311",
  address =      "Evanston, IL, USA",
  publisher =    "American Automatic Control Council",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza:1992:GPgs,
  author =       "John R. Koza",
  title =        "Genetic evolution and co-evolution of game
                 strategies",
  booktitle =    "The International Conference on Game Theory and Its
                 Applications, Stony Brook, New York. July 15, 1992",
  year =         "1992",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The problem of discovering a strategy for playing a
                 game is an important problem in game theory. This
                 problem can be viewed as requiring discovery of a
                 computer program. The desired computer program takes
                 either the entire history of past moves in the game or
                 the current state of the game as its input and produces
                 the next move as its output. This paper describes the
                 recently developed genetic programming paradigm which
                 genetically breeds populations of computer programs to
                 solve problems. In genetic programming, the individuals
                 in the population are independently acting hierarchical
                 compositions of functions and arguments of various
                 sizes and shapes. Each of these individual computer
                 programs is evaluated for its fitness in playing the
                 game. A simulated evolutionary process driven by this
                 measure of fitness then uses the Darwinian principle of
                 reproduction and survival of the fittest and the
                 genetic operation of crossover (sexual recombination)
                 to solve the problem. Genetic programming can also
                 operate simultaneously on two (or more) populations of
                 programs. In such {"}co-evolution,{"} each population
                 acts as the environment for the other population. In
                 particular, each individual of the first population is
                 evaluated for {"}relative fitness{"} by testing it
                 against each individual in the second population, and,
                 simultaneously, each individual in the second
                 population is evaluated for {"}relative fitness{"} by
                 testing it against each individual in the first
                 population. In this paper, genetic programming is
                 illustrated with three different problems. The first
                 problem involves genetically breeding a population of
                 computer programs to find an optimal strategy for a
                 player of a discrete two-person 32-outcome game
                 represented by a game tree in extensive form. In this
                 problem, the entire history of past moves of both
                 players is used as input to the computer program. The
                 second problem involves genetically breeding a minimax
                 control strategy in a differential game with an
                 independently-acting pursuer and evader. In this
                 problem, the state of the game is used as input to the
                 computer program. The third problem illustrates the
                 {"}co-evolution{"} and involves genetically breeding an
                 optimal strategy for a player of a discrete two-person
                 32-outcome game represented by a game tree in extensive
                 form.",
  notes =        "Paper presented at",
}

@InCollection{Koza:1994:wallGP,
  author =       "John R. Koza",
  title =        "Evolution of a subsumption architecture that performs
                 a wall following task for an autonomous mobile robot
                 via genetic programming",
  booktitle =    "Computational Learning Theory and Natural Learning
                 Systems",
  publisher =    "MIT Press",
  year =         "1994",
  editor =       "Thomas Petsche",
  volume =       "2",
  pages =        "321--346",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza:1993:sddGP,
  author =       "John R. Koza",
  title =        "Simultaneous discovery of detectors and a way of using
                 the detectors via genetic programming",
  booktitle =    "1993 IEEE International Conference on Neural
                 Networks",
  year =         "1993",
  volume =       "III",
  pages =        "1794--1801",
  address =      "San Francisco, USA",
  publisher_address = "Piscataway, NJ, USA",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza:1993:mprsGP,
  author =       "John R. Koza",
  title =        "Discovery of a main program and reusable subroutines
                 using genetic programming",
  booktitle =    "Proceedings of the Fifth Workshop on Neural Networks:
                 An International Conference on Computational
                 Intelligence: Neural Networks, Fuzzy Systems,
                 Evolutionary Programming, and Virtual Reality",
  year =         "1993",
  pages =        "109--118",
  publisher_address = "San Diego, CA, USA",
  organisation = "The Society for Computer Simulation",
  keywords =     "genetic algorithms, genetic programming",
}

@Book{Koza:1993:alife,
  editor =       "John R. Koza",
  title =        "Artificial Life at Stanford 1993",
  publisher =    "Stanford University Bookstore",
  year =         "1993",
  address =      "Stanford, California, 94305-3079 USA, Phone
                 415-329-1217 or 800-533-2670",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This volume contains 16 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the winter quarter
                 1993. The appendix contains some of the material
                 containing basicinformation about the course.
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
  ISBN =         "0-18-171957-6",
}

@Book{Koza:1993:GA,
  editor =       "John R. Koza",
  title =        "Genetic Algorithms at Stanford 1993",
  publisher =    "Stanford University Bookstore",
  year =         "1993",
  address =      "Stanford, California, 94305-3079 USA, Phone
                 415-329-1217 or 800-533-2670",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This volume contains 24 papers written and submitted
                 by students describing their term projects for the
                 course in genetic algorithms (Computer Science 426) at
                 Stanford University for the spring quarter 1993. The
                 appendix contains some of the material containing basic
                 information about the course.
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{koza:1993:LsCA,
  author =       "John R. Koza",
  title =        "Discovery of rewrite rules in Lindenmayer systems and
                 state transition rules in cellular automata via genetic
                 programming",
  booktitle =    "Symposium on Pattern Formation (SPF-93), Claremont,
                 California, USA",
  year =         "1993",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "It is difficult to write programs for both Lindenmayer
                 systems and cellular automata. This paper demonstrates
                 the possibility of discovering the rewrite rule for
                 Lindenmayer systems and the state transition rules for
                 cellular automata by means of genetic programming.
                 Genetic programming is an extension of the genetic
                 algorithm in which computer programs are genetically
                 bred to solve problems. We demonstrate the use of
                 genetic programming to discover the rewrite rules for a
                 Lindenmayer system for the quadratic Koch island using
                 a pattern matching measure as the driving force for the
                 evolutionary process. We also demonstrate the use of
                 genetic programming to discover the state transition
                 rules for a one-dimensional and two-dimensional
                 cellular automata using entropy as the driving force
                 for the evolutionary process.",
  notes =        "Presented at",
}

@InCollection{Kinnear:Koza:1994:adf,
  author =       "John R. Koza",
  title =        "Scalable learning in genetic programming using
                 automatic function definition",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  year =         "1994",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  pages =        "99--117",
  chapter =      "6",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{Kinnear:Koza:1994:intro,
  author =       "John R. Koza",
  title =        "Introduction to genetic programming",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  year =         "1994",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  pages =        "21--42",
  chapter =      "2",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{Koza:1994:spsrCP,
  author =       "John R. Koza",
  title =        "Spontaneous emergence of self-replicating and
                 evolutionarily self-improving computer programs",
  booktitle =    "Artificial Life III",
  publisher =    "Addison-Wesley",
  year =         "1994",
  editor =       "Christopher G. Langton",
  volume =       "XVII",
  series =       "SFI Studies in the Sciences of Complexity",
  pages =        "225--262",
  address =      "Santa Fe, New Mexico, USA",
  publisher_address = "Redwood City, CA, USA",
  month =        "15-19 " # jun # " 1992",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Held June 1992 in Santa Fe, New Mexico, USA",
}

@InProceedings{Koza:1994:rppsGP,
  author =       "J. R. Koza",
  title =        "Recognizing patterns in protein sequences using
                 {iteration-performing} calculations in genetic
                 programming",
  booktitle =    "1994 IEEE World Congress on Computational
                 Intelligence",
  year =         "1994",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza:1994:itpsGP,
  author =       "John R. Koza",
  title =        "Automated discovery of detectors and
                 {iteration-performing} calculations to recognize
                 patterns in protein sequences using genetic
                 Programming",
  booktitle =    "Proceedings of the Conference on Computer Vision and
                 Pattern Recognition",
  year =         "1994",
  pages =        "684--689",
  publisher =    "IEEE Computer Society Press",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper describes an automated process for the
                 dynamic creation of a pattern-recognizing computer
                 program consisting of initially-unknown detectors, an
                 initially-unknown iterative calculation incorporating
                 the as-yet-uncreated detectors, and an
                 initially-unspecified final calculation incorporating
                 the results of the as-yet-uncreated iteration. The
                 program's goal is to recognize a given protein segment
                 as being a transmembrane domain or non-transmembrane
                 area. The recognizing program to solve this problem
                 will be evolved using the recently-developed genetic
                 programming paradigm. Genetic programming starts with a
                 primordial ooze of randomly generated computer programs
                 composed of available programmatic ingredients and then
                 genetically breeds the population using the Darwinian
                 principle of survival of the fittest and the genetic
                 crossover (sexual recombination) operation. Automatic
                 function definition enables genetic programming to
                 dynamically create subroutines (detectors). When
                 cross-validated, the best genetically-evolved
                 recognizer achieves an out-of-sample correlation of
                 0.968 and an out-of-sample error rate of 1.6%. This
                 error rate is better than that recently reported for
                 five other methods.

                 ",
  notes =        "

                 ",
}

@TechReport{koza:1994:aao,
  author =       "John R. Koza",
  title =        "Architecture-Altering Operations for Evolving the
                 Architecture of a Multipart Program in Genetic
                 Programming",
  type =         "Technical Report",
  number =       "{STAN}-{CS}-94-1528",
  institution =  "Dept. of Computer Science, Stanford University",
  keywords =     "genetic algorithms, genetic programming",
  year =         "1994",
  address =      "Stanford, California 94305, USA",
  month =        oct,
  URL =          "ftp://elib.stanford.edu/pub/reports/cs/tr/94/1528/CS-TR-94-1528.ps",
  abstract =     "Previous work described a way to evolutionarily select
                 the architecture of a multi-part computer program From
                 among preexisting alternatives in the population while
                 concurrently solving a problem during a run of genetic
                 programming. This report describes six new
                 architecture-altering operations that provide a way to
                 evolve the architecture of a multi-part program in the
                 sense of actually changing the architecture of programs
                 dynamically during the run. The new
                 architecture-altering operations are motivated by the
                 naturally occurring operation of gene duplication as
                 described in Susumu Ohno's provocative 1970 book
                 Evolution by Means of Gene Duplication as well as the
                 naturally occurring operation of gene deletion. The six
                 new architecture-altering operations are branch
                 duplication, argument duplication, branch creation,
                 argument creation, branch deletion and argument
                 deletion. A connection is made between genetic
                 programming and other techniques of automated problem
                 solving by interpreting the architecture-altering
                 operations as providing an automated way to specialize
                 and generalize programs. The report demonstrates that a
                 hierarchical architecture can be evolved to solve an
                 illustrative symbolic regression problem using the
                 architecture- altering operations. Future work will
                 study the amount of additional computational effort
                 required to employ the architecture-altering
                 operations.",
  notes =        "Postscript barfed on our printer. See also
                 koza:1995:ea",
  size =         "57 pages",
}

@Article{koza:1994:SandC,
  author =       "John R. Koza",
  title =        "Genetic Programming: {O}n the programming of computers
                 by means of natural selection",
  journal =      "Statistics and Computing",
  year =         "1994",
  volume =       "4",
  number =       "2",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Chapman \& Hall, London",
  notes =        "Special issue on Evolutionary Programming, Guest
                 editor Zbigniew Michalewicz",
}

@InProceedings{Koza:1990:cartGP,
  author =       "John R. Koza and Martin A. Keane",
  title =        "Cart centering and broom balancing by genetically
                 breeding populations of control strategy programs",
  booktitle =    "Proceedings of International Joint Conference on
                 Neural Networks",
  year =         "1990",
  month =        "15-19 " # jan,
  volume =       "I",
  pages =        "198--201",
  address =      "Washington",
  publisher_address = "Hillsdale, NJ, USA",
  publisher =    "Lawrence Erlbaum",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza:1990:GPbroom,
  author =       "John R. Koza and Martin A. Keane",
  title =        "Genetic breeding of non-linear optimal control
                 strategies for broom balancing",
  booktitle =    "Proceedings of the Ninth International Conference on
                 Analysis and Optimization of Systems. 1990",
  year =         "1990",
  pages =        "47--56",
  address =      "Antibes, France",
  publisher_address = "Berlin, Germany",
  month =        jun,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Many seemingly different problems in machine learning,
                 artificial intelligence, and symbolic processing can be
                 viewed as requiring the discovery of a computer program
                 that produces some desired output for particular
                 inputs. When viewed in this way, the process of solving
                 these problems becomes equivalent to searching a space
                 of possible computer programs for a highly fit
                 individual computer program. The recently developed
                 genetic programming paradigm described herein provides
                 a way to search the space of possible computer programs
                 for a highly fit individual computer program to solve
                 (or approximately solve) a surprising variety of
                 different problems from different fields. In genetic
                 programming, populations of computer programs are
                 genetically bred using the Darwinian principle of
                 survival of the fittest and using a genetic crossover
                 (sexual recombination) operator appropriate for
                 genetically mating computer programs. Genetic
                 programming is illustrated via an example of machine
                 learning of the Boolean 11-multiplexer function,
                 symbolic regression of the econometric exchange
                 equation from noisy empirical data, the control problem
                 of backing up a tractor-trailer truck, the
                 classification problem of distinguishing between two
                 intertwined spirals., and the robotics problem of
                 controlling an autonomous mobile robot to find a box in
                 the middle of an irregular room and move the box to the
                 wall. Hierarchical automatic function definition
                 enables genetic programming to define potentially
                 useful functions automatically and dynamically during a
                 run - much as a human programmer writing a complex
                 computer program creates subroutines (procedures,
                 functions) to perform groups of steps which must be
                 performed with different instantiations of the dummy
                 variables (formal parameters) in more than one place in
                 the main program. Hierarchical automatic function
                 definition is illustrated via the machine learning of
                 the Boolean 11-parity function.",
}

@InProceedings{Koza:1993:pimlssi,
  author =       "John R. Koza and Martin A. Keane and James P. Rice",
  title =        "Performance improvement of machine learning via
                 automatic discovery of facilitating functions as
                 applied to a problem of symbolic system
                 identification",
  booktitle =    "1993 IEEE International Conference on Neural
                 Networks",
  year =         "1993",
  volume =       "I",
  pages =        "191--198",
  address =      "San Francisco, USA",
  publisher_address = "Piscataway, NJ, USA",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
}

@Misc{Koza:1992:patGPpcp,
  author =       "John R. Koza and James P. Rice",
  title =        "A Non-Linear Genetic Process for Use with Plural
                 Co-Evolving Populations",
  howpublished = "U.S. Patent",
  year =         "1992",
  month =        sep,
  note =         "filed september 18, 1990, issued september 15, 1992,
                 5,148,513",
  keywords =     "genetic algorithms, genetic programming",
}

@Misc{Koza:1992:patGPpse,
  author =       "John R. Koza and James P. Rice",
  title =        "A Non-Linear Genetic Process for Problem Solving Using
                 Spontaneously Emergent Self-Replicating and Self-
                 Improving Entities",
  howpublished = "U.S. Patent",
  year =         "1992",
  month =        jun,
  note =         "application filed june 16, 1992, issued february 14,
                 1995, 5,390,282",
  size =         "pages",
  keywords =     "genetic algorithms, genetic programming",
}

@Misc{Koza:1992:patGPdeADF,
  author =       "John R. Koza and James P. Rice",
  title =        "A Non-Linear Genetic Process for Data Encoding and for
                 Solving Problems Using Automatically Defined
                 Functions",
  howpublished = "U.S. Patent",
  year =         "1992",
  month =        may,
  note =         "application filed May 11, 1992, issued August 30,
                 1994, 5,343,554",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@Misc{Koza:1994:patGPeARCH,
  author =       "John R. Koza and David Andre and Walter Alden
                 Tackett",
  title =        "Evolution of the Architecture of a Multi-Part Program
                 to Solve a Problem Using Architecture Altering
                 Operations",
  howpublished = "U.S. Patent Application",
  year =         "1994",
  month =        aug,
  note =         "filed August 4, 1994",
  keywords =     "genetic algorithms, genetic programming",
}

@TechReport{Koza:1992:fflizrd,
  author =       "John R. Koza and James P. Rice and Jonathan
                 Roughgarden",
  title =        "Evolution of Food Foraging Strategies for the
                 Caribbean Anolis Lizard Using Genetic Programming",
  institution =  "Santa Fe Institute",
  year =         "1992",
  type =         "Working Paper",
  number =       "92-06-028",
  address =      "1399 Hyde Park Road Santa Fe, New Mexico 87501-8943
                 USA

                 ",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
}

@Article{Koza:1992:lizrd,
  author =       "John R. Koza and James P. Rice and Jonathan
                 Roughgarden",
  title =        "Evolution of food foraging strategies for the
                 Caribbean Anolis lizard using genetic programming",
  journal =      "Adaptive Behavior",
  year =         "1992",
  volume =       "1",
  number =       "2",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "47--74",
}

@InCollection{Koza:1991:ALvid,
  author =       "John R. Koza and James P. Rice",
  title =        "A genetic approach to artificial intelligence",
  booktitle =    "Artificial Life II Video Proceedings",
  publisher =    "Addison-Wesley",
  year =         "1991",
  editor =       "Christopher G. Langton",
  address =      "Santa Fe, New Mexico, USA",
  publisher_address = "Redwood City, CA, USA",
  month =        feb # " 1990",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza:1192:robGP,
  author =       "John R. Koza and James P. Rice",
  title =        "Automatic programming of robots using genetic
                 programming",
  booktitle =    "Proceedings of Tenth National Conference on Artificial
                 Intelligence",
  year =         "1992",
  pages =        "194--201",
  publisher_address = "Menlo Park, CA, USA",
  publisher =    "AAAI Press/MIT Press",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Koza91,
  author =       "John R. Koza and James P. Rice",
  title =        "Genetic Generation of Both the Weights and
                 Architecture for a Neural Network",
  booktitle =    "International Joint Conference on Neural Networks,
                 IJCNN-91",
  year =         "1991",
  volume =       "II",
  pages =        "397--404",
  address =      "Washington State Convention and Trade Center, Seattle,
                 WA, USA",
  publisher_address = "1109 Spring Street, Suite 300, Silver Spring, MD
                 20910, USA",
  month =        "8-12 " # jul,
  publisher =    "IEEE Computer Society Press",
  keywords =     "genetic algorithms, genetic programming,
                 connectionism, one-bit adder, cogann ref",
  ISBN =         "0-7803-0164-1",
  LCCN =         "QA76.87.I57 1991b",
  bibdate =      "Wed Jan 15 14:07:16 1997",
  notes =        "Two volumes. IEEE catalog number: 91CH3049-4.",
  acknowledgement = ack-nhfb,
  notes =        "IJCNN-91",
}

@InProceedings{Koza90,
  author =       "J. R. Koza",
  title =        "Concept formation and decision tree induction using
                 the genetic programming paradigm",
  editor =       "H.-P. Schwefel and R. M{\"{a}}nner",
  volume =       "496",
  series =       "Lecture Notes in Computer Science",
  pages =        "124--128",
  booktitle =    "Parallel Problem Solving from Nature - Proceedings of
                 1st Workshop, PPSN 1",
  year =         "1991",
  publisher =    "Springer-Verlag",
  address =      "Dortmund, Germany",
  publisher_address = "Berlin, Germany",
  keywords =     "genetic algorithms, genetic programming",
  month =        "1-3 " # oct,
}

@InProceedings{Koza92a,
  author =       "J. R. Koza",
  title =        "Evolution of subsumption using genetic programming",
  editor =       "F. J. Varela and P. Bourgine",
  pages =        "110--119",
  booktitle =    "Proceedings of the First European Conference on
                 Artificial Life. Towards a Practice of Autonomous
                 Systems",
  year =         "1992",
  publisher =    "MIT Press",
  publisher_address = "Cambridge, MA, USA",
  address =      "Paris, France",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-13 " # dec,
  notes =        "ECAL-91. Evolution of sonar guided wall following
                 robot",
}

@InProceedings{Koza92,
  author =       "John R. Koza",
  title =        "A Genetic Approach to the Truck Backer Upper Problem
                 and the Inter-Twined Spiral Problem",
  booktitle =    "Proceedings of IJCNN International Joint Conference on
                 Neural Networks",
  volume =       "IV",
  pages =        "310--318",
  year =         "1992",
  publisher_address = "Piscataway, NJ, USA",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 connectionism",
  abstract =     "ABSTRACT Neural networks are a biologically motivated
                 problem-solving paradigm that has proven successful in
                 robustly solving a variety of problems. This paper
                 describes another biologically motivated paradigm,
                 namely genetic programming, which can also solve a
                 variety of problems. This paper explains genetic
                 programming and applies it to two well-know benchmark
                 problems from the field of neural networks. The truck
                 backer upper problem is a multi-dimensional control
                 problem and the inter-twined spirals problem is a
                 challenging classification problem.",
  notes =        "IJCNN-92

                 ",
}

@InProceedings{Koza92b,
  author =       "John R. Koza",
  title =        "Hierarchical automatic function definition in genetic
                 programming",
  booktitle =    "Foundations of Genetic Algorithms 2",
  editor =       "L. Darrell Whitley",
  publisher =    "Morgan Kaufmann",
  year =         "1992",
  pages =        "297--318",
  address =      "Vail, Colorado, USA",
  month =        "24--29 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Email: Koza@Sunburn.Stanford.edu",
  abstract =     "A key goal in machine learning and artificial
                 intelligence is to automatically and dynamically
                 decompose problems into simpler problems in order to
                 facilitate their solution. This paper describes two
                 extensions to genetic programming, called
                 {"}automatic{"} function definition and {"}hierarchical
                 automatic{"} function definition, wherein functions
                 that might be useful in solving a problem are
                 automatically and dynamically defined during a run in
                 terms of dummy variables. The defined functions are
                 then repeatedly called from the automatically
                 discovered {"}main{"} result-producing part of the
                 program with different instantiations of the dummy
                 variables. In the {"}hierarchical{"} version of
                 automatic function definition, automatically defined
                 functions may call other automatically defined
                 functions, thus creating a hierarchy of dependencies
                 among the automatically defined functions. The
                 even-11-parity problem was solved using hierarchical
                 automatic function definition.",
}

@InCollection{Article:91:Koza:GeneticAlgoritm,
  author =       "John R. Koza",
  title =        "A hierarchical approach to learning the boolean
                 multiplexer function",
  pages =        "171--192",
  editor =       "Gregory J. E. Rawlins",
  booktitle =    "Foundations of genetic algorithms",
  publisher =    "Morgan Kaufmann",
  publisher_address = "San Mateo, California, USA",
  year =         "1991",
  address =      "Indiana University",
  month =        "15-18 " # jul # " 1990",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper desribes the recently developed genetic
                 programming paradigm, which genetically breeds
                 populations of computer programs to solve problems. In
                 genetic programming, the individuals in the population
                 are hierarchical compositions of functions and
                 arguments. Each of these individual computer programs
                 is evaluated for its fitness in handling the
                 problemenvironment. The size and shape of the computer
                 program needed to solve the problem is not
                 predetermined by the user, but instead emerges from the
                 simulated evolutionary process driven by fitness. In
                 this paper, the operation of the genetic programming
                 paradigm is illustrated with the problem of learning
                 the boolean 11-multiplexer function.",
  notes =        "FOGA-90",
}

@Book{koza:book,
  author =       "John R. Koza",
  title =        "Genetic Programming: On the Programming of Computers
                 by Means of Natural Selection",
  year =         "1992",
  publisher =    "MIT Press",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming, text book",
  ISBN =         "0-262-11170-5",
}

@InProceedings{koza:adf,
  author =       "John R. Koza",
  title =        "Simultaneous Discovery of Reusable Detectors and
                 Subroutines Using Genetic Programming",
  editor =       "Stephanie Forrest",
  publisher_address = "San Mateo, CA, USA",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  publisher =    "Morgan Kaufmann",
  size =         "8 pages",
  pages =        "295--302",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Comparison of GP and GP+Automatic Function definition
                 for San Mateo trail ants, finds improvement of 1:2 in
                 number of fitness cases required and 21% reduction is
                 size of eventual s-expressions. NO CASE made that
                 either cases are using optimal parameters.",
}

@Book{koza:video,
  author =       "John R. Koza and James P. Rice",
  title =        "Genetic Programming:The Movie",
  year =         "1992",
  publisher =    "MIT Press",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
}

@Book{koza:gp2,
  author =       "John R. Koza",
  title =        "Genetic Programming {II}: Automatic Discovery of
                 Reusable Programs",
  publisher =    "MIT Press",
  year =         "1994",
  address =      "Cambridge Massachusetts",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-11189-6",
  size =         "746 pages",
}

@Book{koza:video2,
  author =       "John R. Koza",
  title =        "Genetic Programming {II} Videotape: The next
                 generation",
  publisher =    "MIT Press",
  year =         "1994",
  address =      "55 Hayward Street, Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  size =         "62 minutes",
}

@InProceedings{koza:1994:cpstd,
  author =       "John R. Koza",
  title =        "Evolution of a computer program for classifying
                 protein segments as transmembrane domains using genetic
                 programming",
  booktitle =    "Proceedings of the Second International Conference on
                 Intelligent Systems for Molecular Biology",
  year =         "1994",
  editor =       "Russ Altman and Douglas Brutlag and Peter Karp and
                 Richard Lathrop and David Searls",
  pages =        "244--252",
  publisher_address = "Menlo Park, CA, USA",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{koza:1994:eecb,
  author =       "John R. Koza",
  title =        "Evolution of emergent cooperative behavior using
                 genetic programming",
  booktitle =    "Computing with Biological Metaphors",
  publisher =    "Chapman \& Hall",
  year =         "1994",
  editor =       "Ray Paton",
  pages =        "280--297",
  address =      "London, UK",
  keywords =     "genetic algorithms, genetic programming",
}

@Book{koza:1994:alife,
  editor =       "John R. Koza",
  title =        "Artificial Life at Stanford 1994",
  year =         "1994",
  address =      "Stanford, California, 94305-3079 USA, Phone
                 415-329-1217 or 800-533-2670",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming, artificial
                 life",
  ISBN =         "0-18-182105-2",
  size =         "215 pages",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@Book{koza:1994:gas,
  title =        "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  address =      "Stanford, California, 94305-3079 USA, Phone
                 415-329-1217 or 800-533-2670",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  size =         "approx 230 pages",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@TechReport{Koza:1995:pGPnt,
  author =       "John R. Koza and David Andre",
  title =        "Parallel Genetic Programming on a Network of
                 Transputers",
  institution =  "Stanford University, Department of Computer Science",
  year =         "1995",
  type =         "Technical Report",
  number =       "CS-TR-95-1542",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp::/elib.stanford.edu/pub/reports/cs/tr/95/1542/",
  abstract =     "This report describes the parallel implementation of
                 genetic programming in the C programming language using
                 a PC 486 type computer (running Windows) acting as a
                 host and a network of transputers acting as processing
                 nodes. Using this approach, researchers of genetic
                 algorithms and genetic programming can acquire
                 computing power that is intermediate between the power
                 of currently available workstations and that of
                 supercomputers at a cost that is intermediate between
                 the two.

                 A comparison is made of the computational effort
                 required to solve the problem of symbolic regression of
                 the Boolean even-5-parity function with different
                 migration rates. Genetic programming required the least
                 computational effort with an 8% migration rate.
                 Moreover, this computational effort was less than that
                 required for solving the problem with a serial computer
                 and a panmictic population of the same size. That is,
                 apart from the nearly linear speed-up in executing a
                 fixed amount of code inherent in the parallel
                 implementation of genetic programming, parallelization
                 delivered more than linear speed-up in solving the
                 problem using genetic programming.",
  notes =        "Our printers barfed about halfway through. See also
                 andre:1995:parallel",
  size =         "21 pages",
}

@InProceedings{koza:1995:ea,
  author =       "John R. Koza",
  title =        "Evolving the Architecture of a Multi-Part Program in
                 Genetic Programming Using Architecture-Altering
                 Operations",
  booktitle =    "Evolutionary Programming {IV} Proceedings of the
                 Fourth Annual Conference on Evolutionary Programming",
  year =         "1995",
  editor =       "John Robert McDonnell and Robert G. Reynolds and David
                 B. Fogel",
  pages =        "695--717",
  address =      "San Diego, CA, USA",
  month =        "1-3 " # mar,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming, ADF",
  ISBN =         "0-262-13317-2",
  size =         "23 pages",
  notes =        "EP-95, Like koza:1994:aao",
}

@Misc{koza:1995:hcbgs,
  author =       "John R. Koza",
  title =        "A Response to the {ML}-95 paper entitled ``{H}ill
                 climbing beats genetic search on a Boolean circuit
                 synthesis of {K}oza's''",
  howpublished = "Distributed 11 July 1995 at the 1995 International
                 Machine Learning Conference in Tahoe City, California,
                 USA",
  year =         "1995",
  month =        "11 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/jktahoe24page.html#anchor5865650",
  size =         "24 pages",
  notes =        "See lang:1995:hcbgs",
}

@InProceedings{Koza:1995:2ss,
  author =       "John R. Koza",
  title =        "Two Ways of Discovering the Size and Shape of a
                 Computer Program to Solve a Problem",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "287--294",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-370-0",
}

@InProceedings{koza:1995:protien,
  author =       "John R. Koza and David Andre",
  title =        "Automatic Discovery Using Genetic Programming of an
                 Unknown-Sized Detector of Protein Motifs Containing
                 Repeatedly-used Subexpressions",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "89--97",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  size =         "9 pages",
  abstract =     "GP successfully evolved code for detecting the D-E-A-D
                 box family of protiens which worked as well or better
                 than human written code",
  notes =        "part of rosca:1995:ml",
}

@InProceedings{koza:1995:gendup,
  author =       "John R. Koza",
  title =        "Gene Duplication to Enable Genetic Programming to
                 Concurrently Evolve Both the Architecture and
                 Work-Performing Steps of a Computer Program",
  booktitle =    "IJCAI-95 Proceedings of the Fourteenth International
                 Joint Conference on Artificial Intelligence",
  year =         "1995",
  volume =       "1",
  pages =        "734--740",
  address =      "Montreal, Quebec, Canada",
  publisher_address = "San Francisco, CA, USA",
  month =        "20-25 " # aug,
  organisation = "IJCAII,AAAI,CSCSI",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-363-8",
}

@InProceedings{koza:1995:adpm,
  author =       "John R. Koza and David Andre",
  title =        "Automated discovery of protein motifs with genetic
                 programming",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "38--49",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InProceedings{koza:1995:earch,
  author =       "John R. Koza and David Andre",
  title =        "Evolution of Both the Architecture and the Sequence of
                 Work-Performing Steps of a Computer Program Using
                 Genetic Programming with Architecture-Altering
                 Operations",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "50--60",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP{\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/ Transmembrane protien
                 classification {"}out of sample error rate that was
                 better than that previously reported for other
                 previously repored human-written algorithms{"} [p50]",
}

@Book{koza:1995:gagp,
  title =        "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  address =      "Stanford, California, 94305-3079 USA, Phone
                 415-329-1217 or 800-533-2670",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford University Bookstore",
  email =        "mailorder@bookstore.stanford.edu",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  URL =          "http://www.genetic-programming.org/gpstanfordpapers.html",
  notes =        "This volume (ISBN 0-18-195720-5) contains 34 papers
                 written and submitted by students describing their term
                 projects for the course {"}Genetic Algorithms and
                 Genetic Programming{"} (Computer Science 426) at
                 Stanford University offered during the fall quarter
                 1995 (both on campus and on SITN TV). The appendix to
                 this volume contains material providing basic
                 information about the course, including schedules,
                 reading lists, project instructions, and the take-home
                 final. In the take-home final examination in this
                 course, each student {"}peer reviews{"} 4 papers
                 written by other students in the class. Copies of the
                 1995 volume (ISBN 0-18-195720-5) are available DIRECTLY
                 from Stanford University Bookstore for $14.96 plus
                 $6.00 shipping and handling (in the USA) by calling
                 415-329-1217 or 800-533-2670 or by writing Stanford
                 Bookstore Stanford University Stanford, California
                 94305-3079 USA The E-Mail address of the bookstore for
                 e-mail orders is mailorder@bookstore.stanford.edu. Be
                 sure to mention the ISBN number, exact title, refer to
                 {"}Custom Publishing{"} and {"}CSD 000{"} when ordering
                 to avoid confusion with course readers, collections of
                 student papers, and other materials associated with my
                 courses at Stanford. A course reader entitled Course
                 Reader for Computer Science 426 (Genetic Algorithms)
                 for Fall Quarter 1995 (ISBN 0-18-192183-9) contains 10
                 selected papers from the current genetic algorithms and
                 genetic programming literature to supplement the two
                 textbooks used in the CS 426 course.
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InCollection{koza:1995:GPem,
  author =       "John Koza",
  title =        "Genetic Programming for Economic Modelling",
  booktitle =    "Intelligent Systems for Finance and Business",
  publisher =    "John Wiley \& Sons",
  year =         "1995",
  editor =       "Suran Goonatilake and Philip Treleaven",
  chapter =      "14",
  address =      "605 Third Avenue, New York, NY 10158-0012, USA",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.cs.ucl.ac.uk/staff/S.Goonatilake/busbook.html
                 contains info on book",
}

@InCollection{koza:1996:aigp2,
  author =       "John R. Koza and David Andre",
  title =        "Classifying Protein Segments as Transmembrane Domains
                 Using Architecture-Altering Operations in Genetic
                 Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "155--176",
  chapter =      "8",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "The biological theory of gene duplication, concerning
                 how new structures and new behaviors are created in
                 living things, is brought to bear on the problem of
                 automated architecture discovery in genetic
                 programming. Using architecture-altering operations
                 patterned after naturally-occurring gene duplication,
                 genetic programming is used to evolve a computer
                 program to classify a given protein segment as being a
                 transmembrane domain or non-transmembrane area of the
                 protein. The out-of-sample error rate for the best
                 genetically-evolved program achieved was slightly
                 better than that of previously-reported human-written
                 algorithms for this problem. This is an instance of an
                 automated machine learning algorithm rivaling a
                 human-written algorithm for a problem.",
}

@InProceedings{koza:1996:eiGP,
  author =       "John R. Koza and David Andre",
  title =        "Evolution of iteration in genetic programming",
  booktitle =    "Evolutionary Programming V: Proceedings of the Fifth
                 Annual Conference on Evolutionary Programming",
  year =         "1996",
  editor =       "Lawrence J. Fogel and Peter J. Angeline and Thomas
                 Baeck",
  address =      "San Diego",
  publisher_address = "Cambridge, MA, USA",
  month =        feb # " 29-" # mar # " 3",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-06190-2",
  abstract =     "The solution to many problems requires, or is
                 facilitated by, the use of iteration. Moreover, because
                 iterative steps are repeatedly executed, they must have
                 some degree of generality. An automatic programming
                 system should require that the user make as few
                 problem-specific decisions as possible concerning the
                 size, shape, and character of the ultimate solution to
                 the problem. Work first presented at the Fourth Annual
                 Conference on Evolutionary Programming in 1995 (EP-95)
                 demonstrated that six then-new architecture-altering
                 operations made it possible to automate the decision
                 about the architecture of an overall program
                 dynamically during a run of genetic programming. The
                 question arises as to whether it is also possible to
                 automate the decision about whether to employ
                 iteration, how much iteration to employ, and the
                 particular sequence of iterative steps. This paper
                 introduces the new operation of restricted iteration
                 creation that automatically creates a restricted
                 iteration-performing branch out of a portion of an
                 existing computer program during a run a genetic
                 programming. Genetic programming with the new operation
                 is then used (in conjunction with the other
                 architecture-altering operations first presented at
                 EP-95) to evolve a computer program to solve a
                 non-trivial problem",
  notes =        "EP-96 http://www.natural-selection.com/eps/EP96.html",
}

@InProceedings{koza:1996:eeaGP,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Toward evolution of electronic animals using genetic
                 programming",
  booktitle =    "Artificial Life V: Proceedings of the Fifth
                 International Workshop on the Synthesis and Simulation
                 of Living Systems",
  year =         "1996",
  publisher_address = "Cambridge, MA, USA",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/alife-animals-96.ps",
  abstract =     "This paper describes an automated process for
                 designing an optimal food-foraging controller for a
                 lizard. The controller consists of an analog electrical
                 circuit that is evolved using the principles of natural
                 selection, sexual recombination, and developmental
                 biology. Genetic programming creates both the topology
                 of the controller circuit and the numerical values for
                 each electrical component.",
  notes =        "ALIFE-V",
}

@InProceedings{koza:1996:WYWIWYG,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Automated {WYWIWYG} Design of Both the Topology and
                 Component Values of Electrical Circuits Using Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "123--131",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/gp96.nielsen.ps",
  size =         "9 pages",
  abstract =     "This paper describes an automated process for
                 designing electrical circuits in which {"}What You Want
                 Is What You Get{"} ({"}WYWIWYG{"} - pronounced
                 {"}wow-eee-wig{"}). The design process uses genetic
                 programming to produce both the topology of the desired
                 circuit and the sizing (numerical values) for all the
                 components of a circuit. Genetic programming
                 successfully evolves both the topology and the sizing
                 for an asymmetric bandpass filter that was described as
                 being difficult-to-design in a leading electrical
                 engineering journal. This evolved circuit is another
                 instance in which a genetically evolved solution to a
                 non- trivial problem is competitive with human
                 performance.",
  notes =        "GP-96",
}

@InProceedings{koza:1996:ADFaacs,
  author =       "John R. Koza and David Andre and Forrest H {Bennett
                 III} and Martin A. Keane",
  title =        "Use of Automatically Defined Functions and
                 Architecture-Altering Operations in Automated Circuit
                 Synthesis Using Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "132--149",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/gp96.adf.ps",
  size =         "9 pages",
  abstract =     "This paper demonstrates the usefulness of
                 automatically defined functions and
                 architecture-altering operations in designing analog
                 electrical circuits using genetic programming. A design
                 for a lowpass filter is genetically evolved in which an
                 automatically defined function is profitably reused in
                 the 100% compliant circuit. The symmetric reuse of an
                 evolved substructure directly enhances the performance
                 of the circuit. Genetic programming rediscovered the
                 classical ladder topology used in Butterworth and
                 Chebychev filters as well as the more complex topology
                 used in Cauer (elliptic) filters. A design for a
                 double-passband filter is genetically evolved in which
                 the architecture- altering operations discover a
                 suitable program architecture dynamically during the
                 run. Two automatically defined functions are profitably
                 reused in the genetically evolved 100% complaint
                 circuit.",
  notes =        "GP-96",
}

@Proceedings{koza:gp96,
  title =        "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/GP96MITproceedings.html",
  notes =        "GP-96",
}

@InProceedings{koza:1996:4problems,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Four problems for which a computer program evolved by
                 genetic programming is competitive with human
                 performance",
  booktitle =    "Proceedings of the 1996 IEEE International Conference
                 on Evolutionary Computation",
  year =         "1996",
  volume =       "1",
  pages =        "1--10",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/icec-96.ps",
  size =         "10 pages",
  abstract =     "It would be desirable if computers could solve
                 problems without the need for a human to write the
                 detailed programmatic steps. That is, it would be
                 desirable to have a domain-independent automatic
                 programming technique in which {"}What You Want Is What
                 You Get{"} ({"}WYWIWYG{"} pronounced
                 {"}wow-eee-wig{"}). Genetic programming is such a
                 technique. This paper surveys three recent examples of
                 problems (from the fields of cellular automata and
                 molecular biology) in which genetic programming evolved
                 a computer program that produced results that were
                 slightly better than human performance for the same
                 problem. This paper then discusses the problem of
                 electronic circuit synthesis in greater detail. It
                 shows how genetic programming can evolve both the
                 topology of a desired electrical circuit and the sizing
                 (numerical values) for each component in a crossover
                 (woofer and tweeter) filter. Genetic programming has
                 also evolved the design for a lowpass filter, the
                 design of an amplifier, and the design for an
                 asymmetric bandpass filter that was described as being
                 difficult-to-design in an article in a leading
                 electrical engineering journal.",
  notes =        "ICEC-96 Population 640,000 on filter problem, 1%
                 mutation. 64 demes, torrodally connected at end of each
                 generation. 4 emigrant boats of 2% per generation.",
}

@Proceedings{koza:LBgp96,
  title =        "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  editor =       "John R. Koza",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/GP96latebreaking.html",
  size =         "210 pages",
  notes =        "GP-96LB In order to provide conference attendees at
                 the Genetic Programming 1996 Conference (GP-96) held at
                 Stanford University on July 28 P 31, 1996 (Sunday P
                 Wednesday) with information about research that was
                 initiated, enhanced, improved, or completed after the
                 original paper submission deadline of January 10, 1996,
                 this rapidly-printed book of 27 late-breaking papers is
                 being distributed to all attendees (in addition to the
                 official conference proceedings published by the MIT
                 Press). The deadline for late-breaking papers was July
                 3, 1996. Late-breaking papers were briefly examined for
                 minimum standards of acceptability and relevance, but
                 were not peer reviewed or evaluated by the conference
                 organizers. This volume (ISBN 0-18-201-031-7) may be
                 purchased directly from the Custom Publishing
                 Department of the Stanford University Bookstore by
                 calling 415-329-1217 or 800-533-2670 or by writing
                 Custom Publishing Department, Stanford Bookstore,
                 Stanford University, Stanford, California 94305-3079
                 USA. The E-Mail address of the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu. The price
                 is $9.54 plus $6.00 shipping and handling (in the
                 USA).",
}

@InProceedings{koza:1996:eldld60db,
  author =       "John R. Koza and David Andre and Forrest H {Bennett
                 III} and Martin A. Keane",
  title =        "Evolution of a Low-Distortion, Low-Bias 60 Decibel Op
                 Amp with Good Frequency Generalization using Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "94--100",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/gp96.late.ps",
  abstract =     "Genetic programming was used to evolve both the
                 topology and the sizing (numerical values) for each
                 component of a low-distortion, low-bias 60 decibel
                 (1000-to-1) amplifier circuit with good frequency
                 generalization. The evolved circuit was composed of two
                 types of transistors (active elements) as well as
                 resistors and capacitors.",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{koza:1996:96db,
  author =       "John R. Koza and David Andre and Forrest H {Bennett
                 III} and Martin A. Keane",
  title =        "Design of a 96 Decibel operational amplifier and other
                 problems for which a computer program evolved by
                 genetic programming is competitive with human
                 performance.",
  booktitle =    "Proceedings of l996 Japan-China Joint International
                 Workshop on Information Systems",
  year =         "1996",
  editor =       "Mitsuo Gen and Weixuan Zu",
  pages =        "30--49",
  address =      "Ashikaga",
  month =        "4-16 " # oct,
  organisation = "Ashikaga Institute of Technology",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/Ashikaga96dB.ps",
  abstract =     "It would be desirable if computers could solve
                 problems without the need for a human to write the
                 detailed programmatic steps. That is, it would be
                 desirable to have a domain-independent automatic
                 programming technique in which {"}What You Want Is What
                 You Get{"} ({"}WYWIWYG{"} ­p; pronounced
                 {"}wow-eee-wig{"}). Genetic programming is such a
                 technique. This paper surveys three recent examples of
                 problems (one from the field of cellular automata and
                 two from the fields of molecular biology) in which
                 genetic programming evolved a computer program that
                 produced results that were slightly better than human
                 performance for the same problem. This paper then
                 discusses a fourth problem in greater detail and
                 demonstrates that a design for a low-distortion 96
                 decibel op amp (including both topology and component
                 sizing) can be evolved using genetic programming. The
                 information that the user must supply to genetic
                 programming consists of the parts bin (transistors,
                 resistors, and capacitors) and the fitness measure for
                 the major operating characteristics of an op amp.",
  notes =        "Invited paper",
}

@InProceedings{koza:1996:icesadf,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A Keane",
  title =        "Reuse, parameterized reuse, and hierarchical reuse of
                 substructures in evolving electrical circuits using
                 genetic programming",
  booktitle =    "Proceedings of International Conference on Evolvable
                 Systems: From Biology to Hardware (ICES-96)",
  year =         "1996",
  editor =       "Tetsuya Higuchi and Iwata Masaya and Weixin Liu",
  volume =       "1259",
  series =       "Lecture Notes in Computer Science",
  address =      "Tsukuba, Japan",
  publisher_address = "Berlin, Germany",
  month =        "7-8 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-63173-9",
  ISSN =         "0302-9743",
  LCCN =         "QA76.618 .I57 1996",
  bibdate =      "Mon Nov 24 10:31:37 1997",
  acknowledgement = ack-nhfb,
  URL =          "http://www-cs-faculty.stanford.edu/~koza/ICESADF.ps",
  size =         "13 pages",
  abstract =     "Most practical electrical circuits contain modular
                 substructures that are repeatedly used to create the
                 overall circuit. Genetic programming with automatically
                 defined functions and architecture-altering operations
                 successfully evolved a design for a two-band crossover
                 (woofer and tweeter) filter with a crossover frequency
                 of 2,512 Hz. Both the topology and the sizing
                 (numerical values) for each component of a the circuit
                 were evolved. In the evolved circuit, three different
                 electrical substructures were used; one was invoked
                 five times; and one was invoked as part of a hierarchy;
                 and one substructure was invoked with different
                 numerical arguments so that different numerical
                 component values were assigned to the substructure's
                 components.",
  notes =        "URL=version 1 as presented at the conference
                 http://www.etl.go.jp:8080/etl/kikou/ICES96/",
}

@InProceedings{koza:1996:adtsaec,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A Keane",
  title =        "Automated design of both the topology and sizing of
                 analog electrical circuits using genetic programming",
  booktitle =    "Artificial Intelligence in Design '96",
  year =         "1996",
  editor =       "John S. Gero and Fay Sudweeks",
  pages =        "151--170",
  address =      "Dordrecht",
  publisher =    "Kluwer Academic",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper describes an automated process for
                 designing analog electrical circuits based on the
                 principles of natural selection, sexual recombination,
                 and developmental biology. The design process starts
                 with the random creation of a large population of
                 program trees composed of circuit-constructing
                 functions. Each program tree specifies the steps by
                 which a fully developed circuit is to be progressively
                 developed from a common embryonic circuit appropriate
                 for the type of circuit that the user wishes to design.
                 Each fully developed circuit is translated into a
                 netlist, simulated using a modified version of SPICE,
                 and evaluated as to how well it satisfies the user's
                 design requirements. The fitness measure is a
                 user-written computer program that may incorporate any
                 calculable characteristic or combination of
                 characteristics of the circuit, including the circuit's
                 behavior in the time domain, its behavior in the
                 frequency domain, its power consumption, the number of
                 components, cost of components, or surface area
                 occupied by its components. The population of program
                 trees is genetically bred over a series of many
                 generations using genetic programming. Genetic
                 programming is driven by a fitness measure and employs
                 genetic operations such as Darwinian reproduction,
                 sexual recombination (crossover), and occasional
                 mutation to create offspring. This automated
                 evolutionary process produces both the topology of the
                 circuit and the numerical values for each component.
                 This paper describes how genetic programming can evolve
                 the circuit for a difficult-to-design low-pass
                 filter.",
}

@InCollection{koza:1996:adpmECTA,
  author =       "John R. Koza and David Andre",
  title =        "Automatic discovery of protein motifs using genetic
                 programming",
  booktitle =    "Evolutionary Computation: Theory and Applications",
  publisher =    "World Scientific",
  year =         "1996",
  editor =       "Xin Yao",
  address =      "Singapore",
  note =         "In Press 1997?",
  keywords =     "genetic algorithms, genetic programming, DEAD box,
                 SWISSPROT, PROSITE",
  URL =          "http://www.genetic-programming.com/ECTA.ps",
  abstract =     "Automated methods of machine learning may prove to be
                 useful in discovering biologically meaningful
                 information hidden in the rapidly growing databases of
                 DNA sequences and protein sequences. Genetic
                 programming is an extension of the genetic algorithm in
                 which a population of computer programs is bred, over a
                 series of generations, in order to solve a problem.
                 Genetic programming is capable of evolving complicated
                 problem-solving expressions of unspecified size and
                 shape. Moreover, when automatically defined functions
                 are added to genetic programming, genetic programming
                 becomes capable of efficiently capturing and exploiting
                 recurring sub-patterns. This chapter describes how
                 genetic programming with automatically defined
                 functions successfully evolved motifs for detecting the
                 D-E-A-D box family of proteins and for detecting the
                 manganese superoxide dismutase family. Both motifs were
                 evolved without prespecifying their length. Both
                 evolved motifs employed automatically defined functions
                 to capture the repeated use of common subexpressions.
                 When tested against the SWISS-PROT database of
                 proteins, the two genetically evolved consensus motifs
                 detect the two families either as well, or slightly
                 better than, the comparable human-written motifs found
                 in the PROSITE database.",
  notes =        "ECTA, two ADFs each has OR in function set (ie
                 combination of 2 alternative amino acids at this
                 point). Result producing branch has AND (ie two
                 adjacent (along backbone) amino-acids (or sets of
                 aacids)). Covariance fitness, formula in terms of
                 number true positives etc.

                 Jury method: 12 motives evolved by separate GP runs
                 combined into one by requiring unanimous jury decision.
                 (Combined by hand or automatically?)

                 Parallel GP system, 64 transputer nodes.",
}

@InProceedings{koza:1996:biscsp,
  author =       "John R. Koza and David Andre",
  title =        "A case study where biology inspired a solution to a
                 computer science problem",
  booktitle =    "Pacific Symposium on Biocomputing '96",
  year =         "1996",
  editor =       "Lawrence Hunter and Teri E. Klein",
  pages =        "500--511",
  publisher_address = "Singapore",
  publisher =    "World Scientific",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "PSB 96",
}

@Article{koza:1997:evonews,
  author =       "John R. Koza",
  title =        "Genetic Programming: Automatic Programming of
                 Computers",
  journal =      "EvoNews",
  year =         "1997",
  volume =       "1",
  number =       "3",
  pages =        "4--7",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Introduction and survey of GP",
  notes =        "EvoNews - The Newsletter of EvoNet at
                 http://www.dcs.napier.ac.uk/evonet/evonews.htm",
}

@InProceedings{koza:1997:3sfd,
  author =       "John R. Koza and Forrest H {Bennett III} and Jason
                 Lohn and Frank Dunlap and Martin A. Keane and David
                 Andre",
  title =        "Evolution of tri-state Frequency Discriminator for the
                 Source Identification Problem using Genetic
                 Programming",
  booktitle =    "International Conference of Information Sciences",
  year =         "1997",
  editor =       "Paul P. Wang",
  volume =       "1",
  pages =        "95--99",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/FEAfre.ps",
  size =         "2",
  abstract =     "Automated synthesis of analog electronic circuits is
                 recognized as a difficult problem. Genetic programming
                 was used to evolve both the topology and the sizing
                 (numerical values) for each component of a circuit that
                 can perform source identification by correctly classify
                 an incoming signal into categories.",
  notes =        "FEA-97

                 ",
}

@InProceedings{koza:1997:96db,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Evolution Using Genetic Programming of a
                 Low-Distortion 96 Decibel Operational Amplifier",
  booktitle =    "Proceedings of the 1997 ACM Symposium on Applied
                 Computing",
  year =         "1997",
  editor =       "Barrett Bryant and Janice Carroll and Dave Oppenheim
                 and Jim Hightower and K. M. George",
  pages =        "207--216",
  address =      "Hyatt Sainte Claire Hotel, San Jose, California, USA",
  publisher_address = "New York",
  month =        "28 " # feb # "-2 " # mar,
  organisation = "Association for Computing Machinery",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/SACamp.ps",
  abstract =     "There is no known general technique for automatically
                 designing an analog electrical circuit that satisfies
                 design specifications. Genetic programming was used to
                 evolve both the topology and the sizing (numerical
                 values) for each component of a low-distortion 96
                 decibel (64,860-to-1) amplifier circuit.",
  notes =        "ACM SAC-97",
}

@InProceedings{koza:1997:ascc,
  author =       "John R. Koza and Forrest H {Bennett III} and Jason
                 Lohn and Frank Dunlap and Martin A. Keane and David
                 Andre",
  title =        "Automated Synthesis of Computational Circuits Using
                 Genetic Programming",
  booktitle =    "Proceedings of the 1997 {IEEE} International
                 Conference on Evolutionary Computation",
  year =         "1997",
  pages =        "447--452",
  address =      "Indianapolis",
  publisher_address = "Piscataway, NJ, USA",
  month =        "13-16 " # apr,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/ICECcomp.ps",
  abstract =     "Analog electrical circuits that perform mathematical
                 functions (e.g., cube root, square) are called
                 computational circuits. Computational circuits are of
                 special practical importance when the small number of
                 required mathematical functions does not warrant
                 converting an analog signal into a digital signal,
                 performing the mathematical function in the digital
                 domain, and then converting the result back to the
                 analog domain. The design of computational circuits is
                 difficult even for mundane mathematical functions and
                 often relies on the clever exploitation of some aspect
                 of the underlying device physics of the components.
                 Moreover, implementation of each different mathematical
                 function typically requires an entirely different
                 clever insight. This paper demonstrates that
                 computational circuits can be designed without such
                 problem-specific insights using a single uniform
                 approach involving genetic programming. Both the
                 circuit topology and the sizing of all circuit
                 components are created by genetic programming. This
                 uniform approach to the automated synthesis of
                 computational circuits is illustrated by evolving
                 circuits that perform the cube root function (for which
                 no circuit was found in the published literature) as
                 well as for the square root, square, and cube
                 functions.",
  notes =        "ICEC-97, cube root, square, embryonic circuit",
}

@InProceedings{koza:1997:dhgopacGP,
  author =       "John R. Koza and David Andre and Forrest H {Bennett
                 III} and Martin A. Keane",
  title =        "Design of a high-gain operational amplifier and other
                 circuits by means of genetic programming",
  booktitle =    "Evolutionary Programming VI. 6th International
                 Conference, EP97",
  year =         "1997",
  editor =       "Peter J. Angeline and Robert G. Reynolds and John R.
                 McDonnell and Russ Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  pages =        "125--136",
  address =      "Indianapolis, Indiana, USA",
  publisher_address = "Berlin",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/EPamp.ps",
  abstract =     "This paper demonstrates that a design for a
                 low-distortion high-gain 96 decibel (64,860-to-1)
                 operational amplifier (including both circuit topology
                 and component sizing) can be evolved using genetic
                 programming.",
  notes =        "EP-97",
}

@Book{koza:1997:GAGPs,
  editor =       "John R. Koza",
  title =        "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  URL =          "http://www.genetic-programming.org/gpstanfordpapers.html",
  notes =        "Thu, 27 Mar 1997 07:03:18 PST

                 This volume (ISBN 0-18-205981-2) contains 24 papers
                 written and submitted by students describing their term
                 projects for the course {"}Genetic Algorithms and
                 Genetic Programming{"} (Computer Science 426) at
                 Stanford University offered during the winter quarter
                 1997 (both on campus and on SITN TV). The appendix to
                 this volume contains material providing basic
                 information about the course, including schedules,
                 reading lists, project instructions, and the take-home
                 final. In the take-home final examination in this
                 course, each student {"}peer reviews{"} 4 papers
                 written by other students in the class. Copies of the
                 1997 volume (ISBN 0-18-205981-2) are available DIRECTLY
                 from Stanford University Bookstore for $12.54 plus
                 $6.00 shipping and handling (in the USA) by calling
                 415-329-1217 or 800-533-2670 or by writing Stanford
                 Bookstore Stanford University Stanford, California
                 94305-3079 USA

                 The E-Mail address of the bookstore for e-mail orders
                 is mailorder@bookstore.stanford.edu. The WWW URL for
                 the Stanford Bookstore is
                 http://bookstore.stanford.edu/.

                 Be sure to mention the ISBN number, exact title, refer
                 to {"}Custom Publishing{"} and {"}CSD 000{"} when
                 ordering to avoid confusion with course readers,
                 collections of student papers, and other materials
                 associated with my courses at Stanford.",
}

@InProceedings{koza:1997:etofccGP,
  author =       "John R. Koza and Forrest H {Bennett III} and Martin A.
                 Keane and David Andre",
  title =        "Evolution of a Time-Optimal Fly-To Controller Circuit
                 using Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "207--212",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/GPfly.ps",
  abstract =     "Most problem-solving techniques used by engineers
                 involve the introduction of analytical and mathematical
                 representations and techniques that are entirely
                 foreign to the problem at hand. Genetic programming
                 offers the possibility of solving problems in a more
                 direct way using the given ingredients of the problem.
                 This idea is explored by considering the problem of
                 designing an electrical controller to implement a
                 solution to the time-optimal fly-to control problem.",
  notes =        "GP-97",
}

@InProceedings{koza:1997:aptorcaect,
  author =       "John R. Koza and Forrest H {Bennett III} and Martin A.
                 Keane and David Andre",
  title =        "Automatic programming of a time-optimal robot
                 controller and an analog electrical circuit to
                 implement the robot controller by means of genetic
                 programming",
  booktitle =    "Proceedings of 1997 IEEE International Symposium on
                 Computational Intelligence in Robotics and Automation",
  year =         "1997",
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "340--346",
  address =      "Los Alamitos, CA, USA",
  publisher =    "Computer Society Press",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/CIRAfly.ps",
  abstract =     "Genetic programming is an automatic programming
                 technique that evolves computer programs to solve, or
                 approximately solve, problems. This paper presents two
                 examples in which genetic programming creates a
                 computer program for controlling a robot so that the
                 robot moves to a specified destination point in minimal
                 time. In the first approach, genetic programming
                 evolves a computer program composed of ordinary
                 arithmetic operations and conditional operations to
                 implement a time-optimal control strategy. In the
                 second approach, genetic programming evolves the design
                 of an analog electrical circuit consisting of
                 transistors, diodes, resistors, and power supplies to
                 implement a near-optimal control strategy.",
  notes =        "IEEE CIRA-97",
}

@InProceedings{koza:1997:aaoda3asic,
  author =       "John R. Koza and Forrest H {Bennett III} and Jason
                 Lohn and Frank Dunlap and Martin A. Keane and David
                 Andre",
  title =        "Use of Architecture-Altering Operations to Dynamically
                 Adapt a Three-Way Analog Source Identification Circuit
                 to Accommodate a New Source",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "213--221",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/GPway.ps",
  abstract =     "The problem of source identification involves
                 correctly classifying an incoming signal into a
                 category that identifies the signal's source. The
                 problem is difficult because information is not
                 provided concerning each source's distinguishing
                 characteristics and because successive signals from the
                 same source differ. The source identification problem
                 can be made more difficult by dynamically changing the
                 repertoire of sources while the problem is being
                 solved. We used genetic programming to evolve both the
                 topology and the sizing (numerical values) for each
                 component of an analog electrical circuit that can
                 correctly classify an incoming analog electrical signal
                 into three categories. Then, the repertoire of sources
                 was dynamically changed by adding a new source during
                 the run. The paper describes how the
                 architecture-altering operations enabled genetic
                 programming to adapt, during the run, to the changed
                 environment. Specifically, a three-way source
                 identification circuit was evolved and then adapted
                 into a four-way classifier, during the run, thereby
                 successfully handling the additional new source.",
  notes =        "GP-97",
}

@Proceedings{koza:gp97,
  title =        "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  year =         "1997",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "13-16 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.mkp.com/books_catalog/1-55860-483-9.asp",
  size =         "pages",
  notes =        "GP-97",
}

@Proceedings{koza:gp97lb,
  title =        "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  URL =          "http://www.genetic-programming.org/gp97latebreaking.html",
  size =         "pages",
  abstract =     "This book containing 38 late-breaking papers and 16
                 one-page summaries of PhD thesis work in progress was
                 distributed to all attendees of the Genetic Programming
                 1997 Conference (GP-97) held at Stanford University on
                 July 13 - 16, 1997 (Sunday &shyp; Wednesday). This
                 rapidly-printed book was distributed in addition to the
                 peer-reviewed conference proceedings book published by
                 Morgan Kaufmann Publishers of San Francisco. The 38
                 late-breaking papers describe research that was
                 initiated, enhanced, improved, or completed after the
                 conference's original paper submission deadline of
                 January 8, 1997. The deadline for late-breaking papers
                 was June 11, 1997. Late-breaking papers were briefly
                 examined for minimum standards of acceptability and
                 relevance, but were not peer reviewed or evaluated by
                 the conference organizers. The statements and opinions
                 contained in these papers are solely those of the
                 authors and not those of the editor or Genetic
                 Programming Conferences, Inc. (a California
                 not-for-profit corporation), the AAAI, or the Stanford
                 Bookstore. Late-breaking papers were presented as part
                 of the poster session on the evening of Monday July 14,
                 1997. The 16 one-page summaries of PhD thesis work in
                 progress were provided by the 16 students who presented
                 their work at the PhD Student Workshop held on Saturday
                 July 12, 1997 (the day before the start of the
                 conference).",
  notes =        "GP-97LB",
}

@InProceedings{Koza:1997:rrFPGAafeGP,
  author =       "John R. Koza and Forrest H {Bennett III} and Jeffrey
                 L. Hutchings and Stephen L. Bade and Martin A. Keane
                 and David Andre",
  title =        "Rapidly Reconfigurable Field-Programmable Gate Arrays
                 for Accelerating Fitness Evaluation in Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "121--131",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/GPfgpa.ps",
  abstract =     "The dominant component of the computational burden of
                 solving non-trivial problems with evolutionary
                 algorithms is the task of measuring the fitness of each
                 individual in each generation of the evolving
                 population. The advent of rapidly reconfigurable
                 field-programmable gate arrays (FPGAs) and the idea of
                 evolvable hardware opens the possiblity of embodying
                 each individual of the evolving population into
                 hardware for the purpose of accelerating the
                 time-consuming fitness evaluation task This paper
                 demonstrates how the massive parallelism of the rapidly
                 reconfigurable Xilinx XC6216 FPGA can be exploited to
                 accelerate the computationally burdensome fitness
                 evaluation task of genetic programming. The work was
                 done on Virtual Computing Corporation's low-cost HOTS
                 expansion board for PC type computers. A 16-step
                 7-sorter was evolved that has two fewer steps than the
                 sorting network described in the 1962 O'Connor and
                 Nelson patent on sorting networks and that has the same
                 number of steps as the minimal 7-sorter that was
                 devised by Floyd and Knuth subsequent to the patent.",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InCollection{koza:1997:fwpaGP,
  author =       "John R. Koza",
  title =        "Future work and practical applications of genetic
                 programming",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and D. B. Fogel and Z. Michalewicz",
  pages =        "H1.1--1--6",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  URL =          "http://www.genetic-programming.com/HECfuture.ps",
  abstract =     "Genetic programming is a relatively new
                 domain-independent method for evolving computer
                 programs to solve problems. This chapter suggests
                 avenues for possible future research on genetic
                 programming, opportunities to extend the technique, and
                 areas for possible practical applications.",
  notes =        "Survey",
  size =         "6 pages",
}

@InCollection{koza:1997:cpstdGP,
  author =       "John R. Koza",
  title =        "Classifying protein segments as transmembrane domains
                 using genetic programming and architecture-altering
                 operations",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and D. B. Fogel and Z. Michalewicz",
  pages =        "G6.1:1--5",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  URL =          "http://www.genetic-programming.com/HECtm.ps",
  abstract =     "The goal of automatic programming is to create, in an
                 automated way, a computer program that enables a
                 computer to solve a problem. Ideally, an automatic
                 programming system should require that the user
                 pre-specify as little as possible about the problem. In
                 particular, it is desirable that the user not be
                 required to specify the size and shape (i.e., the
                 architecture) of the ultimate solution to the problem
                 before applying the technique. This paper describes how
                 the biological theory of gene duplication described in
                 Susumu Ohno's provocative book, Evolution by Means of
                 Gene Duplication, was brought to bear on a vexatious
                 problem from the domain of automated machine learning
                 in the computer science field. The resulting
                 biologically-motivated approach using six new
                 architecture-altering operations enables genetic
                 programming to automatically discover the size and
                 shape of the solution at the same time as it is
                 evolving a solution to the problem

                 Genetic programming with the architecture-altering
                 operations was used to evolve a computer program to
                 classify a given protein segment as being a
                 transmembrane domain or non-transmembrane area of the
                 protein (without biochemical knowledge, such as
                 hydrophobicity values). The best genetically-evolved
                 program achieved an out-of-sample error rate that was
                 better than that reported for other previously reported
                 human-constructed algorithms. This is an instance of an
                 automated machine learning algorithm that is
                 competitive with human performance on a non-trivial
                 problem.",
}

@Article{koza:1997:asaecmGP,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane and Frank Dunlap",
  title =        "Automated Synthesis of Analog Electrical Circuits by
                 Means of Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "1997",
  volume =       "1",
  number =       "2",
  pages =        "109--128",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, analogue
                 circuit syntehesis, design automation, electrical
                 cicuits",
  ISSN =         "1089-778X",
  URL =          "http://www.genetic-programming.com/IEEETEC.ps",
  size =         "20 pages",
  abstract =     "The design (synthesis) of analog electrical circuits
                 starts with a high-level statement of the circuit's
                 desired behavior and requires creating a circuit that
                 satisfies the specified design goals. Analog circuit
                 synthesis entails the creation of both the topology and
                 the sizing (numerical values) of all of the circuit's
                 components. The difficulty of the problem of analog
                 circuit synthesis is well known and there is no
                 previously known general automated technique for
                 synthesizing an analog circuit from a high-level
                 statement of the circuit's desired behavior. This paper
                 presents a single uniform approach using genetic
                 programming for the automatic synthesis of both the
                 topology and sizing of a suite of eight different
                 prototypical analog circuits, including a lowpass
                 filter, a crossover (woofer and tweeter) filter, a
                 source identification circuit, an amplifier, a
                 computational circuit, a time-optimal controller
                 circuit, a temperature-sensing circuit, and a voltage
                 reference circuit. The problem-specific information
                 required for each of the eight problems is minimal and
                 consists primarily of the number of inputs and outputs
                 of the desired circuit, the types of available
                 components, and a fitness measure that restates the
                 high-level statement of the circuit's desired behavior
                 as a measurable mathematical quantity. The eight
                 genetically evolved circuits constitute an instance of
                 an evolutionary computation technique producing results
                 on a task that is usually thought of as requiring human
                 intelligence. The fact that a single uniform approach
                 yielded a satisfactory design for each of the eight
                 circuits as well as the fact that a satisfactory design
                 was created on the first or second run of each problem
                 are evidence for the general applicability of genetic
                 programming for solving the problem of automatic
                 synthesis of analog electrical circuits.",
}

@InProceedings{koza:1997:ASILIMOAR,
  author =       "John R. Koza and Forrest H {Bennett III} and Jeffrey
                 L. Hutchings and Stephen L. Bade and Martin A. Keane
                 and David Andre",
  title =        "Evolving sorting networks using genetic programming
                 and the rapidly reconfigurable Xilinx 6216
                 field-programmable gate array",
  booktitle =    "Proceedings of the 31st Asilomar Conference on
                 Signals, Systems, and Computers",
  year =         "1997",
  publisher =    "IEEE Press",
  note =         "In Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/ASILIMOAR97.ps",
  abstract =     "This paper describes how the massive parallelism of
                 the rapidly reconfigurable Xilinx XC6216 FPGA (in
                 conjunction with Virtual Computing Corporation's H.O.T.
                 Works board) can be exploited to accelerate the
                 computationally burdensome fitness measurement task of
                 genetic algorithms and genetic programming. This
                 acceleration is accomplished by embodying each
                 individual of the evolving population into hardware in
                 order to perform this time-consuming fitness
                 measurement task. A 16-step sorting network for seven
                 items was evolved that has two fewer steps than the
                 sorting network described in the 1962 O'Connor and
                 Nelson patent on sorting networks (and the same number
                 of steps as a 7-sorter that was devised by Floyd and
                 Knuth subsequent to the patent and that is now known to
                 be minimal).",
}

@InProceedings{koza:1997:IJCAIWS,
  author =       "John R. Koza and Forrest H {Bennett III} and Jeffrey
                 L. Hutchings and Stephen L. Bade and Martin A. Keane
                 and David Andre",
  title =        "Evolving Sorting Networks using Genetic Programming
                 and Rapidly Reconfigurable Field-Programmable Gate
                 Arrays",
  booktitle =    "Workshop on Evolvable Systems. International Joint
                 Conference on Artificial Intelligence",
  year =         "1997",
  editor =       "Tetsuya Higuchi",
  pages =        "27--32",
  address =      "Nagoya",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/IJCAIWS97.ps",
  abstract =     "This paper describes ongoing work involving the use of
                 the Xilinx XC6216 rapidly reconfigurable
                 field-programmable gate array to evolve sorting
                 networks using genetic programming. We successfully
                 evolved a network for sorting seven items that employs
                 two fewer steps than the sorting network described in a
                 l962 patent and that has the same number of steps as
                 the seven-sorter devised by Floyd and Knuth subsequent
                 to the patent.",
  notes =        "IJCAI-97",
}

@InProceedings{koza:1998:ecprrFPGAgp,
  author =       "John R. Koza and Forrest H {Bennett III} and Jeffrey
                 L. Hutchings and Stephen L. Bade and Martin A. Keane
                 and David Andre",
  title =        "Evolving Computer Programs using Rapidly
                 Reconfigurable {FPGA}s and Genetic Programming",
  booktitle =    "FPGA'98 Sixth International Symposium on Field
                 Programmable Gate Arrays",
  year =         "1998",
  editor =       "Jason Cong",
  pages =        "209--219",
  address =      "Doubletree Hotel, Monterey, California, USA",
  publisher_address = "New York, NY, USA",
  month =        "22-24 " # feb,
  organisation = "ACM/SIGDA",
  publisher =    "ACM Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/FPGA98.ps",
  abstract =     "This paper describes how the massive parallelism of
                 the rapidly reconfigurable Xilinx XC6216 FPGA (in
                 conjunction with Virtual Computing's H.O.T. Works
                 board) can be exploited to accelerate the
                 time-consuming fitness measurement task of genetic
                 algorithms and genetic programming. This acceleration
                 is accomplished by embodying each individual of the
                 evolving population into hardware in order to perform
                 the fitness measurement task. A 16-step sorting network
                 for seven items was evolved that has two fewer steps
                 than the sorting network described in the 1962 O'Connor
                 and Nelson patent on sorting networks (and the same
                 number of steps as a 7-sorter that was devised by Floyd
                 and Knuth subsequent to the patent and that is now
                 known to be minimal). Other minimal sorters have been
                 evolved.",
  notes =        "FPGA'98 http://www.ece.nwu.edu/~hauck/fpga98",
}

@InProceedings{koza:1998:pmGPcpsemcl,
  author =       "John Koza and Forrest Bennett and David Andre",
  title =        "Using Programmatic Motifs and Genetic Programming to
                 Classify Protein Sequences as to Extracellular and
                 Membrane Cellular Location",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  URL =          "http://www.genetic-programming.com/EP98.ps",
  abstract =     "As newly sequenced proteins are deposited into the
                 world's ever-growing archive of protein sequences, they
                 are typically immediately tested by various algorithms
                 for clues as to their biological structure and
                 function. One question about a new protein involves its
                 cellular location &shyp; that is, where the protein
                 resides in a living organism (extracellular, membrane,
                 etc.). A human-created five-way algorithm for cellular
                 location using statistical techniques with 76% accuracy
                 was recently reported. This paper describes a two-way
                 algorithm that was evolved using genetic programming
                 with 83% accuracy for determining whether a protein is
                 extracellular and with 89% accuracy for membrane
                 proteins. Unlike the statistical calculation, the
                 genetically evolved algorithm employs a large and
                 varied arsenal of computational capabilities, including
                 arithmetic functions, conditional operations,
                 subroutines, iterations, memory, data structures,
                 set-creating operations, macro definitions, recursion,
                 etc. The genetically evolved classification algorithm
                 can be viewed as an extension (which we call a
                 programmatic motif) of the conventional notion of a
                 protein motif.",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7",
}

@InProceedings{koza:1998:cpeupmGP,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre",
  title =        "Classifying Proteins as Extracellular using
                 Programmatic Motifs and Genetic Programming",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "212--217",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/ICEC98.ps",
  file =         "c037.pdf",
  size =         "6 pages",
  abstract =     "As newly sequenced proteins are deposited into the
                 world' s ever-growing archive of protein sequences,
                 they are typically immediately tested by various
                 computerized algorithms for clues as to their
                 biological structure and function. One question about a
                 new protein involves its cellular location - that is,
                 where the protein resides in a living organism
                 (extracellular, intracellular, etc.). A 1997 paper
                 reported a human-created five-way algorithm for
                 cellular location created using statistical techniques
                 with 76% accuracy.

                 This paper describes a two-way classification algorithm
                 that was evolved using genetic programming with 83%
                 accuracy for determining whether a protein is
                 extracellular. Unlike the statistical calculation, the
                 genetically evolved algorithm employs a large and
                 varied arsenal of computational capabilities, including
                 arithmetic functions, conditional operations,
                 subroutines, iterations, memory, data structures,
                 set-creating operations, macro definitions, recursion,
                 etc. The genetically evolved classification algorithm
                 can be viewed as an extension (which we call a
                 programmatic motif) of the conventional notion of a
                 protein motif. The genetically evolved program
                 constitutes an instance of an evolutionary computation
                 technique producing a solution to a problem that is
                 competitive with that produced using human
                 intelligence.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@Proceedings{koza:gp98,
  title =        "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  address =      "University of Wisconsin, Madison, WI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems, evolutionary programming, evolvable hardware,
                 DNA computing, evolutionary robotics, evolutionary
                 strategies",
  ISBN =         "1-55860-548-7",
  size =         "892 pages",
  abstract =     "Sat, 08 Aug 1998 20:03:37 PDT contains 120 papers and
                 13 poster papers that were selected by a peer review
                 process and is available directly from Morgan Kaufmann
                 Publishers. This book may be purchased for $74.95 plus
                 shipping and handling charges and applicable sales tax
                 from the publisher. Morgan Kaufmann Publishers 340 Pine
                 Street - 6th Floor San Francisco, CA 94104 USA E-MAIL:
                 mkp@mkp.com",
  notes =        "GP-98",
}

@Proceedings{koza:gp98lb,
  title =        "Late Breaking Papers at the 1998 Genetic Programming
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, WI, USA",
  month =        "22-25 " # jul,
  publisher =    "Omni Press",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems, evolutionary programming, evolvable hardware,
                 DNA computing",
  size =         "270 pages",
  notes =        "GP-98LB 53 late breaking papers and 12 summaries of
                 PHD thesis work in progress",
}

@Book{koza:1998:GAGPs,
  editor =       "John R. Koza",
  title =        "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  publisher =    "Stanford Bookstore",
  year =         "1998",
  address =      "Stanford, California, 94305-3079 USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  URL =          "http://www.genetic-programming.org/gpstanfordpapers.html",
  abstract =     "This volume contains 19 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the winter quarter 1998.",
  notes =        "These books contain the papers written and submitted
                 by students describing their term projects for the
                 courses involved. They also contain information on the
                 structure of the courses involved. These books are
                 available from the Custom Publishing Department of the
                 Stanford Bookstore. These volumes are in the
                 Mathematics and Computer Science Library in the Main
                 Quad at Stanford University. These volumes are
                 available directly from the Stanford Bookstore by
                 calling 650-329-1217 or 800-533-2670 or by writing
                 Stanford Bookstore, Stanford University, Stanford,
                 California 94305-3079 USA. The E-Mail address of the
                 bookstore for mail orders is
                 mailorder@bookstore.stanford.edu. Be sure to mention
                 the ISBN number (or Stanford Bookstore order number),
                 the exact title, and refer to {"}Custom Publishing{"}
                 when ordering these items to avoid confusion with the
                 numerous other course readers, collections of student
                 papers, and other materials at the Stanford
                 Bookstore.",
  size =         "176 pages",
}

@InCollection{koza:1998:WYNNE,
  author =       "John R. Koza",
  title =        "Using biology to solve a problem in automated machine
                 learning",
  booktitle =    "Models of Action: Mechanisms for Adaptive Behavior",
  publisher =    "Lawrence Erlbaum Associates",
  year =         "1998",
  editor =       "Clive Wynne and John Staddon",
  chapter =      "5",
  pages =        "157--199",
  address =      "Hillsdale, NJ, USA",
  note =         "In Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/WYNNE.ps",
  abstract =     "This chapter describes how the biological theory of
                 gene duplication described in Susumu Ohno's provocative
                 book, Evolution by Means of Gene Duplication, was
                 brought to bear on a vexatious problem from the domain
                 of automated machine learning. The goal of automatic
                 programming is to create, in an automated way, a
                 computer program that enables a computer to solve a
                 problem. Ideally, an automatic programming system
                 should require that the user pre-specify little about
                 the problem environment. Genetic programming is a
                 domain-independent approach to automated machine
                 learning that attempts to evolve a computer program
                 that solves, or approximately solves, problems.
                 Starting with a primordial ooze of randomly generated
                 computer programs composed of the available
                 programmatic ingredients, genetic programming applies
                 the principles of animal husbandry (including Darwinian
                 selection and sexual recombination) to breed new (and
                 often improved) populations of computer programs. One
                 of the undesirable aspects of many techniques of
                 automated machine learning is that the user of the
                 technique may be required to specify the size and shape
                 (i.e., the architecture) of the ultimate solution to
                 his problem before he can begin to apply the technique
                 to his problem. Specification of the size and shape of
                 the solution often corresponds to discovering a way to
                 decompose the problem into useful subspaces (usually of
                 lower dimensionality) or to discovering a congenial
                 representation of the problem that facilitates solution
                 of the problem. Thus, in practice, for many problems of
                 interest, determining the size and shape of the
                 solution may be the problem (or at least a substantial
                 part of the problem). This chapter describes how
                 biology motivated a solution to the problem of
                 architecture discovery for genetic programming. The
                 resulting biologically-motivated approach enables
                 genetic programming to automatically discover the size
                 and shape of the solution at the same time as genetic
                 programming is evolving a solution to the problem. This
                 is accomplished using six new architecture-altering
                 operations that provide a way to automatically
                 discover, during a run of genetic programming, both the
                 architecture and the sequence of steps of a multi-part
                 computer program that will solve the given problem.",
  size =         "75 pages",
}

@InProceedings{koza:1998:ACDM,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Evolutionary Design of Analog Electrical Circuits
                 using Genetic Programming",
  booktitle =    "Proceedings of Adaptive Computing in Design and
                 Manufacture Conference",
  year =         "1998",
  address =      "Plymouth, England",
  month =        "21-23 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/ACDM98.ps",
  abstract =     "The design (synthesis) of analog electrical circuits
                 entails the creation of both the topology and sizing
                 (numerical values) of all of the circuit's components.
                 There has previously been no general automated
                 technique for automatically designing an analog
                 electrical circuit from a high-level statement of the
                 circuit's desired behavior. This paper shows how
                 genetic programming can be used to automate the design
                 of both the topology and sizing of a suite of five
                 prototypical analog circuits, including a lowpass
                 filter, a tri-state frequency discriminator circuit, a
                 60 dB amplifier, a computational circuit for the square
                 root, and a time-optimal robot controller circuit. All
                 five of these genetically evolved circuits constitute
                 instances of an evolutionary computation technique
                 solving a problem that is usually thought to require
                 human intelligence.",
  notes =        "ACDM-98",
}

@InProceedings{koza:1998:CISE,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Automatic creation of computer programs for designing
                 electrical circuits using genetic programming",
  booktitle =    "Computational Intelligence in Software Engineering.",
  publisher =    "World Scientific",
  year =         "1998",
  editor =       "Witold Pedrycz and James F. Peters",
  address =      "Singapore",
  note =         "In Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/CISE.ps",
  abstract =     "One of the central goals of computer science is to get
                 computers to solve problems starting from only a
                 high-level statement of the problem. The goal of
                 automating the design process bears many similarities
                 to the goal of automatically creating computer
                 programs. The design process entails creation of a
                 complex structure to satisfy user-defined requirements.
                 The design process is usually viewed as requiring human
                 intelligence. Indeed, design is a major activity of
                 practicing engineers. For these reasons, the design
                 process offers a practical yardstick for evaluating
                 automated programming (program synthesis) techniques.
                 In particular, the design (synthesis) of analog
                 electrical circuits entails the creation of both the
                 topology and sizing (numerical values) of all of a
                 circuit's components. There has previously been no
                 general automated technique for automatically designing
                 an analog electrical circuit from a high-level
                 statement of the circuit's desired behavior. This paper
                 shows how genetic programming can be used to automate
                 the design of both the topology and sizing of a suite
                 of five prototypical analog circuits, including a
                 lowpass filter, a tri-state frequency discriminator
                 circuit, a 60 dB amplifier, a computational circuit for
                 the square root, and a time-optimal robot controller
                 circuit. All five of these genetically evolved circuits
                 constitute instances of an evolutionary computation
                 technique solving a problem that is usually thought to
                 require human intelligence.",
  size =         "pages",
}

@InProceedings{koza:1998:ISER,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Fourteen instances where genetic programming has
                 produced results that are competitive with results
                 produced by humans",
  booktitle =    "Evolutionary Robotics: From Intelligent Robots to
                 Artificial Life (ER'98)",
  year =         "1998",
  editor =       "Takeshi Gomi",
  pages =        "37--76",
  address =      "Kanata, Canada",
  publisher =    "AAI Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/ISER98.ps",
}

@InProceedings{koza:1998:KENTWILLIAMS,
  author =       "John R. Koza",
  title =        "Genetic programming",
  booktitle =    "Encyclopedia of Computer Science and Technology",
  publisher =    "Marcel-Dekker",
  year =         "1998",
  editor =       "James G. Williams and Allen Kent",
  volume =       "39",
  pages =        "29--43",
  note =         "Supplement 24",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/KENTWILLIAMS98.ps",
  notes =        "This is summary of genetic programming for
                 Encyclopedia of Computer Science and Technology edited
                 by Allen Kent and James G. Williams. remove microsft
                 junk header from KENTWILLIAMS98.ps before trying to
                 print

                 Good quick survey of GP (koza and others work).",
  size =         "On line version 26 pages",
}

@InCollection{koza:1999:dacGP,
  author =       "John Koza and Forrest H {Bennet III} and David Andre
                 and Martin A. Keane",
  title =        "The Design of Analog Circuits by Means of Genetic
                 Programming",
  booktitle =    "Evolutionary Design Using Computers",
  publisher =    "Morgan Kaufmann",
  year =         "1999",
  editor =       "Peter Bentley",
  chapter =      "16",
  pages =        "365--385",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-605-X",
  URL =          "http://www.genetic-programming.com/EDC99.ps",
  URL =          "http://www.cs.ucl.ac.uk/staff/P.Bentley/evdes.html",
  abstract =     "In this chapter, genetic programming succeeded in
                 evolving both the topology and sizing of six different
                 prototypical analog electrical circuits, including a
                 lowpass filter, a highpass filter, a tri-state
                 frequency discriminator circuit, a 60 dB amplifier, a
                 computational circuit for the square root, and a
                 time-optimal robot controller circuit. All six of these
                 genetically evolved circuits constitute instances of an
                 evolutionary computation technique solving a problem
                 that is usually thought to require human intelligence.
                 There has previously been no general automated
                 technique for synthesizing an analog electrical circuit
                 from a high-level statement of the circuit's desired
                 behavior. The approach using genetic programming to the
                 problem of analog circuit synthesis is general; it can
                 be directly applied to other problems of analog circuit
                 synthesis. Each of the problems in this chapter
                 illustrates the automatic creation of a satisfactory
                 way of {"}how to do it{"} from a high-level statement
                 of {"}what needs to be done.{"}",
  notes =        "ghostview barfs at EDC99.ps 26/11/99",
}

@InCollection{koza:1999:aigp3,
  author =       "John R. Koza and Forrest H {Bennett III}",
  title =        "Automatic Synthesis, Placement, and Routing of
                 Electrical Circuits by Means of Genetic Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "6",
  pages =        "105--134",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{koza:1999:GPdim,
  author =       "John R. Koza and Forrest H {Bennett III} and Oscar
                 Stiffelman",
  title =        "Genetic Programming as a {Darwinian} Invention
                 Machine",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "93--108",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  URL =          "http://www.genetic-programming.com/EUROGP99.ps",
  abstract =     "Genetic programming is known to be capable of creating
                 designs that satisfy prespecified high-level design
                 requirements for analog electrical circuits and other
                 complex structures. However, in the real world, it is
                 often important that a design satisfy various
                 non-technical requirements. One such requirement is
                 that a design not possess the key characteristics of
                 any previously known design. This paper shows that
                 genetic programming can be used to generate novel
                 solutions to a design problem so that genetic
                 programming may be potentially used as an invention
                 machine. This paper turns the clock back to the period
                 just before the time (1917) when George Campbell of
                 American Telephone and Telegraph invented and patented
                 the design for an electrical circuit that is now known
                 as the ladder filter. Genetic programming is used to
                 reinvent the Campbell filter. The paper then turns the
                 clock back to the period just before the time (1928)
                 when Wilhelm Cauer invented and patented the elliptic
                 filter. Genetic programming is then used to reinvent a
                 technically equivalent filter that avoids the key
                 characteristics of then-preexisting Campbell filter.
                 Genetic programming can be used as an invention machine
                 by employing a two-part fitness measure that
                 incorporates both the degree to which an individual in
                 the population satisfies the given technical
                 requirements and the degree to which the individual
                 does not possess the key characteristics of preexisting
                 technology.",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Evolving novel (patentable) circuits. Add fitness
                 penalty for being like existing circuits.

                 Ghostview barfs with EUROGP99.ps 27/11/99",
}

@Book{koza:gp3,
  author =       "John R. Koza and {David Andre} and  Forrest H {Bennett
                 III} and Martin Keane",
  title =        "Genetic Programming 3: {Darwinian} Invention and
                 Problem Solving",
  publisher =    "Morgan Kaufman",
  year =         "1999",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-543-6",
  URL =          "http://www.genetic-programming.org/gpbook3toc.html",
  URL =          "http://www.mkp.com/books_catalog/1-55860-543-6.asp",
  notes =        "Genetic programming is a method for getting a computer
                 to solve a problem by telling it what needs to be done
                 instead of how to do it. The authors present
                 genetically evolved solutions to dozens of problems of
                 design, optimal control, classification, system
                 identification, function learning, and computational
                 molecular biology. Among the solutions are 14 results
                 competitive with human-produced results, including 10
                 rediscoveries of previously patented
                 inventions.

                 Researchers in artificial intelligence, machine
                 learning, evolutionary computation, and genetic
                 algorithms will find this an essential reference to the
                 most recent and most important results in the rapidly
                 growing field of genetic programming.

                 FEATURES

                 -- Explains how the success of genetic programming
                 arises from seven fundamental differences
                 distinguishing it from conventional approaches to
                 artificial intelligence and machine learning

                 -- Describes how genetic programming uses
                 architecture-altering operations to make on-the-fly
                 decisions on whether to use subroutines, loops,
                 recursions, and memory

                 -- Demonstrates that genetic programming possesses 16
                 attributes that can reasonably be expected of a system
                 for automatically creating computer programs

                 -- Presents the general-purpose Genetic Programming
                 Problem Solver

                 -- Includes an introduction to genetic programming for
                 the uninitiated

                 CONTENTS

                 Introduction -- Background -- Architecture-Altering
                 Operations -- Genetic Programming Problem Solver (GPPS)
                 -- Automated Synthesis of Analog Electrical Circuits --
                 Evolvable Hardware -- Discovery of Cellular Automata
                 Rules -- Discovery of Motifs and Programmatic Motifs
                 for Molecular Biology -- Parallelization and
                 Implementation Issues -- Conclusion",
  size =         "1100 pages",
}

@Book{koza:video3,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane and Scott Brave",
  title =        "Genetic Programming {III} Videotape: Human Competitive
                 Machine Intelligence",
  publisher =    "Morgan Kaufmann",
  year =         "1999",
  address =      "340 Pine Street - 6th Floor, San Francisco, CA 94104,
                 USA",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This 45-minute videotape surveys the new book Genetic
                 Programming III: Darwinian Invention and Problem
                 Solving. koza:gp3 The book shows how genetic
                 programming can automatically create a computer program
                 to solve a problem. Fourteen of the results are
                 competitive with human-produced results. Ten infringe
                 on previously issued patents or duplicate the
                 functionality of previous patents in novel and creative
                 ways.",
  URL =          "http://www.mkp.com/books_catalog/1-55860-616-5.asp",
  notes =        "Videotapes available in VHS NTSC or VHS PAL format
                 ISBN for VHS NTSC: 1-55860-617-3 ISBN for VHS PAL:
                 1-55860-616-5",
  size =         "45 minutes",
}

@InProceedings{koza:1999:GPt3wami,
  author =       "J. R. Koza and F. Bennett and D. Andre and M. A.
                 Keane",
  title =        "Genetic Programming: Turing's Third Way to Achieve
                 Machine Intelligence",
  booktitle =    "Evolutionary Algorithms in Engineering and Computer
                 Science",
  year =         "1999",
  editor =       "Kaisa Miettinen and Marko M. Mäkelä and Pekka
                 Neittaanmäki and Jacques Periaux",
  pages =        "185--197",
  address =      "Jyväskylä, Finland",
  publisher_address = "Chichester, UK",
  month =        "30 " # may # " - 3 " # jun,
  publisher =    "John Wiley \& Sons",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-471-99902-4",
  URL =          "http://www.genetic-programming.com/EUROGEN99TURING.ps",
  abstract =     "This paper is about genetic programming - a way to
                 implement Turing?s third way to achieve machine
                 intelligence. Genetic programming is a {"}genetical or
                 evolutionary{"} technique that automatically creates a
                 computer program from a high-level statement of a
                 problem's requirements.",
  notes =        "EUROGEN'99
                 http://www.wiley.com/Corporate/Website/Objects/Products/0,9049,91449,00.html",
}

@Book{koza:1999:GAGPs,
  editor =       "John R. Koza",
  title =        "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  publisher =    "Stanford Bookstore",
  year =         "1999",
  address =      "Stanford, California, 94305-3079 USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.org/gpstanfordpapers.html",
  abstract =     "This volume contains 30 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426 . Medical Information Sciences
                 226) at Stanford University offered during the winter
                 quarter 1999 (both on campus and on SITN TV).",
  notes =        "Stanford Bookstore order number 00000-1216B

                 These books contain the papers written and submitted by
                 students describing their term projects for the courses
                 involved. They also contain information on the
                 structure of the courses involved. These books are
                 available from the Custom Publishing Department of the
                 Stanford Bookstore. These volumes are in the
                 Mathematics and Computer Science Library in the Main
                 Quad at Stanford University. These volumes are
                 available directly from the Stanford Bookstore by
                 calling 650-329-1217 or 800-533-2670 or by writing
                 Stanford Bookstore, Stanford University, Stanford,
                 California 94305-3079 USA. The E-Mail address of the
                 bookstore for mail orders is
                 mailorder@bookstore.stanford.edu. Be sure to mention
                 the ISBN number (or Stanford Bookstore order number),
                 the exact title, and refer to {"}Custom Publishing{"}
                 when ordering these items to avoid confusion with the
                 numerous other course readers, collections of student
                 papers, and other materials at the Stanford
                 Bookstore.",
  size =         "271 pages",
}

@InProceedings{koza:1999:JJH,
  author =       "John R. Koza",
  title =        "Human-Competitive Machine Intelligence by Means of
                 Genetic Algorithms",
  booktitle =    "Festschrift in honor of John H. Holland",
  year =         "1999",
  editor =       "Lashon Booker and Stephanie Forrest and Melanie
                 Mitchell and Rick Riolo",
  pages =        "15--22",
  address =      "Ann Arbor, MI: Center for the Study of Complex
                 Systems",
  month =        may # " 15 - 18",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/JHH99.ps",
  notes =        "see also koza:2000:IS",
}

@InProceedings{koza:1999:DIMACS,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Genetic Programming: Biologically Inspired Computation
                 that Creatively Solves Non-Trivial Problems",
  booktitle =    "Proceedings of DIMACS Workshop on Evolution as
                 Computation",
  year =         "1999",
  editor =       "Laura Landweber and Erik Winfree and Richard Lipton
                 and Stephen Freeland",
  address =      "Princeton University",
  month =        "11-12 " # jan,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-66709-1",
  URL =          "http://www.genetic-programming.com/DIMACS99.ps",
  URL =          "http://www.genetic-programming.com/eac2001chapter.pdf",
  size =         "30 pages",
  abstract =     "This paper describes a biologically inspired
                 domain-independent technique, called genetic
                 programming, that automatically creates computer
                 programs to solve problems. Starting with a primordial
                 ooze of thousands of randomly created computer
                 programs, genetic programming progressively breeds a
                 population of computer programs over a series of
                 generations using the Darwinian principle of natural
                 selection, recombination (crossover), mutation, gene
                 duplication, gene deletion, and certain mechanisms of
                 developmental biology. The technique is illustrated by
                 applying it to a non-trivial problem involving the
                 automatic synthesis (design) of a lowpass filter
                 circuit. The evolved results are competitive with
                 human-produced solutions to the problem. In fact, four
                 of the automatically created circuits exhibit
                 human-level creativity and inventiveness, as evidenced
                 by the fact that they correspond to four inventions
                 that were patented between 1917 and 1936",
  notes =        "ghostview barfs at DIMACS99.ps 26/11/99
                 eac2001chapter.pdf prints ok 9 Feb
                 2001

                 http://dimacs.rutgers.edu/Workshops/Evolution/

                 Published Jan 2001
                 http://www.amazon.com/exec/obidos/ASIN/3540667091/dominantsystems/107-7663466-9560554",
}

@InProceedings{koza:1999:ISIC,
  author =       "John R. Koza and Martin A. Keane and Forrest H
                 {Bennett III} and Jessen Yu and William Mydlowec and
                 Oscar Stiffelman",
  title =        "Automatic creation of both the topology and parameters
                 for a robust controller by means of genetic
                 programming",
  booktitle =    "Proceedings of the 1999 IEEE International Symposium
                 on Intelligent Control, Intelligent Systems, and
                 Semiotics",
  year =         "1999",
  pages =        "344--352",
  publisher_address = "Piscataway, NJ, USA",
  organisation = "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/ISIC99.ps",
  abstract =     "The paper describes a general automated method for
                 synthesizing the design of both the topology and
                 parameter values for controllers. The automated method
                 automatically makes decisions concerning the total
                 number of processing blocks to be employed in the
                 controller, the type of each block, the topological
                 interconnections between the blocks, the values of all
                 parameters for the blocks, and the existence, if any,
                 of internal feedback between the blocks of the overall
                 controller. Incorporation of time-domain,
                 frequency-domain, and other constraints on the control
                 or state variables (often analytically intractable
                 using conventional methods) can be readily
                 accommodated. The automatic method described in the
                 paper (genetic programming) is applied to a problem of
                 synthesizing the design of a robust controller for a
                 plant with a second-order lag. A textbook PID
                 compensator preceded by a lowpass pre-filter delivers
                 credible performance on this problem. However, the
                 automatically created controller employs a second
                 derivative processing block (in addition to
                 proportional, integrative, and derivative blocks and a
                 pre-filter). It is better than twice as effective as
                 the textbook controller as measured by the integral of
                 the time-weighted absolute error, has only two-thirds
                 of the rise time in response to the reference (command)
                 input, and is 10 times better in terms of suppressing
                 the effects of disturbance at the plant input.",
  notes =        "IEEE ISIC-99",
}

@InProceedings{koza:1999:IEEECDC,
  author =       "John R. Koza and Martin A. Keane and Jessen Yu and
                 Forrest H {Bennett III} and William Mydlowec and Oscar
                 Stiffelman",
  title =        "Automatic synthesis of both the topology and
                 parameters for a robust controller for a non-minimal
                 phase plant and a three-lag plant by means of genetic
                 programming",
  booktitle =    "Proceedings of 1999 IEEE Conference on Decision and
                 Control",
  year =         "1999",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/IEEECDC99.ps",
  abstract =     "This paper describes how genetic programming can be
                 used to automate the synthesis of the design of both
                 the topology and parameter values for controllers. The
                 method described in this paper automatically makes
                 decisions concerning the total number of processing
                 blocks to be employed in the controller, the type of
                 each block, the topological interconnections between
                 the blocks, the values of all parameters for the
                 blocks, and the existence, if any, of internal feedback
                 between the blocks of the overall controller. This
                 design process can readily combine optimization of
                 performance (e.g., by a metric such as the integral of
                 the time-weighted error) with time-domain constraints
                 and frequency-domain constraints. Genetic programming
                 is applied to two illustrative problems of controller
                 synthesis: the design of a robust controller for a
                 non-minimal-phase plant and the design of a robust
                 controller for a three-lag plant. A previously
                 published PID compensator (Astrom and Hagglund 1995)
                 for the three-lag plant delivers credible performance.
                 The automatically created controller is better than 7.2
                 times as effective as the previous controller as
                 measured by the integral of the time-weighted absolute
                 error, has only 50% of the rise time in response to the
                 reference input, has only 35% of the settling time, and
                 is 92.7 dB better in terms of suppressing the effects
                 of disturbance at the plant input.",
  notes =        "IEEE CDC-99",
}

@Misc{koza:1999:at,
  author =       "John Koza",
  title =        "What is Genetic Programming?",
  howpublished = "www",
  year =         "1999",
  month =        "25 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/gpanimatedtutorial.html",
  size =         "pages",
  notes =        "Brief introduction. Several animated gifs etc",
}

@InProceedings{koza:1999:SIGP,
  author =       "John R. Koza and Martin A. Keane and Forrest H Bennett
                 III and Jessen Yu and William Mydlowec and Oscar
                 Stiffelman",
  title =        "Searching for the Impossible using Genetic
                 Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1083--1091",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  abstract =     "Many potential inventions are never discovered because
                 the thought processes of scientists and engineers are
                 channeled along well-traveled paths. In contrast, the
                 evolutionary process tends to opportunistically solve
                 problems without considering whether the evolved
                 solution comports with human preconceptions about
                 whether the goal is impossible. This paper demonstrates
                 how genetic programming can be used to automate the
                 process of exploring queries, conjectures, and
                 challenges concerning the existence of seemingly
                 impossible entities. The paper suggests a way by which
                 genetic programming can be used to automate the
                 invention process. We illustrate the concept using a
                 challenge posed by a leading analog electrical engineer
                 concerning whether it is possible to design a circuit
                 composed of only resistors and capacitors that delivers
                 a gain of greater than one. The paper contains a
                 circuit evolved by genetic programming that satisfies
                 the requirement of this challenge as well a related
                 more difficult challenge. The original challenge was
                 motivated by a circuit patented in 1956 for
                 preprocessing inputs to oscilloscopes. The paper also
                 contains an evolved circuit satisfying (and exceeding)
                 the original design requirements of the circuit
                 patented in 1956. This evolved circuit is another
                 example of a result produced by genetic programming
                 that is competitive with a human-produced result that
                 was considered to be creative and inventive at the time
                 it was first discovered.",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Book{koza:2000:gagp,
  title =        "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  address =      "Stanford, California, 94305-3079 USA, Phone
                 415-329-1217 or 800-533-2670",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford University Bookstore",
  email =        "mailorder@bookstore.stanford.edu",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.org/gpstanfordpapers.html",
  notes =        "This volume contains 55 papers written by students
                 describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Medical Information Sciences 226 / Computer Science
                 426 / Electrical Engineering 392K) at Stanford
                 University during the winter quarter 2000 (offered on
                 campus, on SITN TV, and on Stanford On Line). Note that
                 this book of student papers is different from the
                 course reader used in the course.

                 Stanford Bookstore order number 002-0-00-002365-B.
                 Stanford University Bookstore for $26.00

                 Telephone 415-329-1217 or 800-533-2670 or by writing
                 Stanford Bookstore Stanford University Stanford,
                 California 94305-3079 USA The E-Mail address of the
                 bookstore for e-mail orders is
                 mailorder@bookstore.stanford.edu. Stanford koza CS426
                 course.",
}

@InProceedings{koza:2000:astpc3lp5sGP,
  author =       "John R. Koza and Martin A. Keane and Jessen Yu and
                 William Mydlowec and Forrest H {Bennett III}",
  title =        "Automatic Synthesis of Both the Topology and
                 Parameters for a Three-Lag Plant with a five-Second
                 Delay Using Genetic Programming",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and George D. Smith
                 and David Corne and Martin Oates and Emma Hart and Pier
                 Luca Lanzi and Egbert Jan Willem and Yun Li and Ben
                 Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "168--177",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67353-9",
  URL =          "http://www.genetic-programming.com/evoscondi2000.ps",
  abstract =     "This paper describes how the process of synthesizing
                 the design of both the topology and the numerical
                 parameter values (tuning) for a controller can be
                 automated by using genetic programming. Genetic
                 programming can be used to automatically make the
                 decisions concerning the total number of signal
                 processing blocks to be employed in a controller, the
                 type of each block, the topological interconnections
                 between the blocks, and the values of all parameters
                 for all blocks requiring parameters. In synthesizing
                 the design of controllers, genetic programming can
                 simultaneously optimize prespecified performance
                 metrics (such as minimizing the time required to bring
                 the plant output to the desired value), satisfy
                 time-domain constraints (such as overshoot and
                 disturbance rejection), and satisfy frequency domain
                 constraints. Evolutionary methods have the advantage of
                 not being encumbered by preconceptions that limit its
                 search to well-traveled paths. Genetic programming is
                 applied to an illustrative problem involving the design
                 of a controller for a three-lag plant with a
                 significant (five-second) time delay in the external
                 feedback from the plant to the controller. A delay in
                 the feedback makes the design of an effective
                 controller especially difficult.",
  notes =        "EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel,
                 EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland,
                 UK, April 17, 2000
                 Proceedings

                 http://evonet.dcs.napier.ac.uk/evoworkshops/

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67353-9",
}

@TechReport{koza:2000:0851,
  author =       "John R. Koza and William Mydlowec and Guido Lanza and
                 Jessen Yu and Martin A. Keane",
  title =        "Reverse Engineering and Automatic Synthesis of
                 Metabolic Pathways from Observed Data Using Genetic
                 Programming",
  institution =  "Stanford Medical Informatics",
  year =         "2000",
  number =       "SMI-2000-0851",
  email =        "koza@stanford.edu,
                 myd@cs.stanford.edu,guidissimo@hotmail.com,
                 jyu@cs.stanford.edu, makeane@ix.netcom.com",
  keywords =     "genetic algorithms, genetic programming, metabolic
                 pathways, chemical reaction networks",
  URL =          "http://smi-web.stanford.edu/pubs/SMI_Abstracts/SMI-2000-0851.html",
  abstract =     "Recent work has demonstrated that genetic programming
                 is capable of automatically creating complex networks
                 (such as analog electrical circuits and controllers)
                 whose behavior is modeled by continuous-time
                 differential equations (both linear and non-linear) and
                 whose behavior matches prespecified output values. The
                 concentrations of substances participating in networks
                 of chemical reactions are also modeled by non-linear
                 continuous-time differential equations. This paper
                 demonstrates that it is possible to automatically
                 create (reverse engineer) a network of chemical
                 reactions from observed time-domain data. Genetic
                 programming starts with observed time-domain
                 concentrations of input substances and automatically
                 creates both the topology of the network of chemical
                 reactions and the rates of each reaction within the
                 network such that the concentration of the final
                 product of the automatically created network matches
                 the observed time-domain data. This paper describes how
                 genetic programming automatically created a metabolic
                 pathway involving four chemical reactions that takes in
                 glycerol and fatty acid as input, uses ATP as a
                 cofactor, and produces diacyl-glycerol as its final
                 product. In addition, this paper describes how genetic
                 programming similarly created a metabolic pathway
                 involving three chemical reactions for the synthesis
                 and degradation of ketone bodies. Both automatically
                 created metabolic pathways contain at least one
                 instance of three noteworthy topological features,
                 namely an internal feedback loop, a bifurcation point
                 where one substance is distributed to two different
                 reactions, and an accumulation point where one
                 substance is accumulated from two sources.",
  notes =        "See also koza:2000:ICSB

                 These slide transparencies were presented at the
                 Computation in Cells workshop on Tuesday April 18, 2000
                 in Hertfordshire, UK and partially at the tutorial on
                 Saturday April 15, 2000 at the Euro-GP-2000 conference
                 in
                 Edinburgh.

                 http://www.genetic-programming.com/cincells.ps",
  size =         "53 pages",
}

@InProceedings{koza:2000:acc,
  author =       "John R. Koza and Martin A. Keane and Jessen Yu and
                 William Mydlowec and Forrest H {Bennett III}",
  title =        "Automatic synthesis of both the control law and
                 parameters for a controller for a three-lagplant with
                 five-second delay using genetic programming and
                 simulation techniques",
  booktitle =    "Proceedings of the 2000 American Control Conference",
  year =         "2000",
  pages =        "453--459",
  address =      "Chicago, Illinois, USA",
  month =        jun # " 28 - 30",
  organization = "American Automatic Control Council",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/acc2000.ps",
  abstract =     "This paper describes how the process of synthesizing
                 the design of both the topology (control law) and the
                 numerical parameter values (tuning) for a controller
                 can be automated using genetic programming. Genetic
                 programming can be used to automatically make the
                 decisions concerning the total number of signal
                 processing blocks to be employed in a controller, the
                 type of each block, the topological interconnections
                 between the blocks, and the values (tuning) of all
                 parameters for all blocks requiring parameters. In
                 synthesizing the design of controllers, genetic
                 programming can simultaneously optimize prespecified
                 performance metrics (such as minimizing the time
                 required to bring the plant output to the desired
                 value), satisfy time-domain constraints (such as
                 overshoot and disturbance rejection), and satisfy
                 frequency domain constraints. Evolutionary methods have
                 the advantage of not being encumbered by preconceptions
                 that limit its search to well-traveled paths. Genetic
                 programming is applied to an illustrative problem
                 involving the design of a controller for a three-lag
                 plant with a significant (five-second) time delay in
                 the external feedback from the plant to the controller.
                 The delay in the feedback makes the design of an
                 effective controller difficult.",
}

@TechReport{koza:2000:reader,
  author =       "John R. Koza",
  title =        "{CS} 426 / {MIS} 226 / {EE} 392{K}: Genetic Algorithms
                 and Genetic Programming Winter 2000 Course Reader",
  institution =  "Stanford University Bookstore",
  year =         "2000",
  number =       "2810000021091",
  address =      "Stanford, CA, USA",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "This course reader contains slide transparencies and
                 course materials used in CS 426 / MIS 226 / EE 392K
                 course on genetic algorithms and genetic programming in
                 the Winter quarter 2000 at Stanford University.",
}

@InProceedings{koza:2000:EH,
  author =       "John R. Koza and Jessen Yu and Martin A. Keane and
                 William Mydlowec",
  title =        "Use of conditional developmental operators and free
                 variables in automatically synthesizing generalized
                 circuits using genetic programming",
  booktitle =    "Proceedings of the Second NASA / DoD Workshop on
                 Evolvable Hardware",
  year =         "2000",
  pages =        "5--15",
  address =      "Palo Alto, California",
  month =        jul # " 13-15",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-0762-X",
  URL =          "http://www.genetic-programming.com/eh2000.ps",
  abstract =     "This paper demonstrates that genetic programming can
                 be used to create a circuit-constructing computer
                 program that contains both conditional operations and
                 inputs (free variables). The conditional operations and
                 free variables enable a single genetically evolved
                 program to yield functionally and topologically
                 different electrical circuits. The conditional
                 operations can trigger the execution of alternative
                 sequences of steps based on the particular values of
                 the free variables. The particular values of the free
                 variables can also determine the component value of the
                 circuit's components. Thus, a single evolved computer
                 program can represent the solution to many instances of
                 a problem. This principle is illustrated by evolving a
                 single computer program that yields a lowpass or a
                 highpass filter whose passband and stopband boundaries
                 depend on the program's inputs.",
  notes =        "EH-2000",
}

@InCollection{koza:2000:idas,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A. Keane",
  title =        "Automatic design of analog electrical circuits using
                 genetic programming",
  booktitle =    "Intelligent Data Analysis in Science",
  publisher =    "Oxford University Press",
  year =         "2000",
  editor =       "Hugh Cartwright",
  chapter =      "8",
  pages =        "172--200",
  address =      "Oxford",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/idashcartwright.ps",
  abstract =     "The design (synthesis) of analog electrical circuits
                 entails the creation of both the topology and sizing
                 (numerical values) of all of the circuit's components.
                 There has previously been no general automated
                 technique for automatically designing an analog
                 electrical circuit from a high-level statement of the
                 circuit's desired behavior. This chapter introduces
                 genetic programming and shows how it can be used to
                 automate the design of both the topology and sizing of
                 a suite of five prototypical analog circuits, including
                 a lowpass filter, a tri-state frequency discriminator
                 circuit, a 60 dB amplifier, a computational circuit for
                 the square root, and a time-optimal robot controller
                 circuit. The problem-specific information required for
                 each of the eight problems is minimal and consists
                 primarily of the number of inputs and outputs of the
                 desired circuit, the types of available components, and
                 a fitness measure that restates the high-level
                 statement of the circuit's desired behavior as a
                 measurable mathematical quantity. All five of these
                 genetically evolved circuits constitute instances of an
                 evolutionary computation technique solving a problem
                 that is usually thought to require human
                 intelligence.",
}

@Article{Koza:2000:CMAME,
  author =       "J. R. Koza and F. H {Bennett III} and D. Andre and M.
                 A. Keane",
  title =        "Synthesis of topology and sizing of analog electrical
                 circuits by means of genetic programming",
  journal =      "Computer Methods in Applied Mechanics and
                 Engineering",
  volume =       "186",
  pages =        "459--482",
  year =         "2000",
  number =       "2-4",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V29-40CRYKF-M/1/5360f0a76976701c046d5032c98fdcee",
  abstract =     "The design (synthesis) of an analog electrical circuit
                 entails the creation of both the topology and sizing
                 (numerical values) of all of the circuit's components.
                 There has previously been no general automated
                 technique for automatically creating the design for an
                 analog electrical circuit from a high-level statement
                 of the circuit's desired behavior. This paper shows how
                 genetic programming can be used to automate the design
                 of eight prototypical analog circuits, including a
                 lowpass filter, a highpass filter, a bandstop filter, a
                 tri-state frequency discriminator circuit, a
                 frequency-measuring circuit, a 60 dB amplifier, a
                 computational circuit for the square root function, and
                 a time-optimal robot controller circuit.",
}

@InProceedings{Koza2:2000:GECCOlb,
  author =       "William Mydlowec and John R. Koza",
  title =        "Use of Time-Domain Simulations in Automatic Synthesis
                 of Computational Circuits Using {GP}",
  pages =        "187--197",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/gecco2000lbpcomp.ps",
  abstract =     "Previously reported applications of genetic
                 programming to the automatic synthesis of computational
                 circuits have employed simulations based on DC sweeps.
                 DC sweeps have the advantage of being considerably less
                 time-consuming than time-domain simulations. However,
                 this type of simulation does not necessarily lead to
                 robust circuits that correctly perform the desired
                 mathematical function over time. This paper addresses
                 the problem of automatically synthesizing computational
                 circuits using multiple time-domain simulations and
                 presents results involving the synthesis of both the
                 topology and sizing for a squaring, square root, and
                 multiplier computational circuit and a lag circuit
                 (from the field of control).",
  notes =        "Part of whitley:2000:GECCOlb",
}

@InProceedings{Koza:2000:GECCOlb,
  author =       "William Comisky and Jessen Yu and John R. Koza",
  title =        "Automatic Synthesis of a Wire Antenna Using Genetic
                 Programming",
  pages =        "179--186",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/gecco2000lbpantenna.ps",
  abstract =     "This paper demonstrates the use of genetic programming
                 to automatically synthesize the design of a wire
                 antenna for an illustrative problem that has been
                 previously solved by both conventional antenna design
                 techniques and the genetic algorithm operating on
                 fixed-length character strings. When the genetic
                 algorithm was used, the human user prespecified many
                 characteristics of the size and shape of the solution.
                 The run of genetic programming also produced a
                 satisfactory result for the illustrative problem.
                 However, it did not require the human user to
                 prespecify the size and shape of the solution.
                 Functions from the Logo programming language and
                 Lindenmayer systems enable genetic programming to draw
                 the antenna. The solution evolved by genetic
                 programming possesses the essential characteristics of
                 the Yagi-Uda type of antenna. The rediscovery by
                 genetic programming of the essential characteristics of
                 the Yagi-Uda antenna is an instance where genetic
                 programming has produced a result that is competitive
                 with a result produced by creative and inventive
                 humans.",
  notes =        "Part of whitley:2000:GECCOlb",
}

@InProceedings{koza:2000:ecfvGP,
  author =       "John R. Koza and Martin A. Keane and Jessen Yu and
                 Forrest H {Bennett III} and William Mydlowec",
  title =        "Evolution of a Controller with a Free Variable using
                 Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "91--105",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  URL =          "http://www.genetic-programming.com/eurogp2000.ps",
  abstract =     "A mathematical formula containing one or more free
                 variables is {"}general{"} in the sense that it
                 provides a solution to an entire category of problems.
                 For example, the familiar formula for solving a
                 quadratic equation contains free variables representing
                 the equation's coefficients. Previous work has
                 demonstrated that genetic programming can automatically
                 synthesize the design for a controller consisting of a
                 topological arrangement of signal processing blocks
                 (such as integrators, differentiators, leads, lags,
                 gains, adders, inverters, and multipliers), where each
                 block is further specified ({"}tuned{"}) by a numerical
                 component value, and where the evolved controller
                 satisfies user-specified requirements. The question
                 arises as to whether it is possible to use genetic
                 programming to automatically create a {"}generalized{"}
                 controller for an entire category of such controller
                 design problems instead of a single instance of the
                 problem. This paper shows, for an illustrative problem,
                 how genetic programming can be used to create the
                 design for both the topology and tuning of controller,
                 where the controller contains a free variable.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@Article{koza:2000:IS,
  author =       "John R. Koza",
  title =        "Human-competitive machine intelligence by means of
                 genetic programming",
  journal =      "IEEE Intelligent Systems",
  year =         "2000",
  volume =       "15",
  number =       "3",
  pages =        "76--78",
  month =        may # "-" # jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1094-7167",
  URL =          "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf",
  size =         "3 pages",
  notes =        "part of hirsh:2000:GP",
}

@Article{koza:2000:achcpcmGP,
  author =       "John R. Koza and Martin A. Keane and Jessen Yu and
                 Forrest H {Bennett III} and William Mydlowec",
  title =        "Automatic Creation of Human-Competitive Programs and
                 Controllers by Means of Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "1/2",
  pages =        "121--164",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, control,
                 network synthesis, human competitive results",
  ISSN =         "1389-2576",
  URL =          "http://www.genetic-programming.com/gpemcontrol.pdf",
  abstract =     "Genetic programming is an automatic method for
                 creating a computer program or other complex structure
                 to solve a problem. This paper first reviews various
                 instances where genetic programming has previously
                 produced human-competitive results. It then presents
                 new human-competitive results involving the automatic
                 synthesis of the design of both the parameter values
                 (i.e., tuning) and the topology of controllers for two
                 illustrative problems. Both genetically evolved
                 controllers are better than controllers designed and
                 published by experts in the field of control using the
                 criteria established by the experts. One of these two
                 controllers infringes on a previously issued patent.
                 Other evolved controllers duplicate the functionality
                 of other previously patented controllers. The results
                 in this paper, in conjunction with previous results,
                 reinforce the prediction that genetic programming is on
                 the threshold of routinely producing human-competitive
                 results and that genetic programming can potentially be
                 used as an ?invention machine? to produce patentable
                 new inventions.",
}

@InProceedings{Koza:2000:GECCO,
  author =       "John R. Koza and Martin A. Keane and Jessen Yu and
                 William Mydlowec",
  title =        "Automatic Synthesis of Electrical Circuits Containing
                 a Free Variable using Genetic Programming",
  pages =        "477--484",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  URL =          "http://www.genetic-programming.com/gecco2000kfilter.ps",
  abstract =     "A mathematical formula containing one or more free
                 variables is {"}general{"} in the sense that it
                 represents the solution to all instances of a problem
                 (instead of just the solution of a single instance of
                 the problem). For example, the familiar formula for
                 solving a quadratic equation contains free variables
                 representing the coefficients of the to-be-solved
                 equation. This paper demonstrates, using an
                 illustrative problem, that genetic programming can
                 automatically create the design for both the topology
                 and component values for an analog electrical circuit
                 in which the value of each component in the evolved
                 circuit is specified by a mathematical expression
                 containing a free variable. That is, genetic
                 programming is used to evolve a general parameterized
                 circuit that satisfies the problem's high-level
                 requirements. The evolved circuit has been
                 cross-validated on unseen values of the free
                 variable.",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{koza:2000:ICSB,
  author =       "John R. Koza and William Mydlowec and Guido Lanza and
                 Jessen Yu and Martin A. Keane",
  title =        "Reverse engineering of metabolic pathways from
                 observed data by means of genetic programming",
  booktitle =    "First International Conference on Systems Biology
                 (ICSB)",
  year =         "2000",
  address =      "Tokyo",
  month =        "14-16 " # nov,
  organisation = "Japan Society for Bioinformatics",
  keywords =     "genetic algorithms, genetic programming, Biology,
                 metabolic pathways, reverse engineering",
  URL =          "http://www.genetic-programming.com/icsb2000mp.ps",
  abstract =     "Recent work has demonstrated that genetic programming
                 is capable of automatically creating complex networks
                 and structures (e.g., analog electrical circuits,
                 controllers, and antennas) whose behavior is governed
                 by linear and non-linear differential equations and
                 whose behavior matches prespecified data values. The
                 concentrations of substances (substrates, products, and
                 catalysts) participating in networks of chemical
                 reactions are described by non-linear continuous-time
                 differential equations (e.g., Michaelis-Menten
                 equations). This paper demonstrates that it is possible
                 to automatically create (reverse engineer) a network of
                 chemical reactions from observed time-domain data.
                 Genetic programming starts with observed time-domain
                 concentrations of substances and automatically creates
                 both the topology and sizing (i.e., the rates of each
                 reaction) of a network whose behavior matches observed
                 time-domain data. Specifically, genetic programming
                 automatically created a metabolic pathway involving
                 four chemical reactions that consume glycerol and fatty
                 acids as input, used ATP as a cofactor, and produced
                 diacyl-glycerol as the final product. The metabolic
                 pathway was created from 270 data points. The
                 automatically created metabolic pathway contains three
                 key topological features, including an internal
                 feedback loop, a bifurcation point where one substance
                 is distributed to two different reactions, and an
                 accumulation point where one substance is accumulated
                 from two sources. The topology and sizing of the entire
                 metabolic pathway was automatically created using only
                 the time-domain concentration values of diacyl-glycerol
                 (the final product).",
  notes =        "ICSB-2000 8 Feb 2001 ghostview and our printers barf
                 at icsb2000mp.ps E-CELL. Population size 100000.",
}

@InProceedings{koza:2001:PSB,
  author =       "J. R. Koza and W. Mydlowec and G. Lanza and J. Yu and
                 M. A. Keane",
  title =        "Reverse Engineering of Metabolic Pathways from
                 Observed Data Using Genetic Programming",
  booktitle =    "Pacific Symposium on Biocomputing 6",
  year =         "2001",
  pages =        "434--445",
  address =      "Hawaii",
  month =        "3-7 " # jan,
  publisher =    "World Scientific press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.smi.stanford.edu/projects/helix/psb01/koza.pdf",
  abstract =     "Recent work has demonstrated that genetic programming
                 is capable of automatically creating complex networks
                 (such as analog electrical circuits and controllers)
                 whose behavior is modeled by linear and non-linear
                 continuous-time differential equations and whose
                 behavior matches prespecified output values. The
                 concentrations of substances participating in networks
                 of chemical reactions are also modeled by non-linear
                 continuous-time differential equations. This paper
                 demonstrates that it is possible to automatically
                 create (reverse engineer) a network of chemical
                 reactions from observed time-domain data. Genetic
                 programming starts with observed time-domain
                 concentrations of input substances and automatically
                 creates both the topology of the network of chemical
                 reactions and the rates of each reaction within the
                 network such that the concentration of the final
                 product of the automatically created network matches
                 the observed time-domain data. Specifically, genetic
                 programming automatically created metabolic pathways
                 involved in the phospholipid cycle and the synthesis
                 and degradation of ketone bodies.",
  notes =        "E-CELL, SPICE3, 270 fitness cases, population size
                 100000",
}

@InProceedings{koza:2001:gecco,
  title =        "Automatic Synthesis of Both the Topology and Sizing of
                 Metabolic Pathways using Genetic Programming",
  author =       "John R. Koza and William Mydlowec and Guido Lanza and
                 Jessen Yu and Martin A. Keane",
  pages =        "57--65",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, chemical
                 reactions, metabolic pathways, microarrays, reaction
                 networks, automated synthesis",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@Book{koza:2002:gagp,
  title =        "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford University Bookstore",
  email =        "mailorder@bookstore.stanford.edu",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "This volume contains 30 papers written by students
                 describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Biomedical Informatics 226 / Computer Science 426 /
                 Electrical Engineering 392K) at Stanford University
                 during the spring quarter 2002. The course was offered
                 on campus, by SCPD on TV, and on the web as Stanford On
                 Line.
                 http://scpd.stanford.edu/scpd/courses/academic/crseDesc.asp?crseID=208&sdID=3

                 These volumes are all in the Mathematics and Computer
                 Science Library at Stanford University and are
                 available for purchase directly from the Stanford
                 University Bookstore by calling 650-329-1217 or
                 800-533-2670 or by writing Stanford Bookstore, Stanford
                 University, Stanford, California 94305-3079 USA. The
                 E-Mail address of the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu. The WWW URL for the
                 Stanford Bookstore is
                 http://bookstore.stanford.edu.

                 Course NOT run in 2000-2001",
}

@InProceedings{Kraft:1994:GPqir,
  author =       "D. H. Kraft and F. E. Petry and W. P. Buckles and T.
                 Sadasivan",
  title =        "The use of genetic programming to build queries for
                 information retrieval",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "468--473",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Syntax of programs restricted to boolean disjunctive
                 normal form. Has 4923 terms (=terminals?), seeds
                 initial population so 80% of terms are predetermined to
                 be relevant, rest chosen at random. 20% mutation rate
                 (three kinds). Fitness ~ relevance of documents
                 retrived.",
}

@InProceedings{krantz:2002:gecco:lbp,
  title =        "Programmatic Compression of Natural Video",
  author =       "Thomas Krantz and Oscar Lindberg and Gunnar Thorburn
                 and Peter Nordin",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "301--307",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 fitness based on mean squared error (across time and
                 pixels) homologous crossover, 3 mutations,
                 parallelisation",
}

@InProceedings{krasnogor:1999:PSPWEA,
  author =       "Natalio Krasnogor and William E. Hart and Jim Smith
                 and David A. Pelta",
  title =        "Protein Structure Prediction With Evolutionary
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1596--1601",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{krawiec:2002:GPEM,
  author =       "Krzysztof Krawiec",
  title =        "Genetic Programming-based Construction of Features for
                 Machine Learning and Knowledge Discovery Tasks",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "4",
  pages =        "329--343",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, machine
                 learning, change of representation, feature
                 construction, feature selection",
  ISSN =         "1389-2576",
  abstract =     "In this paper we use genetic programming for changing
                 the representation of the input data for machine
                 learners. In particular, the topic of interest here is
                 feature construction in the learning-from-examples
                 paradigm, where new features are built based on the
                 original set of attributes. The paper first introduces
                 the general framework for GP-based feature
                 construction. Then, an extended approach is proposed
                 where the useful components of representation
                 (features) are preserved during an evolutionary run, as
                 opposed to the standard approach where valuable
                 features are often lost during search. Finally, we
                 present and discuss the results of an extensive
                 computational experiment carried out on several
                 reference data sets. The outcomes show that classifiers
                 induced using the representation enriched by the
                 GP-constructed features provide better accuracy of
                 classification on the test set. In particular, the
                 extended approach proposed in the paper proved to be
                 able to outperform the standard approach on some
                 benchmark problems on a statistically significant
                 level.",
  notes =        "Article ID: 5103872",
}

@InCollection{krein:1994:self,
  author =       "Todd Krein",
  title =        "Simple Memory Models and the Concept of Self in the
                 Game of Concentration",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "77--86",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InCollection{krien:1994:echo,
  author =       "Todd Krien",
  title =        "The Effect of the Interaction Fraction on Stable Long
                 Term Patterns in Echo Systems and Markers for
                 Determination of Final State",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "73--80",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "John Holland's Echo SFI Echo
                 ftp://santafe.edu/pub/Users/Terry/echo/

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{krink:1999:APMEASVSP,
  author =       "Thiemo Krink and Brian H. Mayoh and Zbigniew
                 Michalewicz",
  title =        "A {PATCHWORK} Model for Evolutionary Algorithms with
                 Structured and Variable Size Populations",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1321--1328",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{kroymann:2000:DPBEGP,
  author =       "Dan B. Kroymann",
  title =        "Dynamic Population Based Evolution using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "228--233",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InCollection{kruckemyer:1994:aais,
  author =       "David Kruckemyer",
  title =        "An Approach to Analyzing the Immune System with
                 Genetic Algorithms",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "81--90",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, John Holland Echo",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{kubica:2002:ICRA,
  author =       "Jeremy Kubica and Eleanor Rieffel",
  title =        "Creating a Smarter Membrane: Automatic Code Generation
                 for Modular Self-Reconfigurable Robots",
  booktitle =    "ICRA'02 Proceedings of the 2002 IEEE International
                 Conference on Robotics and Automation",
  year =         "2002",
  volume =       "1",
  month =        "11-15 " # may,
  pages =        "793--800",
  address =      "Washington, DC",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-7272-7",
  URL =          "http://ieeexplore.ieee.org/iel5/7916/21826/01013455.pdf?isNumber=21826&prod=CNF&arnumber=1013455&arSt=793&ared=800&arAuthor=Kubica%2C+J.%3B+Rieffel%2C+E.",
  abstract =     "This work extends previous research on developing
                 control software for modular robotic smart membranes to
                 a second module type with more complicated movement, to
                 3-D membranes, to the presence of gravity, and to less
                 easily manipulatable objects. Moreover, it extends the
                 capabilities of the membranes from simple filtering to
                 more complex sorting tasks. The control software we
                 developed is completely decentralized, and
                 automatically generated.",
  notes =        "ICRA2002 http://www.icra-iros.com/icra2002/",
}

@InProceedings{kubica:2002:gecco,
  author =       "Jeremy Kubica and Eleanor Rieffel",
  title =        "Collaborating With {A} Genetic Programming System To
                 Generate Modular Robotic Code",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "804--811",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, automatic
                 code generation, human-computer collaboration,
                 primitives, problem set up, robotics",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{kubota:1999:itveGP,
  author =       "N. Kubota and F. Kojima and S. Hashimoto and T.
                 Fukuda",
  title =        "Information transformation by virus-evolutionary
                 genetic programming",
  booktitle =    "Proceedings 4th International Symposium on Artificial
                 Life and Robotics",
  year =         "1999",
  editor =       "M. Sugisaka",
  address =      "B-Con Plaza, Beppu, Oita, Japan",
  month =        "19-22 " # jan,
  organisation = "Oita University",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AROB'99 Details from www site etc",
}

@InProceedings{kubota:2000:gfitelp,
  author =       "Naoyuki Kubota and Setsuo Hashimoto and Fumio Kojima
                 and Kazuhiko Taniguchi",
  title =        "{GP}-Prepocessed Fuzzy Inference for The Energy Load
                 Prediction",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1--6",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, hybrid
                 systems",
  ISBN =         "0-7803-6375-2",
  abstract =     "This paper deals with a prediction system based on
                 genetic programming and fuzzy inference system. In real
                 problems with many parameters, the prediction
                 performance depends on the feature extraction and
                 selection. These processes are performed using methods
                 of multivariate statistical analysis by human
                 operators. However, we should automatically perform
                 feature extraction and selection from many measured
                 data. This paper applies genetic programming for the
                 feature extraction and selection, and further use fuzzy
                 inference for the building energy load prediction. The
                 functions generated by GP translate the measured data
                 into the meaningful information that is used as input
                 data to the fuzzy inference system. The simulation
                 results show that the proposed method can extract
                 meaningful information from the measured data and can
                 predict the building energy load of the next day.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@InProceedings{kuhling:2002:EuroGP,
  title =        "Brute-Force Approach to Automatic Induction of Machine
                 Code on {CISC} Architectures",
  author =       "Felix K{\"u}hling and Krister Wolff and Peter Nordin",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "288--297",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "The usual approach to address the brittleness of
                 machine code in evolution is to constrain mutation and
                 crossover to ensure syntactic closure. In the novel
                 approach presented here we use no constraints on the
                 operators, they all work blindly on the binaries in
                 memory, but we instead encapsulate the code and trap
                 all resulting exceptions. The new approach presented
                 here for machine code evolution on CISC architectures
                 is based on the observation that modern CPUs can cope
                 with incorrect programmes and report errors to the
                 operating system. This way it is possible to return to
                 very simple genetic operators with the objective of
                 increased performance. Furthermore the instruction set
                 used by evolved programmes is no longer limited by the
                 genetic programming system but only by the CPU it runs
                 on. The mapping between evolution platform and
                 execution plattform becomes almost complete, ensuring
                 correct low-level behaviour of all CPU functions.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@Misc{kuhlmann:1995:gp,
  author =       "Hans Kuhlmann and Mike Hollick",
  title =        "Genetic Programming in {C}/{C}++",
  year =         "1995",
  month =        May,
  note =         "CSE99/CIS899 Final Report",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cis.upenn.edu/~hollick/genetic/paper2.html",
  size =         "pages",
  abstract =     "Genetic programming is a relatively new form of
                 artificial intelligence, and is based on the ideas of
                 Darwinian evolution and genetics. The foremost work in
                 genetic programming is John Koza's Genetic Programming,
                 which describes a set of LISP routines which modify
                 randomly generated LISP strings. In this paper, We
                 attempt to explain the paradigm of genetic programming,
                 and its implementation in the C programming language.
                 we shall present results obtained from various trials,
                 and make comments on the viability of this new form of
                 genetic programming.",
}

@InProceedings{kumar:1999:TAI,
  author =       "Sanjeev Kumar and Peter Bentley",
  title =        "The {ABC}'s of evolutionary design: Investigating the
                 evolvability of embryogenies for morphogenesis",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "164--170",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{kundu:1999:CEEOS,
  author =       "Sourav Kundu",
  title =        "Complexity Engineering, Evolution and Optimality of
                 Structures",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "796",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{kunz:2000:GDSBG,
  author =       "Robert Kunz",
  title =        "Genetic Discovery of Solutions to the Broadside Game",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "234--243",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InCollection{kurtin:1998:SCPGA,
  author =       "Brett D. Kurtin",
  title =        "Solving Coding Problems in Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "39--49",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{kuscu:2000:gdsfgp,
  author =       "Ibrahim Kuscu",
  title =        "Generalisation and Domain Specific Functions in
                 Genetic Programming",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1393--1400",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, novel
                 applications ii",
  ISBN =         "0-7803-6375-2",
  abstract =     "This research presents an evaluation of user defined
                 domain specific functions of genetic programming using
                 relational learning problems, generalisation for this
                 class of learning problems and learning bias. After
                 providing a brief theoretical background, two sets of
                 experiments are detailed: experiments and results
                 concerning the Monk-2 problem and experiments
                 attempting to evolve generalising solutions to parity
                 problems with incomplete data sets. The results suggest
                 that using nonproblem specific functions may result in
                 greater generalisation for relational problems.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@TechReport{Kuscu:1994:dANNuGA,
  author =       "Ibrahim Kuscu and Chris Thornton",
  title =        "Design of artificial neural networks using genetic
                 algorithms: review and prospect",
  institution =  "School of Cognitive and Computing Sciences, University
                 of Sussex",
  year =         "1994",
  type =         "Cognitive Science Research Paper",
  number =       "319",
  address =      "Falmer, Brighton, Sussex, UK",
  month =        "30 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp319.ps.Z",
  abstract =     "The design of Artificial Neural Networks by Genetic
                 Algorithm is useful in terms of (1) automating and
                 optimising the design and (2) finding biologically
                 plausible models. This paper presents a review of the
                 state of the art and research prospects in the area.",
  size =         "11 pages",
}

@TechReport{Kuscu:1995:elrst1,
  author =       "Ibrahim Kuscu",
  title =        "Evolution of learning rules for supervised tasks {I}:
                 simple learning problems",
  institution =  "School of Cognitive and Computing Sciences, University
                 of Sussex",
  year =         "1995",
  type =         "Cognitive Science Research Paper",
  number =       "394",
  address =      "Falmer, Brighton, Sussex, UK",
  month =        "10 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp394.ps.Z",
  abstract =     "Initial experiments with a genetic-based encoding
                 schema are presented as a potentially powerful tool to
                 discover learning rules by means of evolution. Several
                 simple supervised learning tasks are tested. The
                 results indicate the potential of the encoding schema
                 to discover learning rules for more complex and larger
                 learning problems.",
  size =         "18 pages",
}

@TechReport{Kuscu:1995:elrst2,
  author =       "Ibrahim Kuscu",
  title =        "Evolution of learning rules for supervised tasks {II}:
                 hard learning problems",
  institution =  "School of Cognitive and Computing Sciences, University
                 of Sussex",
  year =         "1995",
  type =         "Cognitive Science Research Paper",
  number =       "395",
  address =      "Falmer, Brighton, Sussex, UK",
  month =        "10 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp395.ps.Z",
  abstract =     "Recent experiments with a genetic-based encoding
                 schema are presented as a potentially powerful tool to
                 discover learning rules by means of evolution. The
                 representation used is similar to the one used in
                 Genetic Programming (GP) but it employs only a fixed
                 set of functions to solve a variety of problems. In
                 this paper three Monks' and parity problems are tested.
                 The results indicate the usefulness of the encoding
                 schema in discovering learning rules for hard learning
                 problems. The problems and future research directions
                 are discussed within the context of GP practices.",
  size =         "18 pages",
}

@TechReport{Kuscu:1995:elrstMP,
  author =       "Ibrahim Kuscu",
  title =        "Incrementally learning the rules for supervised tasks:
                 the Monk's problems",
  institution =  "School of Cognitive and Computing Sciences, University
                 of Sussex",
  year =         "1995",
  type =         "Cognitive Science Research Paper",
  number =       "396",
  address =      "Falmer, Brighton, Sussex, UK",
  month =        "7 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp396.ps.Z",
  abstract =     "In previous experiments [4][5] evolution of variable
                 length mathematical expressions containing input
                 variables was found to be useful in finding learning
                 rules for simple and hard supervised tasks. However,
                 hard learning problems required special attention in
                 terms of their need for larger size codings of the
                 potential solutions and their ability of generalisation
                 over the testing set. This paper describes new
                 experiments aiming to find better solutions to these
                 issues. Rather than evolution a hill climbing strategy
                 with an incremental coding of potential solutions is
                 used in discovering learning rules for the three Monks'
                 problems. It is found that with this strategy larger
                 solutions can easily be coded for. Although a better
                 performance is achieved in training for the hard
                 learning problems, the ability of the generalisation
                 over the testing cases is observed to be poor.",
  size =         "14 pages",
}

@InProceedings{kuscu:1996:elrhlp,
  author =       "Ibrahim Kuscu",
  title =        "Evolution of Learning Rules for Hard Learning
                 Problems",
  booktitle =    "Evolutionary Programming V: Proceedings of the Fifth
                 Annual Conference on Evolutionary Programming",
  year =         "1996",
  editor =       "Lawrence J. Fogel and Peter J. Angeline and Thomas
                 Baeck",
  address =      "San Diego",
  publisher_address = "Cambridge, MA, USA",
  month =        feb # " 29-" # mar # " 3",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-06190-2",
  URL =          "http://www.cogs.susx.ac.uk/users/ibrahim/epconf.ps",
  size =         "9 pages",
  notes =        "EP-96
                 http://www.natural-selection.com/eps/EP96.html

                 Details from
                 http://www.cogs.susx.ac.uk/users/ibrahim/academic.html",
}

@InProceedings{kuscu:1996:GPiasslp,
  author =       "Ibrahim Kuscu",
  title =        "Genetic Programming and Incremental Aproaches to Solve
                 Supervised Learning Problems",
  booktitle =    "ICML'96, Evolutionary computing and Machine Learning
                 Workshop",
  year =         "1996",
  editor =       "T. Fogarty and G. Venturini",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cogs.susx.ac.uk/users/ibrahim/icml96.ps",
  size =         "10 pages",
  notes =        "Details from
                 http://www.cogs.susx.ac.uk/users/ibrahim/academic.html",
}

@InProceedings{bensusan:1996:ciGP,
  author =       "Hilan Bensusan and Ibrahim Kuscu",
  title =        "Constructive Induction using Genetic Programming",
  booktitle =    "ICML'96, Evolutionary computing and Machine Learning
                 Workshop",
  year =         "1996",
  editor =       "T. Fogarty and G. Venturini",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cogs.susx.ac.uk/users/ibrahim/ciga.ps",
  url_2 =        "http://www.cogs.susx.ac.uk/users/hilanb/ciga.ps",
  size =         "6 pages",
  notes =        "Details from
                 http://www.cogs.susx.ac.uk/users/ibrahim/academic.html",
}

@InProceedings{kuscu:1996:eimshlp,
  author =       "Ibrahim Kuscu",
  title =        "Evolutionary and Incremental Methods to Solve Hard
                 Learning Problems",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "431",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@InProceedings{kuscu:1996:eimshlpLB,
  author =       "Ibrahim Kuscu",
  title =        "Evolutionary and Incremental Methods to Solve Hard
                 Learning Problems",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "101--106",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670 Hill climbing, supervised
                 learning, 3 monks and parity problems",
}

@Unpublished{Kuscu:1997:stfe,
  author =       "Ibrahim Kuscu",
  title =        "Generalisation and Artificial Ant Problem",
  note =         "A very early draft",
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cogs.susx.ac.uk/users/ibrahim/stfe.ps",
  size =         "20 pages",
}

@InProceedings{kuscu:1996:sGPslp,
  author =       "Ibrahim Kuscu",
  title =        "Simple Genetic Programming for Supervised Learning
                 Problems",
  booktitle =    "Proceedings of Fifth Turkish Syposium on Artificial
                 Intelligence and Neural Networks",
  year =         "1996",
  editor =       "E. Alpaydin and U. Cilingiroglu and F. Gurgen and C.
                 Guzelis",
  publisher_address = "Istanbul",
  publisher =    "Bogazici Uni. Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cogs.susx.ac.uk/users/ibrahim/tainn96.ps",
  size =         "10 pages",
  notes =        "Details from
                 http://www.cogs.susx.ac.uk/users/ibrahim/academic.html",
}

@InProceedings{kuscu:1998:egb,
  author =       "Ibrahim Kuscu",
  title =        "Evolving a Generalised Behavior: Artificial Ant
                 Problem Revisited",
  booktitle =    "Seventh Annual Conference on Evolutionary
                 Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "799--808",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  organisation = "Natural Selection, Inc.",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  URL =          "http://www.cogs.susx.ac.uk/users/ibrahim/ep98.ps",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 Santa Fe trail",
}

@InProceedings{kuscu:1998:mpgGP,
  author =       "Ibrahim Kuscu",
  title =        "A Method of Promoting Generalisation in Genetic
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "192",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{kuscu:1998:pglbGP,
  author =       "Ibrahim Kuscu",
  title =        "Promoting Generalization of Learned Behaviours in
                 Genetic Programming",
  booktitle =    "Fifth International Conference on Parallel Problem
                 Solving from Nature",
  year =         "1998",
  editor =       "Agoston E. Eiben and Thomas Back and Marc Schoenauer
                 and Hans-Paul Schwefel",
  volume =       "1498",
  series =       "LNCS",
  pages =        "491--500",
  address =      "Amsterdam",
  publisher_address = "Berlin",
  month =        "27-30 " # sep,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65078-4",
  abstract =     "Recently, growing numbers of research concentrate on
                 robustness of the programs evolved using GP. While some
                 of the researchers report on the brittleness of the
                 solutions evolved, some others proposed methods of
                 promoting robustness. It is important that these
                 methods are not ad hoc and specific for a certain
                 experimental setup. In this research, brittleness of
                 solutions found for the artificial ant problem is
                 reported, and a new method promoting generalization of
                 the solution is presented.",
  notes =        "PPSN-V",
}

@InProceedings{Kvasnieka:1997:WSC2,
  author =       "Vladimir Kvasnieka and Jiøí Pospíchal",
  title =        "Simple Implementation of Genetic Programming by Column
                 Tables",
  booktitle =    "Soft Computing in Engineering Design and
                 Manufacturing",
  year =         "1997",
  editor =       "P. K. Chawdhry and R. Roy and R. K. Pant",
  pages =        "48--56",
  publisher_address = "Godalming, GU7 3DJ, UK",
  month =        "23-27 " # jun,
  publisher =    "Springer-Verlag London",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-76214-0",
  URL =          "http://www.bath.ac.uk/Departments/Eng/wsc2/ind_paper/p_kvasni.html",
  abstract =     "Simple implementation of genetic programming by making
                 use of the column tables is discussed. Implementations
                 of Koza's genetic programming in compiled languages are
                 usually not most efficient when crossover is applied.
                 If chromosomes are directed acyclic graphs, more
                 efficient than rooted trees both in memory requirement
                 as well as in evaluation time of chromosome, then
                 crossover requires traversing the data structures and
                 their preliminary analysis. Column tables inherently
                 code directed acyclic graphs, the implementation of
                 crossover is simple and needs neither traversing nor
                 checking of integrity of resulting data structures and
                 should be therefore more efficient. Stochastic
                 transformation operation mutation is also easily
                 defined. Column tables can represent graphs with
                 several output nodes and may be used e.g. for
                 optimization of feed-forward neural networks. Simple
                 illustrative examples of symbolic regression based on
                 the column tables are presented.",
  notes =        "WSC2 Second On-line World Conference on Soft Computing
                 in Engineering Design and Manufacturing",
}

@InCollection{kwok:2000:EOPGLUGA,
  author =       "Roberta Kwok",
  title =        "Evolution of Oscillating Patterns in the Game of Life
                 Using Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "244--251",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{kwon:2001:gecco,
  title =        "Personalized Email Marketing with a Genetic
                 Programming Circuit Model",
  author =       "Yung-Keun Kwon and Byung-Ro Moon",
  pages =        "1352--1358",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "real world applications, Genetic programming, circuit
                 model, data mining, local optimization,
                 personalization, one-to-one marketing, email
                 marketing",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InCollection{laane:1995:DNCVHTSGP,
  author =       "Lisa A. Laane",
  title =        "Development of Navigational Controllers for Vehicles
                 in Highway Traffic Situations via Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "171--180",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InCollection{Lahiri:1997:galjq,
  author =       "Tirthankar Lahiri",
  title =        "Gnetic Optimization of Large Join Queries",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "128--137",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{lahiri:1998:gotljq,
  author =       "Tirthankar Lahiri",
  title =        "Genetic Optimization Techniques for Large Join
                 Queries",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "535--542",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{WaiLam:1998:dpkdecmdl,
  author =       "Wai Lam and Man Leung Wong and Kwong Sak Leung and Po
                 Shun Ngan",
  title =        "Discovering Probabilistic Knowledge from Databases
                 Using Evolutionary Computation and Minimum Description
                 Length Principle",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "786--794",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolutionary programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{lam:1995:CPOV,
  author =       "Vui Chiap Lam",
  title =        "Camera Placement for Optimal Visibility",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "181--190",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InCollection{lamego:1998:AGAVQNT,
  author =       "Marcelo M. Lamego",
  title =        "Applying Genetic Algorithm and Vector Quantization to
                 Neurointerface Training",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "50--57",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InCollection{lampe:1999:AESADFPCA,
  author =       "Steve Lampe",
  title =        "An Exercise in Spectrum Allocation: Developing a
                 Frequency Plan for the Conrail Acquisition",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "95--104",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{land:1995:srlc,
  author =       "Mark Land and Richard K. Belew",
  title =        "A Programming Language for Artificial Development",
  booktitle =    "Evolutionary Programming {IV} Proceedings of the
                 Fourth Annual Conference on Evolutionary Programming",
  year =         "1995",
  editor =       "John Robert McDonnell and Robert G. Reynolds and David
                 B. Fogel",
  pages =        "403--413",
  address =      "San Diego, CA, USA",
  month =        "1-3 " # mar,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, cellular automata, autocatalytic,
                 algorithmic chemistry",
  ISBN =         "0-262-13317-2",
  size =         "10 pages",
  notes =        "EP-95

                 ",
}

@InProceedings{landweber:1998:eDNA:nscp,
  author =       "Laura F. Landweber and Lila Kari",
  title =        "The Evolution of {DNA} Computing: Nature's Solution to
                 a Computational Problem",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "700--708",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{lang:1995:hcbgs,
  author =       "Kevin J. Lang",
  title =        "Hill Climbing Beats Genetic Search on a Boolean
                 Circuit Synthesis of {K}oza's",
  booktitle =    "Proceedings of the Twelfth International Conference on
                 Machine Learning",
  year =         "1995",
  address =      "Tahoe City, California, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  size =         "4 pages",
  abstract =     "Problem taken from chapter 9 of GP1. Shows hill
                 climbing more efficient than GP on this problem.
                 Speculates that (for this problem) fragments of high
                 fitness individuals have no special value when
                 transplanted into other individuals.

                 See also koza:1995:hcbgs",
  notes =        "

                 ",
}

@TechReport{Langdon:1995:gp.c,
  author =       "W. B. Langdon",
  title =        "Quick Intro to simple-gp.c",
  institution =  "University College London",
  year =         "1994",
  type =         "Internal Note",
  number =       "IN/95/2",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        "25 " # apr,
  keywords =     "Genetic Programming, Genetic Algorithms",
  URL =          "ftp://cs.ucl.ac.uk/genetic/gp-code/simple/intro-simple-gp.ps",
  abstract =     "Wouldn't it be great if computers could actually write
                 the programs? This is the promise of genetic
                 programming. This document gives an introduction to the
                 program simple-gp.c which demonstrates the principles
                 of genetic programming in the C language. This program
                 was designed to show the ideas behind GP, ie to be
                 tinkered with rather than to be a definitive
                 implementation.

                 The appendix contains a glossary of evolutionary
                 computing terms",
  notes =        "C Code available from same ftp site.",
  size =         "14 pages",
}

@TechReport{Langdon:1995:GPdataRN,
  author =       "W. B. Langdon",
  title =        "Evolving Data Structures Using Genetic Programming",
  institution =  "UCL",
  year =         "1995",
  type =         "Research Note",
  number =       "RN/95/1",
  address =      "Gower Street, London, WC1E 6BT, UK",
  month =        jan,
  keywords =     "Genetic Programming, Genetic Algorithms, Automatic
                 Programming, Machine Learning, Artificial Evolution,
                 Data Structures, Object Oriented Programming, Push down
                 Stack, First-in first-out (FIFO) Queue, Automatically
                 Defined Functions (ADF), Pareto fitness, Demes",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/GPdata_icga-95.ps",
  abstract =     "Genetic programming (GP) is a subclass of genetic
                 algorithms (GAs), in which evolving programs are
                 directly represented in the chromosome as trees.
                 Recently it has been shown that programs which
                 explicitly use directly addressable memory can be
                 generated using GP.

                 It is established good software engineering practice to
                 ensure that programs use memory via abstract data
                 structures such as stacks, queues and lists. These
                 provide an interface between the program and memory,
                 freeing the program of memory management details which
                 are left to the data structures to implement. The main
                 result presented herein is that GP can automatically
                 generate stacks and queues.

                 Typically abstract data structures support multiple
                 operations, such as put and get. We show that GP can
                 simultaneously evolve all the operations of a data
                 structure by implementing each such operation with its
                 own independent program tree. That is, the chromosome
                 consists of a fixed number of independent program
                 trees. Moreover, crossover only mixes genetic material
                 of program trees that implement the same operation.
                 Program trees interact with each other only via shared
                 memory and shared ``Automatically Defined Functions''
                 (ADFs).

                 ADFs, ``pass by reference'' when calling them, Pareto
                 selection, ``good software engineering practice'' and
                 partitioning the genetic population into ``demes''
                 where also investigated whilst evolving the queue in
                 order to improve the GP solutions.",
  notes =        "Discussed on GP mailing list 6--13 Jan 95,
                 subj:GPdata. Presented at ICGA-95. Reworked into
                 Langdon:1995:GPdata",
  size =         "10 pages",
}

@InProceedings{Langdon:1995:GPdata,
  author =       "W. B. Langdon",
  title =        "Evolving Data Structures Using Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "295--302",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms, Automatic
                 Programming, Machine Learning, Artificial Evolution,
                 Data Structures, Object Oriented Programming, Push down
                 Stack, First-in first-out (FIFO) Queue, Automatically
                 Defined Functions (ADF), Pareto fitness, Demes",
  ISBN =         "1-55860-370-0",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/GPdata_icga-95.ps",
  size =         "8 pages",
  abstract =     "Genetic programming (GP) is a subclass of genetic
                 algorithms (GAs), in which evolving programs are
                 directly represented in the chromosome as trees.
                 Recently it has been shown that programs which
                 explicitly use directly addressable memory can be
                 generated using GP.

                 It is established good software engineering practice to
                 ensure that programs use memory via abstract data
                 structures such as stacks, queues and lists. These
                 provide an interface between the program and memory,
                 freeing the program of memory management details which
                 are left to the data structures to implement. The main
                 result presented herein is that GP can automatically
                 generate stacks and queues. Typically abstract data
                 structures support multiple operations, such as put and
                 get. We show that GP can simultaneously evolve all the
                 operations of a data structure by implementing each
                 such operation with its own independent program tree.
                 That is, the chromosome consists of a fixed number of
                 independent program trees. Moreover, crossover only
                 mixes genetic material of program trees that implement
                 the same operation. Program trees interact with each
                 other only via shared memory and shared ``Automatically
                 Defined Functions'' (ADFs).

                 ADFs, ``pass by reference'' when calling them, Pareto
                 selection, ``good software engineering practice'' and
                 partitioning the genetic population into ``demes''
                 where also investigated whilst evolving the queue in
                 order to improve the GP solutions.",
  notes =        "Discussed on GP mailing list 6--13 Jan 95, subj:
                 GPdata. Mainly as Langdon:1995:GPdataRN but with more
                 details on pareto selection",
}

@TechReport{Langdon:1995:ppp,
  author =       "W. B. Langdon",
  title =        "Pareto, Population Partitioning, Price and Genetic
                 Programming",
  institution =  "University College London",
  year =         "1995",
  type =         "Research Note",
  number =       "RN/95/29",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        apr,
  keywords =     "Genetic Programming, Genetic Algorithms, Automatic
                 Programming, Machine Learning, Artificial Evolution,
                 Pareto fitness, Demes",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_aaai-pppGP.ps",
  abstract =     "A description of a use of Pareto optimality in genetic
                 programming is given and an analogy with Genetic
                 Algorithm fitness niches is drawn. Techniques to either
                 spread the population across many pareto optimal
                 fitness values or to reduce the spread are described.
                 It is speculated that a wide spread may not aid Genetic
                 Programming. It is suggested that this might give
                 useful insight into many GPs whose fitness is composed
                 of several sub-objectives.

                 The successful use of demic populations in GP leads to
                 speculation that smaller evolutionary steps might aid
                 GP in the long run.

                 An example is given where Price's covariance theorem
                 helped when designing a GP fitness function.",
  notes =        "Accepted by AAAI Fall 1995 Genetic Programming
                 Symposium but withdrawn due to time pressures",
  size =         "11 pages",
}

@TechReport{langdon:1995:dc,
  author =       "W. B. Langdon",
  title =        "Directed Crossover within Genetic Programming",
  institution =  "University College London",
  year =         "1995",
  type =         "Research Note",
  number =       "RN/95/71",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        sep,
  keywords =     "Genetic Programming, Genetic Algorithms, Automatic
                 Programming, Machine Learning, Artificial Evolution",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/directed_crossover.ps",
  abstract =     "This paper describes in detail a mechanism used to
                 bias the choice of crossover locations when evolving a
                 list data structure using genetic programming. The data
                 structure and it evolution will be described by RN
                 95/70.

                 The second section describes current research on
                 biasing the action of reproduction operators within the
                 genetic programming field.",
  size =         "4 pages",
}

@TechReport{langdon:1995:aigpRN,
  author =       "W. B. Langdon",
  title =        "Data Structures and Genetic Programming",
  institution =  "University College London",
  year =         "1995",
  type =         "Research Note",
  number =       "RN/95/70",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        sep,
  keywords =     "Genetic Programming, Genetic Algorithms, list,
                 evolution, automatic code generation",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.aigp2.ch20.ps",
  abstract =     "In real world applications, software engineers
                 recognise the use of memory must be organised via data
                 structures and that software using the data must be
                 independant of the data structures' implementation
                 details. They achieve this by using abstract data
                 structures, such as records, files and buffers.

                 We demonstrate that genetic programming can
                 automatically implement simple abstract data
                 structures, considering in detail the task of evolving
                 a list. We show general and reasonably efficient
                 implementations can be automatically generated from
                 simple primitives.

                 A model for maintaining evolved code is demonstrated
                 using the list problem.

                 4 page early abstract at
                 ftp://cs.ucl.ac.uk/genetic/papers/GPlist_aigp2.ps",
  notes =        "As langdon:1996:aigp2 Syntax rules restrict for while
                 loops so they are not nested. Run time data used to
                 guide location of crossover points CPU and memory usage
                 included in fitness via Pareto tournament selection.
                 Pass by reference and call by reference used
                 inconjunction with ADFs. Scoping rules allow ADFs
                 access to callers data.",
  size =         "20 pages",
}

@TechReport{langdon:1995:survey,
  author =       "William B. Langdon and Adil Qureshi",
  title =        "Genetic Programming -- Computers using ``Natural
                 Selection'' to generate programs",
  institution =  "University College London",
  year =         "1995",
  type =         "Research Note",
  number =       "RN/95/76",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Automatic
                 Programming, Machine Learning",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/surveyRN76.ps",
  abstract =     "Computers that ``program themselves''; science fact or
                 fiction? Genetic Programming uses novel optimisation
                 techniques to ``evolve'' simple programs; mimicking the
                 way humans construct programs by progressively
                 re-writing them. Trial programs are repeatedly modified
                 in the search for ``better/fitter'' solutions. The
                 underlying basis is Genetic Algorithms (GAs).

                 Genetic Algorithms, pioneered by Holland, Goldberg and
                 others, are evolutionary search techniques inspired by
                 natural selection (i.e\ survival of the fittest). GAs
                 work with a ``population'' of trial solutions to a
                 problem, frequently encoded as strings, and repeatedly
                 select the ``fitter'' solutions, attempting to evolve
                 better ones. The power of GAs is being demonstrated for
                 an increasing range of applications; financial,
                 imaging, VLSI circuit layout, gas pipeline control and
                 production scheduling. But one of the most intriguing
                 uses of GAs - driven by Koza - is automatic program
                 generation.

                 Genetic Programming applies GAs to a ``population'' of
                 programs - typically encoded as tree-structures. Trial
                 programs are evaluated against a ``fitness function''
                 and the best solutions selected for modification and
                 re-evaluation. This modification-evaluation cycle is
                 repeated until a ``correct'' program is produced. GP
                 has demonstrated its potential by evolving simple
                 programs for medical signal filters, classifying news
                 stories, performing optical character recognition, and
                 for target identification.

                 This paper surveys the exciting field of Genetic
                 Programming. As a basis it reviews Genetic Algorithms
                 and automatic program generation. Next it introduces
                 Genetic Programming, describing its history and
                 describing the technique via a worked example in C.
                 Then using a taxonomy that divides GP researchs into
                 theory/techniques and applications, it surveys recent
                 work from both of these perspectives.

                 Extensive bibliographies, glossaries and a resource
                 list are included as appendices.",
  size =         "45 pages",
}

@InCollection{langdon:1996:aigp2,
  author =       "William B. Langdon",
  title =        "Data Structures and Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "395--414",
  chapter =      "20",
  address =      "Cambridge, MA, USA",
  keywords =     "Genetic Programming, Genetic Algorithms, list,
                 evolution, automatic code generation",
  ISBN =         "0-262-01158-1",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.aigp2.ch20.ps",
  abstract =     "In real world applications, software engineers
                 recognise the use of memory must be organised via data
                 structures and that software using the data must be
                 independant of the data structures' implementation
                 details. They achieve this by using abstract data
                 structures, such as records, files and buffers.

                 We demonstrate that genetic programming can
                 automatically implement simple abstract data
                 structures, considering in detail the task of evolving
                 a list. We show general and reasonably efficient
                 implementations can be automatically generated from
                 simple primitives.

                 A model for maintaining evolved code is demonstrated
                 using the list problem.",
  notes =        "Syntax rules restrict for while loops so they are not
                 nested. Run time data used to guide location of
                 crossover points CPU and memory usage included in
                 fitness via Pareto tournament selection. Pass by
                 reference and call by reference used inconjunction with
                 ADFs. Scoping rules allow ADFs access to caller's
                 argument directly.",
  size =         "20 pages",
}

@InCollection{biblio:1996:aigp2,
  author =       "William B. Langdon",
  title =        "A Bibliography for Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "507--532",
  chapter =      "B",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.aigp2.appx.ps",
  URL =          "ftp://cs.ucl.ac.uk/genetic/biblio/README.html",
  abstract =     "411 references",
}

@TechReport{Langdon:1996:usedataRN,
  author =       "W. B. Langdon",
  title =        "Using Data Structures within Genetic Programming",
  institution =  "UCL",
  year =         "1996",
  type =         "Research Note",
  number =       "RN/96/1",
  address =      "Gower Street, London, WC1E 6BT, UK",
  month =        jan,
  keywords =     "Genetic Programming, Genetic Algorithms, context free
                 language induction, Reverse Polish Expressions,
                 automatic code generation, Automatic Programming,
                 Machine Learning, Artificial Evolution, Data
                 Structures, Object Oriented Programming, Push down
                 Stack, Automatically Defined Functions (ADF), Pareto
                 fitness, Demes",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.gp96.ps",
  abstract =     "In earlier work we showed that GP can automatically
                 generate simple data types (stacks, queues and lists).
                 The results presented herein show, in some cases,
                 provision of appropriately structured memory can indeed
                 be advantageous to GP in comparison with directly
                 addressable indexed memory.

                 Three ``classic'' problems are solved. The first two
                 require the GP to distinguish between sentences that
                 are in a language and those that are not given positive
                 and negative training examples of the language. The two
                 languages are, correctly nested brackets and a Dyck
                 language (correctly nested brackets of different
                 types). The third problem is to evaluate integer
                 Reverse Polish (postfix) expressions.

                 Comparisons are made between GP attempting to solve
                 these problems when provided with indexed memory or
                 with stack data structures.",
  notes =        "Accepted for presentation at GP96. Updated version in
                 proceedings and url, see Langdon:1996:usedata",
  size =         "10 pages",
}

@InProceedings{Langdon:1996:usedata,
  author =       "W. B. Langdon",
  title =        "Using Data Structures within Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms, context free
                 language induction, context free grammar CFG, matched
                 brackets, Dyck language, Reverse Polish Expressions,
                 automatic code generation, Automatic Programming,
                 Machine Learning, Artificial Evolution, Data
                 Structures, Object Oriented Programming, Push down
                 Stack, Automatically Defined Functions (ADF), Pareto
                 fitness, Demes",
  pages =        "141--148",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.gp96.ps",
  size =         "9 pages",
  abstract =     "Provision of appropriately structured memory is shown,
                 in some cases, to be advantageous to genetic
                 programming (GP) in comparison with directly
                 addressable indexed memory.

                 Three ``classic'' problems are solved. The first two
                 require the GP to distinguish between sentences that
                 are in a context free language and those that are not
                 given positive and negative training examples of the
                 language. The two languages are, correctly nested
                 brackets and a Dyck language (correctly nested brackets
                 of different types). The third problem is to evaluate
                 integer Reverse Polish (postfix)
                 expressions.

                 Comparisons are made between GP attempting to solve
                 these problems when provided with indexed memory or
                 with stack data structures.",
  notes =        "GP-96. Replaces Langdon:1996:usedataRN",
}

@TechReport{langdon:1996:gpdb,
  author =       "W. B. Langdon",
  title =        "Genetic Programming and Databases",
  institution =  "University College London",
  year =         "1996",
  type =         "Internal Note",
  number =       "IN/96/4",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        "11 " # feb,
  keywords =     "Genetic Programming, Genetic Algorithms, Database",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/gpdb.ps",
  notes =        "Short survey",
  size =         "3 pages",
}

@TechReport{langdon:1996gpgridRN,
  author =       "W. B. Langdon",
  title =        "Scheduling Maintenance of Electrical Power
                 Transmission Networks Using Genetic Programming",
  institution =  "University College London",
  year =         "1996",
  type =         "Research Note",
  number =       "RN/96/49",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        "28 " # jun,
  keywords =     "genetic algorithms, genetic programming, scheduling,
                 maintenance, eletrical power transmission network",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.gpgrid_gp96.ps",
  abstract =     "The National Grid Company Plc is responsible for the
                 maintenance of the high voltage electricity
                 transmission network in England and Wales. It must plan
                 maintenance so as to minimize costs taking into
                 account:

                 (1) location and size of demand, (2) generator
                 capacities and availabilities, (3) electricity carrying
                 capacity of the remainder of the network, that part not
                 undergoing maintenance.

                 Previous work showed the combination of a Genetic
                 Algorithm using an order or permutation chromosome
                 combined with hand coded ``Greedy'' Optimizers can
                 readily produce an optimal schedule for a four node
                 test problem. Following this the same GA has been used
                 to find low cost schedules for the South Wales region
                 of the UK high voltage power network.

                 This paper describes the evolution of the best known
                 schedule for the base South Wales problem using Genetic
                 Programming starting from the hand coded heuristics
                 used with the GA.",
  notes =        "See GP96 late breaking papers langdon:1996gpgrid and
                 WSC1 see langdon:1996gpgridWSC Previous work in
                 Langdon:1995:4nodeSV",
  size =         "10 pages",
}

@InProceedings{langdon:1996gpgrid,
  author =       "W. B. Langdon",
  title =        "Scheduling Maintenance of Electrical Power
                 Transmission Networks Using Genetic Programming",
  booktitle =    "Late Breaking Papers at the GP-96 Conference",
  year =         "1996",
  editor =       "John Koza",
  pages =        "107--116",
  address =      "Stanford, CA, USA",
  publisher_address = "Stanford, California, 94305-3079 USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming, scheduling,
                 maintenance, eletrical power transmission network",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.gpgrid_gp96.ps",
  size =         "10 pages",
  abstract =     "The National Grid Company Plc is responsible for the
                 maintenance of the high voltage electricity
                 transmission network in England and Wales. It must plan
                 maintenance so as to minimize costs taking into
                 account:

                 (1) location and size of demand, (2) generator
                 capacities and availabilities, (3) electricity carrying
                 capacity of the remainder of the network, that part not
                 undergoing maintenance.

                 Previous work showed the combination of a Genetic
                 Algorithm using an order or permutation chromosome
                 combined with hand coded ``Greedy'' Optimizers can
                 readily produce an optimal schedule for a four node
                 test problem. Following this the same GA has been used
                 to find low cost schedules for the South Wales region
                 of the UK high voltage power network.

                 This paper describes the evolution of the best known
                 schedule for the base South Wales problem using Genetic
                 Programming starting from the hand coded heuristics
                 used with the GA.",
  notes =        "As langdon:1996gpgridRN and WSC1 see
                 langdon:1996gpgridWSC Previous work in
                 Langdon:1995:4nodeSV",
  notes =        "GP-96LB",
}

@InProceedings{langdon:1996gpgridWSC,
  author =       "W. B. Langdon",
  title =        "Scheduling Maintenance of Electrical Power
                 Transmission Networks Using Genetic Programming",
  booktitle =    "The 1st Online Workshop on Soft Computing (WSC1)",
  year =         "1996",
  address =      "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/",
  month =        "19--30 " # aug,
  organisation = "Research Group on ECOmp of the Society of Fuzzy Theory
                 and Systems (SOFT)",
  publisher =    "Nagoya University, Japan",
  keywords =     "genetic algorithms, genetic programming, scheduling,
                 maintenance, eletrical power transmission network",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.gpgrid_gp96.ps",
  url_2 =        "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/files/langdon.ps",
  URL =          "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/files/langdon.ps.gz",
  abstract =     "The National Grid Company Plc is responsible for the
                 maintenance of the high voltage electricity
                 transmission network in England and Wales. It must plan
                 maintenance so as to minimize costs taking into
                 account:

                 (1) location and size of demand, (2) generator
                 capacities and availabilities, (3) electricity carrying
                 capacity of the remainder of the network, that part not
                 undergoing maintenance.

                 Previous work showed the combination of a Genetic
                 Algorithm using an order or permutation chromosome
                 combined with hand coded ``Greedy'' Optimizers can
                 readily produce an optimal schedule for a four node
                 test problem. Following this the same GA has been used
                 to find low cost schedules for the South Wales region
                 of the UK high voltage power network.

                 This paper describes the evolution of the best known
                 schedule for the base South Wales problem using Genetic
                 Programming starting from the hand coded heuristics
                 used with the GA.",
  size =         "10 pages",
  notes =        "As langdon:1996gpgridRN email WSC1 organisers
                 wsc@bioele.nuee.nagoya-u.ac.jp",
}

@InCollection{langdon:1997:gpgridIEE,
  author =       "W. B. Langdon and P. C. Treleaven",
  title =        "Scheduling Maintenance of Electrical Power
                 Transmission Networks Using Genetic Programming",
  booktitle =    "Artificial Intelligence Techniques in Power Systems",
  publisher =    "IEE",
  year =         "1997",
  editor =       "Kevin Warwick and Arthur Ekwue and Raj Aggarwal",
  chapter =      "10",
  pages =        "220--237",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-85296-897-3",
  URL =          "http://www.iee.org/Publish/Books/Power/Po022c.cfm#10.Scheduling",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/grid_iee/",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_iee_gpgrid.prn.gz",
  abstract =     "The National Grid Company Plc is responsible for the
                 maintenance of the high voltage electricity
                 transmission network in England and Wales. It must plan
                 maintenance so as to minimize costs taking into
                 account:

                 (1) location and size of demand, (2) generator
                 capacities and availabilities, (3) electricity carrying
                 capacity of the remainder of the network, that part not
                 undergoing maintenance.

                 Previous work showed the combination of a Genetic
                 Algorithm using an order or permutation chromosome
                 combined with hand coded ``Greedy'' Optimizers can
                 readily produce an optimal schedule for a four node
                 test problem. Following this the same GA has been used
                 to find low cost schedules for the South Wales region
                 of the UK high voltage power network.

                 This paper describes the evolution of the best known
                 schedule for the base South Wales problem using Genetic
                 Programming starting from the hand coded heuristics
                 used with the GA.",
  notes =        "update of langdon:1996gpgridRN Book published in
                 conjunction with IEE Workshop January 1997
                 http://www.iee.org.uk/publish/books/power.html#Artificial_intelligence",
  notes =        "wbl_iee_gpgrid.prn is MS Postscript and does not
                 display in some versions of ghostview, seems to print
                 ok",
}

@TechReport{langdon:1996:eGPpRN,
  author =       "W. B. Langdon",
  title =        "Evolution of Genetic Programming Populations",
  institution =  "University College London",
  year =         "1996",
  type =         "Research Note",
  number =       "RN/96/125",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        sep,
  keywords =     "Genetic Programming, Genetic Algorithms, population
                 variety, diversity, genetic programming, Price's
                 theorem, Fisher's theorem, evolution, automatic code
                 generation",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/WBL.ecj.price.125.ps.gz",
  abstract =     "We investigate in detail what happens as genetic
                 programming (GP) populations evolve. Since we shall use
                 the populations which showed GP can evolve stack data
                 structures as examples, we start in Section 1 by
                 briefly describing the stack experiment
                 \cite{Langdon:1995:GPdata}. In Section 2 we show
                 Price's Covariance and Selection Theorem can be applied
                 to Genetic Algorithms (GAs) and GP to predict changes
                 in gene frequencies. We follow the proof of the theorem
                 with experimental justification using the GP runs from
                 the stack problem. Section 3 briefly describes Fisher's
                 Fundamental Theorem of Natural Selection and shows in
                 its normal interpretation it does not apply to
                 practical GAs.

                 An analysis of the stack populations, in Section 4,
                 explains that the difficulty of the stack problem is
                 due to the presence of ``deceptive'' high scoring
                 partial solutions in the population. These cause a
                 negative correlation between necessary primitives and
                 fitness. As Price's Theorem predicts, the frequency of
                 necessary primitives falls, eventually leading to their
                 extinction and so to the impossibility of finding
                 solutions like those that are evolved in successful
                 runs.

                 Section 4 investigates the evolution of variety in GP
                 populations. Detailed measurements of the evolution of
                 variety in stack populations reveal loss of diversity
                 causing crossover to produce offspring which are copies
                 of their parents. Section 5 concludes with measurements
                 that show in the stack population crossover readily
                 produces improvements in performance initially but
                 later no improvements at all are made by
                 crossover.

                 Section 6 discusses the importance of these results to
                 GP in general.

                 ",
  notes =        "Abridgment of Chapter 7 of langdon:thesis",
  size =         "48 pages, double spaced",
}

@PhdThesis{langdon:thesis,
  author =       "W. B. Langdon",
  title =        "Data Structures and Genetic Programming",
  school =       "University College, London",
  year =         "1996",
  month =        "27 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  size =         "350 pages",
  abstract =     "This thesis investigates the evolution and use of
                 abstract data types within Genetic Programming (GP). In
                 genetic programming the principles of natural evolution
                 (fitness based selection and recombination) acts on
                 program code to automatically generate computer
                 programs. The research in this thesis is motivated by
                 the observation from software engineering that data
                 abstraction (eg via abstract data types) is essential
                 in programs created by human programmers. We
                 investigate whether abstract data types can be
                 similarly beneficial to the automatic production of
                 programs using GP.

                 GP can automatically ``evolve'' programs which solve
                 non-trivial problems but few experiments have been
                 reported where the evolved programs explicitly
                 manipulate memory and yet memory is an essential
                 component of most computer programs. So far work on
                 evolving programs that explicitly use memory has
                 principally used either problem specific memory models
                 or a simple indexed memory model consisting of a single
                 global shared array. Whilst the latter is potentially
                 sufficient to allow any computation to evolve, it is
                 unstructured and allows complex interaction between
                 parts of programs which weaken their modularity. In
                 software engineering this is addressed by controlled
                 use of memory using scoping rules and abstract data
                 types, such as stacks, queues and files. This thesis
                 makes five main contributions: (1) Proving that
                 abstract data types (stacks, queues and lists) can be
                 evolved using genetic programming. (2) Demonstrating GP
                 can evolve general programs which recognise a Dyck
                 context free language, evaluate Reverse Polish
                 expressions and GP with an appropriate memory structure
                 can solve the nested brackets problem which had
                 previously been solved using a hybrid GP. (3) In these
                 three cases (Dyck, expression evaluation and nested
                 brackets) an appropriate data structure is proved to be
                 beneficial compared to indexed memory. (4)
                 Investigations of real world electrical network
                 maintenance scheduling problems demonstrate that
                 Genetic Algorithms can find low cost viable solutions
                 to such problems. (5) A taxonomy of GP is presented,
                 including a critical review of experiments with
                 evolving memory. These contributions support our thesis
                 that data abstraction can be beneficial to automatic
                 program generation via artificial evolution.",
}

@TechReport{Langdon97,
  author =       "W. B. Langdon and R. Poli",
  title =        "Price's Theorem and the {MAX} Problem",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-97-4",
  month =        jan,
  year =         "1997",
  file =         "/1997/CSRP-97-04.ps.gz",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-04.ps.gz",
  abstract =     "We present a detailed analysis of the evolution of GP
                 populations using the problem of finding a program
                 which returns the maximum possible value for a given
                 terminal and function set and a depth limit on the
                 program tree (known as the MAX problem). We confirm the
                 basic message of \cite{Gathercole:1996:aicrtd} that
                 crossover together with program size restrictions can
                 be responsible for premature convergence to a
                 sub-optimal solution. We show that this can happen even
                 when the population retains a high level of variety and
                 show that in many cases evolution from the sub-optimal
                 solution to the solution is possible if sufficient time
                 is allowed. In both cases theoretical models are
                 presented and compared with actual runs. Experimental
                 evidence is presented that Price's Covariance and
                 Selection Theorem can be applied to GP populations and
                 the practical effect of program size restrictions are
                 noted. Finally we show that covariance between gene
                 frequency and fitness in the first few generations can
                 be used to predict the course of GP runs.",
}

@InProceedings{langdon:1997:MAX,
  author =       "W. B. Langdon and R. Poli",
  title =        "An Analysis of the {MAX} Problem in Genetic
                 Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "222--230",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/WBL.max_gp97.ps",
  size =         "9 pages",
  abstract =     "We present a detailed analysis of the evolution of GP
                 populations using the problem of finding a program
                 which returns the maximum possible value for a given
                 terminal and function set and a depth limit on the
                 program tree (known as the MAX problem). We confirm the
                 basic message of \cite{Gathercole:1996:aicrtd} that
                 crossover together with program size restrictions can
                 be responsible for premature convergence to a
                 sub-optimal solution. We show that this can happen even
                 when the population retains a high level of variety and
                 show that in many cases evolution from the sub-optimal
                 solution to the solution is possible if sufficient time
                 is allowed. In both cases theoretical models are
                 presented and compared with actual runs. Price's
                 Covariance and Selection Theorem is experimentally
                 tested on GP populations. It is shown to hold only in
                 some cases, in others program size restrictions cause
                 important deviation from its predictions.",
  notes =        "GP-97 Considerable update of Langdon97",
}

@TechReport{Langdon:1997:bloatTR,
  author =       "W. B. Langdon and R. Poli",
  title =        "Fitness Causes Bloat",
  institution =  "University of Birmingham, School of Computer Science",
  address =      "Birmingham, B15 2TT, UK",
  number =       "CSRP-97-09",
  month =        "24 " # feb,
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/1997/CSRP-97-09.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-09.ps.gz",
  abstract =     "The problem of evolving an artificial ant to follow
                 the Santa Fe trail is used to demonstrate the well
                 known genetic programming feature of growth in solution
                 length. Known variously as ``bloat'', ``redundancy'',
                 ``introns'', ``fluff'', ``Structural Complexity'' with
                 antonyms ``parsimony'', ``Minimum Description Length''
                 (MDL) and ``Occam's razor''. Comparison with runs with
                 and without fitness selection pressure shows the
                 tendency for solutions to grow in size is caused by
                 fitness based selection. We argue that such growth is
                 inherent in using a fixed evaluation function with a
                 discrete but variable length representation. Since with
                 simple static evaluation search converges to mainly
                 finding trial solutions with the same fitness as
                 existing trial solutions. In general variable length
                 allows many more long representations of a given
                 solution than short ones of the same solution. Thus
                 with an unbiased random search we expect longer
                 representations to occur more often and so
                 representation length tends to increase. I.e. fitness
                 based selection leads to bloat.",
  size =         "16 pages",
}

@InProceedings{Langdon:1997:bloatWSC2,
  author =       "W. B. Langdon and R. Poli",
  title =        "Fitness Causes Bloat",
  booktitle =    "Soft Computing in Engineering Design and
                 Manufacturing",
  year =         "1997",
  editor =       "P. K. Chawdhry and R. Roy and R. K. Pant",
  pages =        "13--22",
  publisher_address = "Godalming, GU7 3DJ, UK",
  month =        "23-27 " # jun,
  publisher =    "Springer-Verlag London",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-76214-0",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/WBL.bloat_wsc2.ps.gz",
  URL =          "http://www.cs.bham.ac.uk/~wbl/bloat_wsc2/bloat_wsc2.html",
  URL =          "http://www.bath.ac.uk/Departments/Eng/wsc2/ind_paper/p_langd.html",
  abstract =     "The problem of evolving an artificial ant to follow
                 the Santa Fe trail is used to demonstrate the well
                 known genetic programming feature of growth in solution
                 length. Known variously as ``bloat'', ``redundancy'',
                 ``introns'', ``fluff'', ``Structural Complexity'' with
                 antonyms ``parsimony'', ``Minimum Description Length''
                 (MDL) and ``Occam's razor''. Comparison with runs with
                 and without fitness selection pressure shows the
                 tendency for solutions to grow in size is caused by
                 fitness based selection. We argue that such growth is
                 inherent in using a fixed evaluation function with a
                 discrete but variable length representation. Since with
                 simple static evaluation search converges to mainly
                 finding trial solutions with the same fitness as
                 existing trial solutions. In general variable length
                 allows many more long representations of a given
                 solution than short ones of the same solution. Thus
                 with an unbiased random search we expect longer
                 representations to occur more often and so
                 representation length tends to increase. I.e. fitness
                 based selection leads to bloat.",
  notes =        "WSC2 Second On-line World Conference on Soft Computing
                 in Engineering Design and Manufacturing extends
                 Langdon:1997:bloatTR",
  size =         "10 pages",
}

@TechReport{Langdon:1997:bloatICGA,
  author =       "W. B. Langdon",
  title =        "Fitness Causes Bloat in Variable Size
                 Representations",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-97-14",
  month =        "14 " # may,
  year =         "1997",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 variable size representation",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-14.ps.gz",
  abstract =     "We argue based upon the numbers of representations of
                 given length, that increase in representation length is
                 inherent in using a fixed evaluation function with a
                 discrete but variable length representation. Two
                 examples of this are analysed, including the use of
                 Price's Theorem. Both examples confirm the tendency for
                 solutions to grow in size is caused by fitness based
                 selection.",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html

                 based upon Langdon:1997:bloatWSC2 but includes data on
                 mutation",
  size =         "3 pages",
}

@InProceedings{Langdon:1997:bloatMUT,
  author =       "W. B. Langdon and R. Poli",
  title =        "Fitness Causes Bloat: Mutation",
  booktitle =    "Late Breaking Papers at the GP-97 Conference",
  year =         "1997",
  editor =       "John Koza",
  pages =        "132--140",
  address =      "Stanford, CA, USA",
  publisher_address = "Stanford, California, 94305-3079 USA",
  month =        "13-16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  file =         "/1997/CSRP-97-16.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-16.ps.gz",
  abstract =     "The problem of evolving, using mutation, an artificial
                 ant to follow the Santa Fe trail is used to study the
                 well known genetic programming feature of growth in
                 solution length. Known variously as ``bloat'',
                 ``fluff'' and increasing ``structural complexity'',
                 this is often described in terms of increasing
                 ``redundancy'' in the code caused by
                 ``introns''.

                 Comparison between runs with and without fitness
                 selection pressure, backed by Price's Theorem, shows
                 the tendency for solutions to grow in size is caused by
                 fitness based selection. We argue that such growth is
                 inherent in using a fixed evaluation function with a
                 discrete but variable length representation. With
                 simple static evaluation search converges to mainly
                 finding trial solutions with the same fitness as
                 existing trial solutions. In general variable length
                 allows many more long representations of a given
                 solution than short ones. Thus in search (without a
                 length bias) we expect longer representations to occur
                 more often and so representation length to tend to
                 increase. I.e. fitness based selection leads to
                 bloat.",
  size =         "9 pages",
  notes =        "GP-97LB Also available as University of Birmingham,
                 School of Computer Science, CSRP-97-16

                 The email address for the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@Misc{Langdon:1997:bloatMUTposter,
  author =       "W. B. Langdon",
  title =        "Fitness Causes Bloat: Mutation",
  year =         "1997",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~wbl/WBL.gp97lb.poster/WBL.gp97lb.poster.html",
  size =         "4 Pages",
  notes =        "Poster for Langdon:1997:bloatMUT Includes data on runs
                 with no effective size limit and parents of children
                 worse than themselves",
}

@TechReport{langdon:1997:grow,
  author =       "Bill Langdon and Chris Clack",
  title =        "Software -- The Next Generation: Grow Your Own
                 Programs",
  institution =  "UCL, Andersen Consulting",
  year =         "1997",
  type =         "white paper",
  address =      "University College London, Gower Street, London",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "45--53",
  notes =        "Part of {"}Emerging Technologies White Papers:
                 Software -- The Next Generation{"} which reports the
                 1996 workshop on Emerging technologies held in UCL
                 Computer Science dept. for Andersen Consulting's
                 Emerging Technologies Group and others.",
  size =         "9 pages",
}

@Article{langdon:1997:gp97,
  author =       "W. B. Langdon",
  title =        "{GP97} Conference Report",
  journal =      "EvoNews",
  year =         "1997",
  volume =       "1",
  number =       "5",
  pages =        "4--5",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~wbl/gp97-report.html",
  notes =        "EvoNews - The Newsletter of EvoNet at
                 http://www.dcs.napier.ac.uk/evonet/evonews.htm

                 ",
  size =         "1 page",
  notes =        "GP-97",
}

@Article{langdon:1998:gp97,
  author =       "W. B. Langdon",
  title =        "{GP97} Conference Report",
  journal =      "Robotica",
  year =         "1998",
  volume =       "16",
  number =       "1",
  pages =        "117",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~wbl/gp97-report.html",
  notes =        "

                 ",
  size =         "1 page",
}

@TechReport{langdon:1997:bloatSAHCP,
  author =       "W. B. Langdon",
  title =        "Fitness Causes Bloat: Simulated Annealing, Hill
                 Climbing and Populations",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-97-22",
  month =        "2 " # sep,
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/CSRP-97-22.ps.gz",
  abstract =     "In many cases programs length's increase (known as
                 ``bloat'', ``fluff'' and increasing ``structural
                 complexity'') during artificial evolution. We show
                 bloat is not specific to genetic programming and
                 suggest it is inherent in search techniques with
                 discrete variable length representations using simple
                 static evaluation functions. We investigate the
                 bloating characteristics of three non-population and
                 one population based search techniques using a novel
                 mutation operator.

                 An artificial ant following the Santa Fe trail problem
                 is solved by simulated annealing, hill climbing, strict
                 hill climbing and population based search using two
                 variants of the the new subtree based mutation
                 operator. As predicted bloat is observed when using
                 unbiased mutation and is absent in simulated annealing
                 and both hill climbers when using the length neutral
                 mutation however bloat occurs with both mutations when
                 using a population.

                 We conclude that there are two causes of bloat.

                 ",
}

@InProceedings{langdon:1997:pgSAHCP,
  author =       "W. B. Langdon",
  title =        "The Evolution of Size in Variable Length
                 Representations",
  booktitle =    "1998 IEEE International Conference on Evolutionary
                 Computation",
  year =         "1998",
  pages =        "633--638",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  organisation = "IEEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, structural
                 complexity, introns, Price's Theorem",
  file =         "c109.pdf",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/WBL.wcci98_bloat.ps.gz",
  URL =          "http://ieeexplore.ieee.org/iel4/5621/15048/00700102.pdf",
  size =         "6 pages",
  abstract =     "In many cases programs length's increase (known as
                 {"}bloat{"}, {"}fluff{"} and increasing {"}structural
                 complexity{"}) during artificial evolution. We show
                 bloat is not specific to genetic programming and
                 suggest it is inherent in search techniques with
                 discrete variable length representations using simple
                 static evaluation functions. We investigate the
                 bloating characteristics of three non-population and
                 one population based search techniques using a novel
                 mutation operator.

                 An artificial ant following the Santa Fe trail problem
                 is solved by simulated annealing, hill climbing, strict
                 hill climbing and population based search using two
                 variants of the the new subtree based mutation
                 operator. As predicted bloat is observed when using
                 unbiased mutation and is absent in simulated annealing
                 and both hill climbers when using the length neutral
                 mutation however bloat occurs with both mutations when
                 using a population.

                 We conclude that there are two causes of bloat 1)
                 search operators with no length bias tend to sample
                 bigger trees and 2) competition within populations
                 favours longer programs as they can usually reproduce
                 more accurately.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence

                 based on langdon:1997:bloatSAHCP",
}

@InCollection{langdon:1997:gridOSNOVA,
  author =       "W. B. Langdon",
  title =        "Scheduling Planned Maintenance of Electrical Power
                 Transmission Networks Using Genetic Algorithms",
  booktitle =    "Artificial Neural Networks and Genetic Algorithms in
                 Power Engineering",
  publisher =    "OSNOVA",
  year =         "1997",
  editor =       "Gennady K. Voronovsky and Serguey A. Sergeev",
  address =      "Ukraine",
  note =         "Forthcoming, in Russian",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/CSRP-97-26.ps.gz",
  notes =        "Emphasis on GA but some GP work mentioned Available as
                 CSRP-97-26",
}

@Misc{langdon:1997:evoGP,
  author =       "W. B. Langdon and R. Poli",
  title =        "Genetic Programming in Europe",
  howpublished = "Report of the EvoGP Working Group on Genetic
                 Programming of the European Network of Excellence in
                 Evolutionary Computing",
  year =         "1997",
  month =        "30 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/evogprep.ps",
  size =         "18 pages",
  notes =        "summary available as langdon:1997:evoGP:summary",
}

@Article{langdon:1997:evoGP:summary,
  author =       "W. B. Langdon and R. Poli",
  title =        "Evo{GP} Report Summary",
  journal =      "EvoNews",
  year =         "1998",
  number =       "6",
  pages =        "6",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dcs.napier.ac.uk/evonet",
  size =         "0.5 page",
  notes =        "Summary of langdon:1997:evoGP",
}

@TechReport{langdon:1997:dynbloatTR,
  author =       "W. B. Langdon and R. Poli",
  title =        "Genetic Programming Bloat with Dynamic Fitness",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-97-29",
  month =        "3 " # dec,
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/1997/CSRP-97-29.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-29.ps.gz",
  abstract =     "In artificial evolution individuals which perform as
                 their parents are usually rewarded identically to their
                 parents. We note that Nature is more dynamic and there
                 may be a penalty to pay for doing the same thing as
                 your parents. We report two sets of experiments where
                 static fitness functions are firstly augmented by a
                 penalty for unchanged offspring and secondly the static
                 fitness case is replaced by randomly generated dynamic
                 test cases. We conclude genetic programming, when
                 evolving artificial ant control programs, is
                 surprisingly little effected by large penalties and
                 program growth is observed in all our experiments.",
}

@InProceedings{Langdon:1997:bloatMUTet,
  author =       "W. B. Langdon and R. Poli",
  title =        "Fitness Causes Bloat: Mutation",
  booktitle =    "ET'97 Theory and Application of Evolutionary
                 Computation",
  year =         "1997",
  editor =       "Chris Clack and Kanta Vekaria and Nadav Zin",
  pages =        "59--77",
  address =      "University College London, UK",
  month =        "15 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The problem of evolving, using mutation, an artificial
                 ant to follow the Santa Fe trail is used to study the
                 well known genetic programming feature of growth in
                 solution length. Known variously as {"}bloat'',
                 {"}fluff '' and increasing {"}structural complexity'',
                 this is often described in terms of increasing
                 ``redundancy'' in the code caused by {"}introns''.
                 Comparison between runs with and without fitness
                 selection pressure, backed by Price's Theorem, shows
                 the tendency for solutions to grow in size is caused by
                 fitness based selection. We argue that such growth is
                 inherent in using a fixed evaluation function with a
                 discrete but variable length representation. With
                 simple static evaluation search converges to mainly
                 finding trial solutions with the same fitness as
                 existing trial solutions. In general variable length
                 allows many more long representations of a given
                 solution than short ones. Thus in search (without a
                 length bias) we expect longer representations to occur
                 more often and so representation length to tend to
                 increase. I.e. fitness based selection leads to
                 bloat.",
  notes =        "http://www.cs.ucl.ac.uk/isrg/et97/ As
                 Langdon:1997:bloatMUT but with improved experiments",
}

@InProceedings{Langdon:1998:bloatMUTegp,
  author =       "W. B. Langdon and R. Poli",
  title =        "Fitness Causes Bloat: Mutation",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "37--48",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  email =        "W.B.Langdon@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.euro98_bloatm.ps.gz",
  size =         "12 pages",
  abstract =     "The problem of evolving, using mutation, an artificial
                 ant to follow the Santa Fe trail is used to study the
                 well known genetic programming feature of growth in
                 solution length. Known variously as {"}bloat{"},
                 {"}fluff{"} and increasing {"}structural complexity{"},
                 this is often described in terms of increasing
                 {"}redundancy{"} in the code caused by
                 {"}introns{"}.

                 Comparison between runs with and without fitness
                 selection pressure, backed by Price's Theorem, shows
                 the tendency for solutions to grow in size is caused by
                 fitness based selection. We argue that such growth is
                 inherent in using a fixed evaluation function with a
                 discrete but variable length representation. With
                 simple static evaluation search converges to mainly
                 finding trial solutions with the same fitness as
                 existing trial solutions. In general variable length
                 allows many more long representations of a given
                 solution than short ones. Thus in search (without a
                 length bias) we expect longer representations to occur
                 more often and so representation length to tend to
                 increase. I.e. fitness based selection leads to
                 bloat.",
  notes =        "EuroGP'98 Based on Langdon:1997:bloatMUT",
}

@TechReport{langdon:1998:antspaceTR,
  author =       "W. B. Langdon and R. Poli",
  title =        "Why Ants are Hard",
  institution =  "University of Birmingham, School of Computer Science",
  year =         "1998",
  number =       "CSRP-98-4",
  month =        jan,
  note =         "Presented at GP-98",
  keywords =     "genetic algorithms, genetic programming",
  email =        "W.B.Langdon@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk",
  file =         "/1998/CSRP-98-04.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-04.ps.gz",
  url_2 =        "http://www.cs.bham.ac.uk/~wbl/antspace_csrp-98-04/",
  abstract =     "The problem of programming an artificial ant to follow
                 the Santa Fe trail is used as an example program search
                 space. Analysis of shorter solutions shows they have
                 many of the characteristics often ascribed to manually
                 coded programs. Enumeration of a small fraction of the
                 total search space and random sampling characterise it
                 as rugged with many multiple plateaus split by deep
                 valleys and many local and global optima. This suggests
                 it is difficult for hill climbing algorithms. Analysis
                 of the program search space in terms of fixed length
                 schema suggests it is highly deceptive and that for the
                 simplest solutions large building blocks must be
                 assembled before they have above average fitness. In
                 some cases we show solutions cannot be assembled using
                 a fixed representation from small building blocks of
                 above average fitness. These suggest the Ant problem is
                 difficult for Genetic Algorithms.

                 Random sampling of the program search space suggests on
                 average the density of global optima changes only
                 slowly with program size but the density of neutral
                 networks linking points of the same fitness grows
                 approximately linearly with program length. This is
                 part of the cause of bloat.

                 Previously reported genetic programming, simulated
                 annealing and hill climbing performance is shown not to
                 be much better than random search on the Ant problem.",
  notes =        "See also langdon:1998:antspace",
}

@InProceedings{langdon:1998:antspace,
  author =       "W. B. Langdon and R. Poli",
  title =        "Why Ants are Hard",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "193--201",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  email =        "W.B.Langdon@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming, Santa Fe
                 trail, Random Search (Monte Carlo sampling), Fitness
                 Landscape, Building blocks, Ramped Half-and-half",
  ISBN =         "1-55860-548-7",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/WBL.antspace_gp98.ps.gz",
  size =         "9 pages",
  abstract =     "The problem of programming an artificial ant to follow
                 the Santa Fe trail is used as an example program search
                 space. genetic programming, simulated annealing and
                 hill climbing performance is shown not to be much
                 better than random search on the Ant
                 problem.

                 Enumeration of a small fraction of the total search
                 space and random sampling characterise it as rugged
                 with multiple plateaus split by deep valleys and many
                 local and global optima. This suggests it is difficult
                 for hill climbing algorithms.

                 Analysis of the program search space in terms of fixed
                 length schema suggests it is highly deceptive and that
                 for the simplest solutions large building blocks must
                 be assembled before they have above average fitness. In
                 some cases we show solutions cannot be assembled using
                 a fixed representation from small building blocks of
                 above average fitness. This suggest the Ant problem is
                 difficult for Genetic Algorithms.

                 Random sampling of the program search space suggests on
                 average the density of global optima changes only
                 slowly with program size but the density of neutral
                 networks linking points of the same fitness grows
                 approximately linearly with program length. This is
                 part of the cause of bloat.",
  notes =        "GP-98. Based on langdon:1998:antspaceTR",
}

@InProceedings{langdon:1998:bloatDF,
  author =       "W. B. Langdon and R. Poli",
  title =        "Genetic Programming Bloat with Dynamic Fitness",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "96--112",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  email =        "W.B.Langdon@cwi.nl",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.euro98_bloatd.ps.gz",
  size =         "16 pages",
  abstract =     "In artificial evolution individuals which perform as
                 their parents are usually rewarded identically to their
                 parents. We note that Nature is more dynamic and there
                 may be a penalty to pay for doing the same thing as
                 your parents. We report two sets of experiments where
                 static fitness functions are firstly augmented by a
                 penalty for unchanged offspring and secondly the static
                 fitness case is replaced by randomly generated dynamic
                 test cases. We conclude genetic programming, when
                 evolving artificial ant control programs, is
                 surprisingly little effected by large penalties and
                 program growth is observed in all our experiments.",
  notes =        "EuroGP'98. Based on CSRP-97-29
                 langdon:1997:dynbloatTR",
}

@InProceedings{langdon:1998:antlook,
  author =       "W. B. Langdon",
  title =        "Better Trained Ants",
  booktitle =    "Late Breaking Papers at EuroGP'98: the First European
                 Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and Marc Schoenauer
                 and Terry Fogarty and Wolfgang Banzhaf",
  pages =        "11--13",
  address =      "Paris, France",
  publisher_address = "School of Computer Science",
  month =        "14-15 " # apr,
  publisher =    "CSRP-98-10, The University of Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/CSRP-98-08.ps.gz",
  url_2 =        "http://www.cs.bham.ac.uk/~wbl/antlook_csrp-98-08",
  size =         "3 pages",
  abstract =     "The problem of programming an artificial ant to follow
                 the Santa~Fe trail has been repeatedly used as a
                 benchmark problem. Recently we have shown performance
                 of several techniques is not much better than the best
                 performance obtainable using uniform random search. We
                 suggested that this could be because the program
                 fitness landscape is difficult for hill climbers and
                 the problem is also difficult for Genetic Algorithms as
                 it contains multiple levels of deception.

                 Here we redefine the problem so the ant is obliged to
                 traverse the trail in approximately the correct order.
                 A simple genetic programming system, with no size or
                 depth restriction, is shown to perform approximately
                 three times better with the improved training
                 function.",
  notes =        "EuroGP'98LB part of Poli:1998:egplb Also available as
                 CSRP-98-08 langdon:1998:antlook See also
                 langdon:1998:antlook2TR",
}

@TechReport{langdon:1998:antlook2TR,
  author =       "W. B. Langdon and R. Poli",
  title =        "Better Trained Ants for Genetic Programming",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-98-12",
  month =        apr,
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  email =        "W.B.Langdon@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk",
  file =         "/1998/CSRP-98-12.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-12.ps.gz",
  abstract =     "The problem of programming an artificial ant to follow
                 the Santa Fe trail has been repeatedly used as a
                 benchmark problem in GP. Recently we have shown
                 performance of several techniques is not much better
                 than the best performance obtainable using uniform
                 random search. We suggested that this could be because
                 the program fitness landscape is difficult for hill
                 climbers and the problem is also difficult for Genetic
                 Algorithms as it contains multiple levels of
                 deception.

                 Here we redefine the problem so the ant is (1) obliged
                 to traverse the trail in approximately the correct
                 order, (2) to find food quickly. We also investigate
                 (3) including the ant's speed in the fitness function,
                 either as a linear addition or as a second objective in
                 a multi-objective fitness function, and (4) GP one
                 point crossover.

                 A simple genetic programming system, with no size or
                 depth restriction, is shown to perform approximately
                 three times better with the improved training function.
                 (Extends CSRP-98-08 langdon:1998:antlook)",
}

@Book{langdon:book,
  author =       "William B. Langdon",
  title =        "Genetic Programming and Data Structures: Genetic
                 Programming + Data Structures = Automatic
                 Programming!",
  publisher =    "Kluwer",
  year =         "1998",
  volume =       "1",
  series =       "Genetic Programming",
  address =      "Boston",
  month =        "24 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7923-8135-1",
  email =        "kluwer@wkap.com",
  URL =          "http://www.wkap.nl/prod/b/0-7923-8135-1",
  url_2 =        "http://www.cs.ucl.ac.uk/staff/W.Langdon/gpdata",
  notes =        "Computers that {"}program themselves{"} has long been
                 an aim of computer scientists. Recently genetic
                 programming (GP) has started to show its promise by
                 automatically evolving programs. Indeed in a small
                 number of problems GP has evolved programs whose
                 performance is similar to or even slightly better than
                 that of programs written by people. The main thrust of
                 GP has been to automatically create functions. While
                 these can be of great use they contain no memory and
                 relatively little work has addressed automatic creation
                 of program code including stored data. It is this issue
                 which GENETIC PROGRAMMING AND DATA STRUCTURES
                 addresses. Motivated by the observation from software
                 engineering that data abstraction (e.g., via abstract
                 data types) is essential in programs created by human
                 programmers. This book will show that abstract data
                 types can be similarly beneficial to the automatic
                 production of programs using GP.

                 GENETIC PROGRAMMING AND DATA STRUCTURES shows how
                 abstract data types (stacks, queues and lists) can be
                 evolved using genetic programming, demonstrate GP can
                 evolve general programs which solve the nested brackets
                 problem, recognise a Dyck context free language and
                 implement a simple four function calculator. In these
                 cases an appropriate data structure is beneficial
                 compared to simple indexed memory. This book also
                 includes a survey of GP, including a critical review of
                 experiments with evolving memory and reports
                 investigations of real world electrical network
                 maintenance scheduling problems that demonstrate that
                 Genetic Algorithms can find low cost viable solutions
                 to such problems.

                 GENETIC PROGRAMMING AND DATA STRUCTURES should be of
                 direct interest to computer scientists doing research
                 on genetic programming, genetic algorithms, data
                 structures, and artificial intelligence. In addition,
                 this book will be of interest to practitioners working
                 in all of these areas and to those interested in
                 automatic programming.

                 Contents

                 Foreword by John R. Koza.

                 Preface. 1. Introduction. 2. Survey. 3. Advanced
                 Genetic Programming Techniques. 4. Evolving a Stack. 5.
                 Evolving a Queue. 6. Evolving a List. 7. Problems
                 Solved Using Data Structures. 8. Evolution of GP
                 Populations. 9. Conclusions.

                 Appendices: A. Number of Fitness Evaluations Required.
                 B. Glossary. C. Scheduling Planned Maintenance of the
                 NationalGrid. D. Implementation. Index.

                 Kluwer Accademic Publishers, Order Dept., Box 358,
                 Accord Station, Hingham, MA 02018-0358, USA Tel: +1 781
                 871-6600 FAX: +1 781 871-6528",
  size =         "292 pages",
}

@InProceedings{langdon:1998:bool,
  author =       "W. B. Langdon and R. Poli",
  title =        "Boolean Functions Fitness Spaces",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, California, 94305-3079 USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/CSRP-98-16.ps.gz",
  url_2 =        "http://www.cs.bham.ac.uk/~wbl/csrp-98-16",
  size =         "9 pages",
  abstract =     "We investigate the distribution of performance of the
                 Boolean functions of 3 Boolean inputs (particularly
                 that of the parity functions), the always-on-6 and
                 even-6 parity functions. We use enumeration, uniform
                 Monte-Carlo random sampling and sampling random full
                 trees. As expected XOR dramatically changes the fitness
                 distributions. In all cases once some minimum size
                 threshold has been exceeded, the distribution of
                 performance is approximately independent of program
                 length. However the distribution of the performance of
                 full trees is different from that of asymmetric trees
                 and varies with tree depth.

                 We consider but reject testing the No Free Lunch (NFL)
                 theorems on these functions.",
  notes =        "GP-98LB, see also langdon:1999:bool,
                 langdon:1999:sptfs",
}

@TechReport{langdon:1998:BBparity,
  author =       "W. B. Langdon and R. Poli",
  title =        "Why ``Building Blocks'' Don't Work on Parity
                 Problems",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-98-17",
  month =        "13 " # jul,
  year =         "1998",
  email =        "W.B.Langdon@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/1998/CSRP-98-17.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-17.ps.gz",
  abstract =     "We investigate the distribution of performance of the
                 parity and always-on Boolean functions given only the
                 appropriate building block. These problems have
                 ``needle in a haystack'' fitness landscape and so are
                 unsuitable for genetic programming or other progressive
                 search techniques. Theoretical analysis shows in the
                 limit as program size grows the density of solutions is
                 independent of size but falls exponentially with number
                 of inputs.",
}

@InCollection{langdon:1999:aigp3,
  author =       "William B. Langdon and Terry Soule and Riccardo Poli
                 and James A. Foster",
  title =        "The Evolution of Size and Shape",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  pages =        "163--190",
  chapter =      "8",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, bloat",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{langdon:1999:bool,
  author =       "W. B. Langdon and R. Poli",
  title =        "Boolean Functions Fitness Spaces",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "1--14",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  size =         "ftp://cs.ucl.ac.uk/genetic/papers/wbl_egp1999.ps.gz",
  abstract =     "We investigate the distribution of performance of the
                 Boolean functions of 3 Boolean inputs (particularly
                 that of the parity functions), the always-on-6 and
                 even-6 parity functions. We use enumeration, uniform
                 Monte-Carlo random sampling and sampling random full
                 trees. As expected XOR dramatically changes the fitness
                 distributions. In all cases once some minimum size
                 threshold has been exceeded, the distribution of
                 performance is approximately independent of program
                 size. However the distribution of the performance of
                 full trees is different from that of asymmetric trees
                 and varies with tree depth.",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@Article{langdon:1999:sptfs,
  author =       "W. B. Langdon",
  title =        "Scaling of Program Tree Fitness Spaces",
  journal =      "Evolutionary Computation",
  year =         "1999",
  volume =       "7",
  number =       "4",
  pages =        "399--428",
  month =        "Winter",
  email =        "W.B.Langdon@cwi.nl",
  keywords =     "genetic algorithms, genetic programming, stochastic
                 search, genetic algorithms, tree fitness landscapes,
                 nand gates, Monte Carlo sampling, symbolic regression,
                 artificial ant",
  ISSN =         "1063-6560",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=7&issue=4",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.fitnessspaces.ps.gz",
  abstract =     "We investigate the distribution of fitness of programs
                 concentrating upon those represented as parse trees,
                 particularly how such distributions scale with respect
                 to changes in size of the programs. By using a
                 combination of enumeration and Monte Carlo sampling on
                 a large number of problems from three very different
                 areas we are lead to suggest, in general, once some
                 minimum size threshold has been exceeded, the
                 distribution of performance is approximately
                 independent of program length. We proof this for linear
                 programs and for simple side effect free parse trees.
                 We give the density of solutions to the parity problems
                 in program trees composed of XOR building blocks.

                 We have so far only conducted limited experiments with
                 programs including side effects and iteration. These
                 suggest a similar result may also hold for this wider
                 class of programs.",
  notes =        "Special issue on scaling See also langdon:1999:bool,
                 langdon:1998:BBparity Slides at
                 http://www.cs.ucl.ac.uk/staff/W.Langdon/fitnessspaces

                 Presented at Schloss Dagstuhl
                 ftp://ftp.dagstuhl.de/pub/Reports/00/00071.ps.gz (page
                 12), and also presented at GECCO'2000",
}

@InProceedings{langdon:1999:fairxo,
  author =       "W. B. Langdon",
  title =        "Size Fair and Homologous Tree Genetic Programming
                 Crossovers",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1092--1097",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, bloat
                 reduction, evolution of shape, subquadratic length
                 growth, linear depth growth, uniform initialisation,
                 binary tree search spaces",
  ISBN =         "1-55860-611-4",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.gecco99.fairxo.ps.gz",
  abstract =     "Size fair and homologous crossover genetic operators
                 for tree based genetic programming are described and
                 tested. Both produce considerably reduced increases in
                 program size and no detrimental effect on GP
                 performance. GP search spaces are partitioned by the
                 ridge in the number of program versus their size and
                 depth. A ramped uniform random initialisation is
                 described which straddles the ridge. With subtree
                 crossover trees increase about one level per generation
                 leading to sub-quadratic bloat in length.",
  notes =        "GECCO-99, part of banzhaf:1999:gecco99 See also
                 langdon:1999:fairxTR A joint meeting of the eighth
                 international conference on genetic algorithms
                 (ICGA-99) and the fourth annual genetic programming
                 conference (GP-99)",
}

@TechReport{langdon:1999:fairxTR,
  author =       "W. B. Langdon",
  title =        "Size Fair and Homologous Tree Crossovers",
  institution =  "Centrum voor Wiskunde en Informatica",
  year =         "1999",
  number =       "SEN-R9907",
  address =      "CWI, P.O. Box 94079, Kruislaan 413, NL-1090 GB
                 Amsterdam, The Netherlands",
  month =        "11 " # apr,
  email =        "W.B.Langdon@cwi.nl",
  keywords =     "genetic algorithms, genetic programming, bloat
                 reduction, evolution of shape, sub-quadratic length
                 growth, linear depth growth, uniform initialisation,
                 binary tree search spaces",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/cwi_fair.ps.gz",
  URL =          "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9907.ps.Z",
  abstract =     "Size fair and homologous crossover genetic operators
                 for tree based genetic programming are described and
                 tested. Both produce considerably reduced increases in
                 program size (i.e. less bloat) and no detrimental
                 effect on GP performance.

                 GP search spaces are partitioned by the ridge in the
                 number of program v. their size and depth. While search
                 efficiency is little effected by initial conditions,
                 these do strongly influence which half of the search
                 space is searched. However a ramped uniform random
                 initialisation is described which straddles the
                 ridge.

                 With subtree crossover trees increase about one level
                 per generation leading to sub-quadratic bloat in
                 program length.",
  notes =        "CWI technical report",
  ISSN =         "1386-369X",
  size =         "23 pages",
}

@Proceedings{langdon:1999:egplb,
  title =        "Late-Breaking Papers of Euro{GP}-99",
  year =         "1999",
  editor =       "W. B. Langdon and Riccardo Poli and Peter Nordin and
                 Terry Fogarty",
  address =      "Goteborg, Sweden",
  month =        "26-27 " # may,
  organisation = "EvoGP",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~rmp/eebic/eurogp99/eurogp99_lbp.html",
  URL =          "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.pdf",
  URL =          "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.ps.Z",
  size =         "33 pages",
  abstract =     "This booklet contains the late-breaking papers of the
                 Second European Workshop on Genetic Programming
                 (EuroGP'99) held in G{\"o}teborg Sweden
                 26--27~May~1999. EuroGP'99 was one of the EvoNet
                 workshops on evolutionary computing, EvoWorkshops'99.
                 The purpose of the late-breaking papers was to provide
                 attendees with information about research that was
                 initiated, enhanced, improved, or completed after the
                 original paper submission deadline in December 1998.

                 To ensure coverage of the most up-to-date research, the
                 deadline for submission was set only a month before the
                 workshop. Late-breaking papers were examined for
                 relevance and quality by the organisers of the
                 EuroGP'99, but no formal review process took place.

                 The 3 late-breaking papers in this booklet (which was
                 distributed at the workshop) were presented during a
                 poster session held on Thursday 27 May 1999 during
                 EuroGP'99.

                 Authors individually retain copyright (and all other
                 rights) to their late-breaking papers. This booklet is
                 available as a technical report SEN-R9913 from Centrum
                 voor Wiskunde en Informatica, Kruislaan 413, NL-1098 SJ
                 Amsterdam
                 http://www.cwi.nl/static/publications/reports/reports.html",
  notes =        "EuroGP'99LB PDF file has formating problems:-( 27 Jul
                 1999",
}

@InProceedings{langdon:1999:fogp,
  author =       "W. B. Langdon",
  title =        "Linear Increase in Tree Height Leads to Sub-Quadratic
                 Bloat",
  booktitle =    "Foundations of Genetic Programming",
  year =         "1999",
  editor =       "Thomas Haynes and William B. Langdon and Una-May
                 O'Reilly and Riccardo Poli and Justinian Rosca",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/WBL.fogp.ps.gz",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/langdon",
  size =         "2 pages",
  notes =        "GECCO'99 WKSHOP, part of haynes:1999:fogp",
}

@InProceedings{langdon:1999:fairx2p,
  author =       "W. B. Langdon",
  title =        "Size Fair Tree Crossovers",
  booktitle =    "Proceedings of the Eleventh Belgium/Netherlands
                 Conference on Artificial Intelligence (BNAIC'99)",
  year =         "1999",
  editor =       "Eric Postma and Marc Gyssen",
  pages =        "255--256",
  address =      "Kasteel Vaeshartelt, Maastricht, Holland",
  month =        "3-4 " # nov,
  organisation = "BNVKI, Dutch and the Belgian AI Association",
  email =        "W.B.Langdon@cwi.nl",
  keywords =     "genetic algorithms, genetic programming, bloat
                 reduction, evolution of shape, sub-quadratic length
                 growth, linear depth growth, uniform initialisation,
                 binary tree search spaces",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/fairxo_bnaic99.ps.gz",
  size =         "2 pages",
  abstract =     "Size fair crossover genetic operator for tree based
                 genetic programming is described and tested. It
                 produces considerably reduced increases in program size
                 and no detrimental effect on GP performance. GP search
                 spaces are partitioned by the ridge in the number of
                 program v. their size and depth. A ramped uniform
                 random initialisation is described which straddles the
                 ridge. With subtree crossover trees increase about one
                 level per generation leading to sub-quadratic bloat in
                 length.",
  notes =        "Resumission of langdon:1999:fairxo
                 http://www.cs.unimaas.nl/~bnvki/bnaic99/",
}

@InProceedings{langdon:2000:seed,
  author =       "W. B. Langdon and J. P. Nordin",
  title =        "Seeding {GP} Populations",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "304--315",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  email =        "W.B.Langdon@cwi.nl nordin@fy.chalmers.se",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_eurogp2000_seed.ps.gz",
  abstract =     "We show GP populations can evolve from ``perfect''
                 programs which match the training material under the
                 influence of a Pareto multi-objective fitness and
                 program size selection scheme to generalise. The
                 technique is demonstrated upon programmatic image
                 compression, two machine learning benchmark problems
                 (Pima Diabetes and Wisconsin Breast Cancer) and a
                 consumer profiling task (Benelearn99).",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@Article{langdon:2000:fairxo,
  author =       "William B. Langdon",
  title =        "Size Fair and Homologous Tree Genetic Programming
                 Crossovers",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "1/2",
  pages =        "95--119",
  month =        apr,
  email =        "W.B.Langdon@cwi.nl",
  keywords =     "genetic algorithms, genetic programming, bloat
                 reduction, evolution of shape, subquadratic length
                 growth, linear depth growth, uniform initialisation,
                 binary tree search spaces",
  ISSN =         "1389-2576",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_fairxo.pdf",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_fairxo.ps.gz",
  size =         "29 pages",
  abstract =     "Size fair and homologous crossover genetic operators
                 for tree based genetic programming are described and
                 tested. Both produce considerably reduced increases in
                 program size (ie less bloat) and no detrimental effect
                 on GP performance.

                 GP search spaces are partitioned by the ridge in the
                 number of program versus their size and depth. While
                 search efficiency is little effected by initial
                 conditions, these do strongly influence which half of
                 the search space is searched. However a ramped uniform
                 random initialisation is described which straddles the
                 ridge.

                 With subtree crossover trees increase about one level
                 per generation leading to sub-quadratic bloat in
                 program length.",
  notes =        "Improved version of langdon:1999:fairxTR See also
                 langdon:1999:fairxo, langdon:1999:fairxo2p",
}

@Article{langdon:1999:geccostudent,
  author =       "W. B. Langdon",
  title =        "{GECCO}'99 Student Workshop",
  journal =      "Newsletter BVNKI",
  year =         "1999",
  volume =       "16",
  number =       "5",
  pages =        "144--143",
  month =        oct,
  email =        "W.B.Langdon@cwi.nl",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1566-8266",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_bnvki_stud99.ps.gz",
  size =         "2 pages",
  notes =        "http://www.cs.unimaas.nl/~bnvki/nieuwsbr.htm

                 See also langdon:2000:geccostudent",
}

@Article{langdon:2000:geccostudent,
  author =       "W. B. Langdon",
  title =        "{GECCO}'99 Student Workshop",
  journal =      "Robotica",
  year =         "2000",
  volume =       "18",
  number =       "1",
  pages =        "87",
  month =        jan,
  email =        "W.B.Langdon@cs.ucl.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.journals.cup.org/bin/bladerunner?REQUNIQ=975926794&REQSESS=1337686&117000REQEVENT=&REQINT1=34524&REQAUTH=0",
  size =         "1 pages",
  notes =        "See also langdon:1999:geccostudent",
}

@Article{langdon:2000:gpembooks,
  author =       "William B. Langdon",
  title =        "Genetic Programming and Evolvable Machines: Books and
                 other Resources",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "1/2",
  pages =        "165--169",
  month =        apr,
  email =        "W.B.Langdon@cwi.nl",
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_gppubs.pdf",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_gppubs.ps.gz",
  size =         "5 pages",
  abstract =     "Presents summaries of published GP/EM literature,
                 Internet based sources of GP/EM information and our
                 plans for future reviews of GP/EM resources (books,
                 world wide web (www) sites, products and programs).",
}

@InProceedings{langdon:1999:benelearn1,
  author =       "W. B. Langdon",
  title =        "Genetic Programming Approach to Benelearn 99: {I}",
  booktitle =    "The Benelearn 1999 Competition",
  year =         "1999",
  editor =       "Peter {van der Putten} and Maarten {van Someren}",
  pages =        "3.5",
  address =      "Sociaal-Wetenschappelijke Informatica, Universiteit
                 van Amsterdam",
  month =        "2 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.benelearn1.99.ps.gz",
  size =         "3 pages",
  abstract =     "We briefly describe our first genetic programming
                 technique used to automatically evolve profiles of
                 potential insurance customers the task is part of the
                 Benelearn'99 competition. The information about
                 customers consists of 86 variables and includes product
                 usage data and socio-demographic data derived from zip
                 codes. The data was supplied by the Dutch data mining
                 company Sentient Machine Research, and is based on real
                 world business data. Profiles which correctly
                 identified more than 40 percent of customers were
                 automatically evolved using genetic programming.",
  notes =        "http://www.swi.psy.uva.nl/benelearn99/comppage.html",
}

@InProceedings{langdon:2000:random,
  author =       "W. B. Langdon and W. Banzhaf",
  title =        "Genetic Programming Bloat without Semantics",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VI 6th
                 International Conference",
  year =         "2000",
  editor =       "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
                 Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
                 Merelo and Hans-Paul Schwefel",
  volume =       "1917",
  series =       "LNCS",
  pages =        "201--210",
  address =      "Paris, France",
  month =        "16-20 " # sep,
  publisher =    "Springer Verlag",
  email =        "W.B.Langdon@cwi.nl, banzhaf@cs.uni-dortmund.de",
  keywords =     "genetic algorithms, genetic programming, evolution of
                 shape, subquadratic length growth, linear depth growth,
                 binary tree search spaces",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/wbl_ppsn2000_poster",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_ppsn2000.ps.gz",
  abstract =     "To investigate the fundamental causes of bloat, six
                 artificial random binary tree search spaces are
                 presented. Fitness is given by program syntax (the
                 genetic programming genotype). GP populations are
                 evolved on both random problems and problems with
                 ``building blocks''. These are compared to problems
                 with explicit ineffective code (introns, junk code,
                 inviable code). Our results suggest the entropy random
                 walk explanation of bloat remains viable. The hard
                 building block problem might be used in further
                 studies, e.g. of standard subtree crossover.",
  notes =        "C++ code at ftp://cs.ucl.ac.uk/wblangdon/gp-code/
                 http://www-syntim.inria.fr/fractales/PPSN2000/program.html#108
                 PPSN'2000

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-41056-2",
}

@InProceedings{langdon:2000:quad,
  author =       "W. B. Langdon",
  title =        "Quadratic Bloat in Genetic Programming",
  pages =        "451--458",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  email =        "W.B.Langdon@cwi.nl",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 introns, ineffective code, evolution of shape,
                 subquadratic length growth, linear depth growth, binary
                 tree search spaces",
  ISBN =         "1-55860-708-0",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL.geccco2000.quad.ps.gz",
  size =         "8 pages",
  abstract =     "In earlier work we predicted program size would grow
                 in the limit at a quadratic rate and up to fifty
                 generations we measured bloat
                 O(generations**(1.2-1.5)). On two simple benchmarks we
                 test the prediction of bloat O(generations**2.0) up to
                 generation 600. In continuous problems the limit of
                 quadratic growth is reached but convergence in the
                 discrete case limits growth in size. Measurements
                 indicate subtree crossover ceases to be disruptive with
                 large programs (1,000,000) and the population
                 effectively converges (even though variety is near
                 unity). Depending upon implementation, we predict run
                 time O(number of generations**(2.0-3.0)) and memory
                 O(number of generations**(1.0-2.0)).",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{langdon:2001:eROC,
  author =       "William B. Langdon and Bernard F. Buxton",
  title =        "Evolving Receiver Operating Characteristics for Data
                 Fusion",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "87--96",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Data Fusion,
                 Data Mining, Knowledge Discovery, Receiver Operating
                 Characteristics, ROC, Combining Classifiers",
  ISBN =         "3-540-41899-7",
  URL =          "http://evonet.dcs.napier.ac.uk/eurogp2001/",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_egp2001.ps.gz",
  size =         "10 pages",
  abstract =     "It has been suggested that the ``Maximum Realisable
                 Receiver Operating Characteristics'' for a combination
                 of classifiers is the convex hull of their individual
                 ROCs [Scott et al., 1998]. As expected in at least some
                 cases better ROCs can be produced. We show genetic
                 programming (GP) can automatically produce a
                 combination of classifiers whose ROC is better than the
                 convex hull of the supplied classifier's ROCs.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{langdon:2001:elvis,
  author =       "William B. Langdon and Peter Nordin",
  title =        "Evolving Hand-Eye Coordination for a Humanoid Robot
                 with Machine Code Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "313--324",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  email =        "W.Langdon@cs.ucl.ac.uk",
  keywords =     "genetic algorithms, genetic programming, Humanoid
                 Robotics, Genetic Reasoning, Brain Building, Robotic
                 Arm, Robots, Inverse Kinematics, Stereo Vision,
                 Discipulus",
  ISBN =         "3-540-41899-7",
  size =         "12 pages",
  URL =          "http://evonet.dcs.napier.ac.uk/eurogp2001/",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_handeye.ps.gz",
  abstract =     "We evolve, using AIMGP machine code genetic
                 programming, Discipulus, an approximation of the
                 inverse kinematics of a real robotics arm with many
                 degrees of freedom. Elvis is a bipedal robot with
                 human-like geometry and motion capabilities --- a
                 humanoid, primarily controlled by evolutionary adaptive
                 methods. The GP system produces a useful inverse
                 kinematic mapping, from target 3-D points (via pairs of
                 stereo video images) to a vector of arm controller
                 actuator set points.",
  notes =        "See movie at
                 http://www.cs.ucl.ac.uk/staff/W.Langdon/elvis

                 EuroGP'2001, part of miller:2001:gp",
}

@Article{langdon:2001:randsearch,
  author =       "W. B. Langdon",
  title =        "Long Random Linear Programs Do Not Generalize",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "2",
  pages =        "95--100",
  month =        jun,
  email =        "W.Langdon@cs.ucl.ac.uk",
  keywords =     "genetic algorithms, genetic programming, generality,
                 random search",
  ISSN =         "1389-2576",
  size =         "6 pages",
  abstract =     "The chance of solving a problem by random search of
                 linear random programs tends to a limit as their size
                 increases. When all outputs are equally used this limit
                 is no more than 2**(-|test set|). Where |test set| is
                 the size of the total test set. This is a
                 generalisation of a previous result,
                 langdon:1999:sptfs.

                 Secondly, we show the chance of finding a long linear
                 general solution by random search is exponentially
                 small.",
  notes =        "No Generalise See also RN/01/14
                 http://www.cs.ucl.ac.uk/staff/W.Langdon/maxproduct/",
}

@InProceedings{langdon:2001:gROC,
  title =        "Genetic Programming for Combining Classifiers",
  author =       "W. B. Langdon and B. F. Buxton",
  pages =        "66--73",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, data fusion,
                 data mining, knowledge discovery, Receiver Operating
                 Characteristics, ensemble of classifiers, size fair
                 crossover",
  ISBN =         "1-55860-774-9",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_gecco2001_roc.ps.gz",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_gecco2001_roc.pdf",
  size =         "8 pages",
  abstract =     "Genetic programming (GP) can automatically fuse given
                 classifiers to produce a combined classifier whose
                 Receiver Operating Characteristics (ROC) are better
                 than scott:1998:BMVC ``Maximum Realisable Receiver
                 Operating Characteristics'' (MRROC). I.e. better than
                 their convex hull. This is demonstrated on artificial,
                 medical and satellite image processing bench marks.",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@Misc{langdon:2001:dea,
  editor =       "W. B. Langdon",
  key =          "W. B. Langdon",
  title =        "Dynamics of Evolutionary Algorithms",
  howpublished = "www",
  year =         "2001",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2001dea.html",
  notes =        "Notes of workshop held at GECCO-2001",
}

@InProceedings{langdon:2001:mcs,
  author =       "W. B. Langdon and B. F. Buxton",
  title =        "Genetic Programming for Improved Receiver Operating
                 Characteristics",
  booktitle =    "Second International Conference on Multiple Classifier
                 System",
  year =         "2001",
  editor =       "Josef Kittler and Fabio Roli",
  volume =       "2096",
  series =       "LNCS",
  pages =        "68--77",
  address =      "Cambridge",
  month =        "2-4 " # jul,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming, data fusion,
                 data mining, knowledge discovery, Receiver Operating
                 Characteristics, ensemble of classifiers, size fair
                 crossover",
  ISBN =         "3-540-42284-6",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_mcs2001.ps.gz",
  size =         "10 pages",
  abstract =     "Genetic programming (GP) can automatically fuse given
                 classifiers to produce a combined classifier whose
                 Receiver Operating Characteristics (ROC) are better
                 than scott:1998:BMVC ``Maximum Realisable Receiver
                 Operating Characteristics'' (MRROC). I.e. better than
                 their convex hull. This is demonstrated on artificial,
                 medical and satellite image processing bench marks.",
  notes =        "http://www.diee.unica.it/mcs/ Technique in
                 langdon:2001:gROC used to combine different classifiers
                 on trained on different data.",
}

@TechReport{langdon:2001:edf,
  author =       "W. B. Langdon",
  title =        "Evolutionary Data Fusion",
  institution =  "University College, London",
  year =         "2001",
  number =       "RN/01/19",
  address =      "UK",
  month =        "3 " # apr,
  keywords =     "genetic algorithms, genetic programming, ROC",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/datafusion.html",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/roc",
  size =         "12 pages",
  notes =        "Distributed at 25 April 2001 Faraday meeting
                 http://www.npl.co.uk/intersect/",
}

@InProceedings{langdon:2001:wsc6,
  author =       "W. B. Langdon and S. J. Barrett and B. F. Buxton",
  title =        "Genetic Programming for Combining Neural Networks for
                 Drug Discovery",
  booktitle =    "Soft Computing and Industry Recent Applications",
  year =         "2001",
  editor =       "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and
                 Takeshi Furuhashi and Frank Hoffmann",
  pages =        "597--608",
  month =        "10--24 " # sep,
  publisher =    "Springer-Verlag",
  note =         "Published 2002",
  email =        "W.Langdon@cs.ucl.ac.uk",
  keywords =     "genetic algorithms, genetic programming, data fusion,
                 data mining, knowledge discovery, Receiver Operating
                 Characteristics, ensemble of classifiers, size fair
                 crossover",
  ISBN =         "1-85233-539-4",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_wsc6.pdf",
  abstract =     "We have previously shown on a range of benchmarks
                 langdon:2001:gROC Genetic programming (GP) can
                 automatically fuse given classifers of diverse types to
                 produce a combined classifer whose Receiver Operating
                 Characteristics (ROC) are better than scott:1998:BMVC's
                 {"}Maximum Realisable Receiver Operating
                 Characteristics{"} (MRROC). I.e. better than their
                 convex hull. Here our technique is used in a blind
                 trial where artifcial neural networks. are trained by
                 Clementine on P450 pharmaceutical data. Using just the
                 networks GP automatically evolves a composite
                 classifer.",
  notes =        "http://www.springer.de/cgi/svcat/search_book.pl?isbn=1-85233-539-4",
}

@TechReport{langdon:2001:pred,
  author =       "W. B. Langdon",
  title =        "Prediction",
  institution =  "Commercial",
  year =         "2001",
  keywords =     "genetic algorithms, genetic programming",
}

@Book{langdon:fogp,
  author =       "W. B. Langdon and Riccardo Poli",
  title =        "Foundations of Genetic Programming",
  publisher =    "Springer-Verlag",
  year =         "2002",
  email =        "W.Langdon@cs.ucl.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-42451-2",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42451-2#english",
  size =         "274 pages",
}

@TechReport{langdon:2002:dagstuhl,
  title =        "Structure of the Genetic Programming Search Space",
  author =       "William B. Langdon",
  editor =       "Hans-Georg Beyer and Ken {De Jong} and Colin Reeves
                 and Ingo Wegener",
  booktitle =    "Theory of Evolutionary Algorithms",
  institution =  "Dagstuhl",
  year =         "2002",
  type =         "Report",
  number =       "330",
  address =      "Germany",
  month =        "13-18 " # jan,
  pages =        "12",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.dagstuhl.de/pub/Reports/02/02031.pdf.gz",
  URL =          "ftp://ftp.dagstuhl.de/pub/Reports/02/02031.ps.gz",
  abstract =     "It is known that the fitness distribution of programs
                 tends to a limit as they get bigger. We use Markov
                 chain convergence theorems to give both upper and lower
                 bounds on program size needed for convergence. Results
                 are presented for four types of computer models. The
                 bounds on the length of random programs needed to
                 converge depends upon the size of memory N. Bounds are
                 exponential in N, N log N and smaller, depending on the
                 model.",
  notes =        "Seminar No. 02031. See also langdon:2002:crlp",
}

@TechReport{langdon:2002:sees,
  author =       "W. B. Langdon",
  title =        "Characteristics of the Genetic Programming Search
                 Space",
  institution =  "University of Hertfordshire, Computer Science",
  booktitle =    "Software Evolution and Evolutionary Computation
                 Symposium Abstracts",
  year =         "2002",
  editor =       "C. L. Nehaniv and M. Loomes and P. Marrow and P.
                 Wernick",
  number =       "364",
  type =         "Technical Report",
  pages =        "10",
  address =      "University of Hertfordshire",
  month =        "2 " # feb,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://homepages.feis.herts.ac.uk/~nehaniv/EN/seec.html",
  size =         "0.25 pages. See also langdon:2002:crlp",
}

@InProceedings{langdon:2002:EuroGP,
  title =        "Combining Decision Trees and Neural Networks for Drug
                 Discovery",
  author =       "William B. Langdon and S. J. Barrett and B. F.
                 Buxton",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "60--70",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  email =        "W.Langdon@cs.ucl.ac.uk B.Buxtong@cs.ucl.ac.uk",
  keywords =     "genetic algorithms, genetic programming, drug design,
                 Receiver Operating Characteristics (ROC), ensemble of
                 classifiers, data fusion, artificial neural networks,
                 clementine, decision trees C4.5, high through put
                 screening (HTS)",
  ISBN =         "3-540-43378-3",
  URL =          "http://link.springer.de/link/service/series/0558/bibs/2278/22780060.htm",
  size =         "10 pages",
  abstract =     "Genetic programming (GP) offers a generic method of
                 automatically fusing together classifiers using their
                 receiver operating characteristics (ROC) to yield
                 superior ensembles. We combine decision trees (C4.5)
                 and artificial neural networks (ANN) on a difficult
                 pharmaceutical data mining (KDD) drug discovery
                 application. Specifically predicting inhibition of a
                 P450 enzyme. Training data came from high throughput
                 screening (HTS) runs. The evolved model may be used to
                 predict behaviour of virtual (i.e. yet to be
                 manufactured) chemicals. Measures to reduce over
                 fitting are also described.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@Proceedings{langdon:2002:GECCO,
  title =        "{GECCO} 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  year =         "2002",
  publisher =    "Morgan Kaufmann Publishers",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  keywords =     "DNA computing, adaptive behavior, agents, ant colony
                 optimization, artificial life, evolution strategies,
                 evolutionary programming, evolutionary robotics,
                 evolutionary routing, evolutionary scheduling evolvable
                 hardware, genetic algorithms, genetic programming,
                 learning classifier systems, methodology, molecular
                 computing, pedagogy, philosophy, real world
                 applications, search-based software engineering",
  ISBN =         "1-55860-878-8",
  URL =          "http://www.isgec.org/GECCO-2002",
  URL =          "http://www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-878-8",
  size =         "1424 pages",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{langdon:2002:crlp,
  title =        "Convergence Rates for the Distribution of Program
                 Outputs",
  author =       "W. B. Langdon",
  pages =        "812--819",
  year =         "2002",
  publisher =    "Morgan Kaufmann Publishers",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  keywords =     "genetic algorithms, genetic programming, Fitness
                 Landscapes, Markov analysis, Mutation convergence time,
                 Total Variation Distance, Markov Minorization, Random
                 walk eigenvalues, Average computer",
  ISBN =         "1-55860-878-8",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_gecco2002.pdf",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_gecco2002.ps.gz",
  size =         "8 pages",
  abstract =     "Fitness distributions (landscapes) of programs tend to
                 a limit as they get bigger. Markov chain convergence
                 theorems give general upper bounds on the linear
                 program sizes needed for convergence. Tight bounds
                 (exponential in N, N log N, and smaller) are given for
                 five computer models (any, average, cyclic, bit flip
                 and Boolean). Mutation randomizes a genetic algorithm
                 population in 0.25 (l+1)(log(l)+4) generations. Results
                 for a genetic programming (GP) like model are confirmed
                 by experiment.",
  notes =        "GECCO-2002 A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002) Part of langdon:2002:GECCO",
}

@Article{langdon:2002:occam,
  author =       "William B. Langdon",
  title =        "Was {Occam} Wrong? Blunting {Occam's} Razor",
  journal =      "BNVKI newsletter",
  year =         "2002",
  volume =       "19",
  number =       "3",
  pages =        "56--57",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1566-8266",
  notes =        "http://www.cs.unimaas.nl/~bnvki/Nieuwsbr.htm",
}

@InProceedings{langdon:2002:geccolb,
  author =       "W. B. Langdon",
  title =        "Random Search is Parsimonious",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  year =         "2002",
  editor =       "Erick Cant{\'u}-Paz",
  pages =        "308--315",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  month =        "9-13 " # jul,
  keywords =     "Genetic Algorithms, Genetic Programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_gecco2002lb.pdf",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_gecco2002lb.ps.gz",
  size =         "8 pages",
  abstract =     "We model in detail the distribution of Boolean
                 functions implemented by random non-recursive programs,
                 similar to linear genetic programming. Most functions
                 are constants, the remainder are mostly simple. Bounds
                 on how long programs need to be before the distribution
                 of their functionality is close to its limiting
                 distribution are provided in general and for average
                 computers.",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 Superseded by langdon:2002:foga",
}

@InProceedings{langdon:2002:foga,
  author =       "W. B. Langdon",
  title =        "How many Good Programs are there? {H}ow Long are
                 they?",
  booktitle =    "Foundations of Genetic Algorithms {VII}",
  year =         "2002",
  editor =       "Jonathan Rowe and Riccardo Poli and Kenneth A. {De
                 Jong}",
  address =      "Torremolinos, Spain",
  publisher_address = "San Francisco, CA, USA",
  month =        "4-6 " # sep,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_gecco2002.pdf",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_gecco2002.ps.gz",
  abstract =     "We model the distribution of functions implemented by
                 non-recursive programs, similar to linear genetic
                 programming (GP). Most functions are constants, the
                 remainder are mostly parsimonious. The effect of ad-hoc
                 rules on GP are described and new heuristics are
                 proposed.

                 Bounds on how long programs need to be before the
                 distribution of their functionality is close to its
                 limiting distribution are provided in general and for
                 average computers.

                 Results for average computers and a model like genetic
                 programming are experimentally tested.",
}

@InProceedings{langdon:2002:crlp2p,
  author =       "W. B. Langdon",
  title =        "Size of Random Programs to ensure Uniformity",
  booktitle =    "Proceedings of the Fourteenth Belgium/Netherlands
                 Conference on Artificial Intelligence (BNAIC'02)",
  year =         "2002",
  editor =       "Hendrik Blockeel and Marc Denecker",
  pages =        "459--460",
  address =      "Leuven, Belgium",
  month =        "21-22 " # oct,
  organisation = "BNVKI, Dutch and the Belgian AI Association",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_bnaic2002.pdf",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_bnaic2002.ps.gz",
  size =         "2 pages",
  abstract =     "Fitness distributions (landscapes) of programs tend to
                 a limit as they get bigger. Markov chain convergence
                 theorems give general upper bounds on the linear
                 program sizes needed for convergence. Tight bounds
                 (exponential in N, N log(N) and smaller) are given in
                 langdon:2002:crlp for the outputs of five computer
                 models (any, average, cyclic, bit flip and Boolean).
                 Mutation randomises a genetic algorithm population in
                 0.25(l+1)(log(l)+4) generations. While
                 langdon:2002:foga considers convergence of functions.
                 We restate the results 0.5N(log(m)+4) and
                 O(N)-O(N^{3/2}) for a genetic programming (GP) like
                 model.",
  notes =        "2 page summary of langdon:2002:crlp

                 Katholieke Universiteit Leuven and Université Libre de
                 Bruxelles in collaboration with PharmaDM and under the
                 auspices of BNVKI/AIABN (the Belgian-Dutch Association
                 for Artificial Intelligence), SIKS (School for
                 Information and Knowledge Systems), and SNN (the
                 Foundation for Neural Networks).",
}

@Misc{langdon:2002:kdmdd,
  author =       "W. B. Langdon and B. F. Buxton and S. J. Barrett",
  title =        "Combining Machine Learning techniques to Predict
                 Compounds' Cytochrome {P450} High Throughput Screening
                 Inhibition",
  howpublished = "Knowledge Discovery meets Drug Discovery",
  year =         "2002",
  month =        "23 " # oct,
  note =         "poster",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/wbl_kdmdd2002.pdf",
  notes =        "http://www.kdnet.org/workshop_overview1_bioinfoLeuven02.htm",
}

@Misc{langdon:2002:iberamia,
  author =       "W. B. Langdon",
  title =        "Application of genetic programming in drug lead
                 discovery",
  howpublished = "8th Iberoamerican Conference on Artificial
                 Intelligence",
  year =         "2002",
  month =        "12 " # nov,
  note =         "Invited conference speaker",
  address =      "Seville, Spain",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.lsi.us.es/iberamia2002/docs/plenarysession.pdf",
  notes =        "http://www.lsi.us.es/iberamia2002/principal_eng.html",
}

@Misc{langdon:2002:his,
  author =       "W. B. Langdon",
  title =        "A hybrid genetic programming neural network classifier
                 for use in drug discovery",
  howpublished = "Second International Conference on Hybrid Intelligent
                 Systems",
  year =         "2002",
  month =        "1-4 " # dec,
  note =         "Invited conference speaker",
  address =      "Universidad de Chile, Chile",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://tamarugo.cec.uchile.cl/~his02/index_files/abs_drug.pdf",
  notes =        "http://tamarugo.cec.uchile.cl/~his02/index.html",
}

@InProceedings{langham:1999:AMPAFEMCAC,
  author =       "A. E. Langham and P. W. Grant",
  title =        "A Multilevel k-way Partitioning Algorithm for Finite
                 Element Meshes using Competing Ant Colonies",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1602--1608",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, ant systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{lanza:2000:ICSB,
  author =       "Guido Lanza and William Mydlowec and John R. Koza",
  title =        "Automatic creation of a genetic network for the lac
                 operon from observed data by means of genetic
                 programming",
  booktitle =    "First International Conference on Systems Biology
                 (ICSB)",
  year =         "2000",
  address =      "Tokyo",
  month =        "14-16 " # nov,
  organisation = "Japan Society for Bioinformatics",
  keywords =     "genetic algorithms, genetic programming, Biology,
                 genetic networks, reverse engineering, lac operon",
  URL =          "http://www.genetic-programming.com/icsb2000gn.ps",
  abstract =     "This paper demonstrates that it is possible to use
                 genetic programming to automatically create (reverse
                 engineer) a computer program representing the logic
                 underlying the genetic network for the expression level
                 of the lac operon (composed of the Z, Y, and A genes)
                 as measured by its mRNA. Genetic programming starts
                 with observed time-domain expression levels of two
                 genes (REPRESSOR or CAP) and the concentrations of two
                 substances (GLUCOSE or LACTOSE) and automatically
                 creates both a topological arrangement of conditional
                 and comparative functions as well as all necessary
                 numerical parameters of a genetic network whose
                 behavior matches observed time-domain data.",
  notes =        "ICSB-2000 8 Feb 2001 ghostview and our printers barf
                 at icsb2000gn.ps population size 10,000",
}

@InProceedings{lanzi:1998:ammXCSM,
  author =       "Pier Luca Lanzi",
  title =        "An Analysis of the Memory Mechanism of {XCSM}",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "643--651",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{lanzi:1999:AEXCSSE,
  author =       "Pier Luca Lanzi and Marco Colombetti",
  title =        "An Extension to the {XCS} Classifier System for
                 Stochastic Environments",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "353--360",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{lanzi:1999:ERCCPIFBMC,
  author =       "Pier Luca Lanzi",
  title =        "Extending the Representation of Classifier Conditions
                 Part {I}: From Binary to Messy Coding",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "337--344",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{lanzi:1999:ERCCPIFMCS,
  author =       "Pier Luca Lanzi and Alessandro Perrucci",
  title =        "Extending the Representation of Classifier Conditions
                 Part {II}: From Messy Coding to {S}-Expressions",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "345--352",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems",
  ISBN =         "1-55860-611-4",
  abstract =     "XCS, Lisp S-expressions, XCSL, 6-multiplexor, woods",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Lathrop:1997:cdgp,
  author =       "James I. Lathrop",
  title =        "Compression Depth and Genetic Programs",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "370--379",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  size =         "9 pages",
  notes =        "Complexity of cellular automata, organisational
                 complexity of populations of finite state machines that
                 play prisoner's dilemma. computational depth

                 p371 {"}Computational depth... its non-computability
                 renders it useless for actual complexity
                 measurements{"}. Compression depth cf gzip

                 p372 GA {"}produces complexity as the population
                 ages{"}. Bennett {"}slow-growth law => {"}complexity is
                 produced slowly and thus cannot be created without
                 commensurate history of computation{"}.

                 p375 IPD FSA GA without fitness selection {"}no
                 significant increase in compression depth{"}. With
                 normal GA fitness selection {"}average compression
                 depth{"} (ie .gz size) {"}of the ten most fit players
                 generally increases as more generations (computation
                 time) is provided.{"} --p376 {"}even if fitness does
                 not{"} (Is this just FSM bloat? WBL)

                 GP-97",
}

@Article{Lavington:1999:IST,
  author =       "S. Lavington and N. Dewhurst and E. Wilkins and A.
                 Freitas",
  title =        "Interfacing knowledge discovery algorithms to large
                 database management systems",
  journal =      "Information and Software Technology",
  volume =       "41",
  pages =        "605--617",
  year =         "1999",
  number =       "9",
  month =        "25 " # jun,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V0B-3WN7DYN-8/1/cdabdda09c085c6a4536aa5e116366ee",
  size =         "13 pages",
  abstract =     "The efficient mining of large, commercially credible,
                 databases requires a solution to at least two problems:
                 (a) better integration between existing Knowledge
                 Discovery algorithms and popular DBMS; (b) ability to
                 exploit opportunities for computational speedup such as
                 data parallelism. Both problems need to be addressed in
                 a generic manner, since the stated requirements of
                 end-users cover a range of data mining paradigms, DBMS,
                 and (parallel) platforms. In this paper we present a
                 family of generic, set-based, primitive operations for
                 Knowledge Discovery in Databases (KDD). We show how a
                 number of well-known KDD classification metrics, drawn
                 from paradigms such as Bayesian classifiers,
                 Rule-Induction/Decision Tree algorithms, Instance-Based
                 Learning methods, and Genetic Programming, can all be
                 computed via our generic primitives. We then show how
                 these primitives may be mapped into SQL and, where
                 appropriate, optimised for good performance in respect
                 of practical factors such as client-server
                 communication overheads. We demonstrate how our
                 primitives can support C4.5, a widely-used rule
                 induction system. Performance evaluation figures are
                 presented for commercially available parallel
                 platforms, such as the IBM SP/2.",
}

@InProceedings{law:1999:G,
  author =       "Kin Lun Law",
  title =        "Generating hard satisfiability problems using genetic
                 programming",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "171--174",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, Genetic Programming",
  notes =        "GECCO-99LB",
}

@InCollection{law:1999:TRDGP,
  author =       "Ken Law",
  title =        "Traffic Rules Discovery using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "105--114",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{Lay:1994:GPssdy,
  author =       "Ming-Yi Lay",
  title =        "Application of genetic programming in analyzing
                 multiple steady states of dynamical systems",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "333--336b",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  notes =        "Uses GP to search for steady states in a reaction
                 vessel. The equations for the behaviour of the chmicals
                 is known but not how to solve them. GP is able to find
                 to high accuracy (7 figure) the steady states. States
                 are divined by two floating point variables. Each
                 represented within the prog by an effectivley
                 indepenant tree, ie they dont exchance via crossover.",
}

@InCollection{GeumYongLee:1999:aigp3,
  author =       "Geum Yong Lee",
  title =        "Genetic Recursive Regression for Modeling and
                 Forecasting Real-World Chaotic Time Series",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "17",
  pages =        "401--423",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{KHLee:2002:FEA,
  author =       "K. H. Lee and Y. S. Yeun and W. S. Ruy and Y. S.
                 Yang",
  title =        "Polynomial Genetic Programming for Response Surface
                 Modeling",
  booktitle =    "4th International Workshop on Frontiers in
                 Evolutionary Algorithms",
  year =         "2002",
  editor =       "Manuel Grana Romay and Richard Duro",
  address =      "North Carolina, USA",
  month =        "8-14 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-9707890-1-7",
  notes =        "FEA2002 In conjunction with Sixth Joint Conference on
                 Information Sciences",
}

@InCollection{lee:1995:efnGPe,
  author =       "Jack Y. B. Lee and P. C. Wong",
  title =        "The effect of function noise on {GP} efficiency",
  booktitle =    "Progress in Evolutionary Computation",
  publisher =    "Springer-Verlag",
  year =         "1995",
  editor =       "X. Yao",
  volume =       "956",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "1--16",
  address =      "Heidelberg, Germany",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Artificial ant on Santa Fe Trail with noisy
                 IfFoodAhead GP does poorly even with small ammounts of
                 with noise. Sometimes population abandon's use of
                 IfFoodAhead entirely (what else could it do?)

                 ",
}

@Unpublished{lee:1996:aigp2,
  author =       "G. Y. Lee",
  title =        "Explicit Models for Chaotic and Noisy Time Series
                 Through the Genetic Recursive Regression",
  year =         "1995",
  keywords =     "genetic algorithms, genetic programming",
  note =         "unpublished",
  notes =        "Draft submitted to Advances in Genetic Programming 2
                 Peter J. Angeline and K. E. {Kinnear, Jr.} (Eds.) MIT
                 Press, 1996.",
}

@InProceedings{lee:1996:hGPGAccrb,
  author =       "Wei-Po Lee and John Hallam and Henrik Hautop Lund",
  title =        "A Hybrid {GP}/{GA} Approach for Co-evolving
                 Controllers and Robot Bodies to Achieve
                 Fitness-Specified Tasks",
  booktitle =    "Proceedings of the 1996 {IEEE} International
                 Conference on Evolutionary Computation",
  year =         "1996",
  address =      "Nagoya, Japan",
  month =        "20-22 " # may,
  organisation = "IEEE Neural Network Council",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-2902-3",
  URL =          "ftp://ftp.daimi.aau.dk/pub/stud/hhl/bodyplan.ps.Z",
  size =         "6 pages",
  notes =        "ICEC-96 Evolves controller for (simulated?) mobile
                 robot

                 ",
}

@InProceedings{lee:1997:aGPebpamr,
  author =       "Wei-Po Lee and John Hallam and Henrik Hautop Lund",
  title =        "Applying Genetic Programming to Evolve Behavior
                 Primitives and Arbitrators for Mobile Robots",
  booktitle =    "Proceedings of IEEE 4th International Conference on
                 Evolutionary Computation",
  year =         "1997",
  volume =       "1",
  publisher =    "IEEE Press",
  note =         "to appear",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.daimi.aau.dk/pub/stud/hhl/ebbicec97.ps.Z",
  size =         "6 pages",
  notes =        "Khepera",
}

@InProceedings{lee:1997:lcrbea,
  author =       "Wei-Po Lee and John Hallam and Henrik Hautop Lund",
  title =        "Learning Complex Robot Behaviours by Evolutionary
                 Approaches",
  booktitle =    "6th European Workshop on Learning Robots, EWLR-6",
  year =         "1997",
  pages =        "42--51",
  address =      "Hotel Metropole, Brighton, UK",
  month =        "1-2 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  size =         "10 pages",
  notes =        "Task of getting Khepera to push a box to a light
                 source broken up by hand into 4 subtasks. Fitness
                 function etc devised for each task and GP used to
                 evolve code to solve it in simulation. Evolved codes
                 put together and run on real robot. Published as
                 lee:1997:lcrbeaLNAI",
}

@InProceedings{lee:1997:lcrbeaLNAI,
  author =       "Wei-Po Lee and John Hallam and Henrik Hautop Lund",
  title =        "Learning Complex Robot Behaviours by Evolutionary
                 Computing with Task Decomposition",
  booktitle =    "Learning Robots, 6th European Workshop, EWLR-6,
                 Proceedings",
  year =         "1997",
  editor =       "A. Birk and J. Demiris",
  series =       "LNAI",
  volume =       "1545",
  pages =        "155",
  address =      "Hotel Metropole, Brighton, UK",
  month =        "1-2 " # aug,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65480-1",
  URL =          "http://link.springer.de/link/service/series/0558/bibs/papers/1545/15450155.pdf",
  URL =          "http://link.springer.de/link/service/series/0558/bibs/1545/15450155.htm",
  size =         "10 pages",
  abstract =     "Building robots can be a tough job because the
                 designer has to predict the interactions between the
                 robot and the environment as well as to deal with them.
                 One solution to cope the difficulties in designing
                 robots is to adopt learning methods. Evolution-based
                 approaches are a special kind of machine learning
                 method and during the last few years some researchers
                 have shown the advantages of using this kind of
                 approach to automate the design of robots. However, the
                 tasks achieved so far are fairly simple. In this work,
                 we analyse the difficulties of applying evolutionary
                 approaches to learn complex behaviours for mobile
                 robots. And, instead of evolving the controller as a
                 whole, we propose to take the control architecture of a
                 behavior-based system and to learn the separate
                 behaviours and the arbitration by the use of an
                 evolutionary approach. By using the technique of task
                 decomposition, the job of defining fitness functions
                 becomes more straightforward and the tasks become
                 easier to achieve. To assess the performance of the
                 developed approach, we have evolved a control system to
                 achieve an application task of box-pushing as an
                 example. Experimental results show the promise and
                 efficiency of the presented approach.",
  notes =        "Published version may be different from that in
                 proceedings lee:1997:lcrbea",
}

@PhdThesis{Wei-PoLee:thesis,
  author =       "Wei-Po Lee",
  title =        "Evolving Robots: from Simple Behaviours to Complete
                 Systems",
  school =       "Department of Artificial Intelligence. University of
                 Edinburgh",
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@Article{Wei-PoLee:1999:ISJ,
  author =       "Wei-Po Lee",
  title =        "Evolving complex robot behaviors",
  journal =      "Information Sciences",
  year =         "1999",
  volume =       "121",
  number =       "1-2",
  pages =        "1--25",
  email =        "wplee@mail.npust.edu.tw",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computing, Computational intelligence, Robot learning,
                 Automatic robot programming",
  ISSN =         "0020-0255",
  URL =          "http://www.elsevier.com/gej-ng/10/23/143/54/25/25/abstract.html",
  abstract =     "Building robots is a tough job because the designer
                 has to predict the interactions between the robot and
                 the environment as well as to deal with them. One
                 solution to such difficulties in designing robots is to
                 adopt learning methods. The evolution-based approach is
                 a special method of machine learning and it has been
                 advocated to automate the design of robots. Yet, the
                 tasks achieved so far are fairly simple. In this work,
                 we first analyze the difficulties of applying
                 evolutionary approaches to synthesize robot controllers
                 for complicated tasks, and then suggest an approach to
                 resolve them. Instead of directly evolving a monolithic
                 control system, we propose to decompose the overall
                 task to fit in the behavior-based control architecture,
                 and then to evolve the separate behavior modules and
                 arbitrators using an evolutionary approach.
                 Consequently, the job of defining fitness functions
                 becomes more straightforward and the tasks easier to
                 achieve. To assess the performance of the developed
                 approach, we evolve a control system to achieve an
                 application task of box-pushing as an example.
                 Experimental results show the promise and efficiency of
                 the presented approach.",
  notes =        "Khepera Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}

@InProceedings{lee:1999:EPLPD,
  author =       "Chang-Yong Lee and Yoonseon Song",
  title =        "Evolutionary Programming using the Levy Probability
                 Distribution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "886--893",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{lee:1999:IHPCIRUIGA,
  author =       "Joo-Young Lee and Sung-Bae Cho",
  title =        "Incorporating Human Preference into Content-based
                 Image Retrieval Using Interactive Genetic Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1788",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{lee:2000:FSKTPGA,
  author =       "Miler Lee",
  title =        "Finding Solutions to the Knight's Tour Problem using
                 Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "252--260",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{lee:2001:tspgp,
  author =       "G. Y. Lee",
  title =        "Time Series Perturbation by Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "403--409",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Time Series,
                 Perturbation Theory",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@InCollection{lee:2002:EPGIMMA,
  author =       "Peter Lee",
  title =        "Evolving Presentations of Genetic Information:
                 Motivation, Methods, and Analysis",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "119--128",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp Positional information on genes
                 => do shorter schema survive? Matlab 6.1.0.450 SUN
                 Blade 2000",
}

@PhdThesis{leger:1999:thesis,
  author =       "Chris Leger",
  title =        "Automated Synthesis and Optimisation of Robot
                 Configurations: An Evolutionary Approach",
  school =       "The Robotics Institute, Carnegie Mellon University",
  year =         "1999",
  address =      "Pittsbugh, PA 15213, USA",
  month =        "9 " # dec,
  note =         "CMU-RI-TR-99-43",
  keywords =     "genetic algorithms, genetic programming, Darwin2K",
  URL =          "http://www.frc.ri.cmu.edu/~blah/papers/thesis.ps.gz",
  URL =          "http://www.frc.ri.cmu.edu/~blah/papers/thesis.pdf",
  size =         "234 pages",
  abstract =     "Robot configuration design is hampered by the lack of
                 established, well-known design rules, and designers
                 cannot easily grasp the space of possible designs and
                 the impact of all design variables on a robot's
                 performance. Realistically, a human can only design and
                 evaluate several candidate configurations, though there
                 may be thousands of competitive designs that should be
                 investigated. In contrast, an automated approach to
                 configuration synthesis can create tens of thousands of
                 designs and measure the performance of each one without
                 relying on previous experience or design rules. This
                 thesis creates Darwin2K, an extensible, automated
                 system for robot configuration synthesis. This research
                 focuses on the development of synthesis capabilities
                 required for many robot design problems: a flexible and
                 effective synthesis algorithm, useful simulation
                 capabilities, appropriate representation of robots and
                 their properties, and the ability to accomodate
                 application-specific synthesis needs. Darwin2K can
                 synthesize and optimize kinematics, dynamics,
                 structural geometry, actuator selection, and task and
                 control parameters for a wide range of robots. Darwin2K
                 uses an evolutionary algorithm to synthesize robots,
                 and uses two new multi-objective selection procedures
                 that are applicable to other evolutionary design
                 domains. The evolutionary algorithm can effectively
                 optimize multiple performance objectives while
                 satisfying multiple performance constraints, and can
                 generate a range of solutions representing different
                 trade-offs between objectives. Darwin2K uses a novel
                 representation for robot configurations called the
                 parameterized module configuration graph, enabling
                 efficient and extensible synthesis of mobile robots, of
                 single, multiple and bifurcating manipulators, and of
                 robots with either modular or monolithic construction.
                 Task-specific simulation is used to provide the
                 synthesis algorithm with performance measurements for
                 each robot. Darwin2K can automatically derive dynamic
                 equations for each robot it simulates, enabling dynamic
                 simulation to be used during synthesis for the first
                 time. Darwin2K also includes a variety of simulation
                 components, including Jacobian and PID controllers,
                 algorithms for estimating link deflection and for
                 detecting collisions; modules for robot links, joints
                 (including actuator models), tools, and bases (fixed
                 and mobile); and metrics such as task coverage, task
                 completion time, end effector error, actuator
                 saturation, and link deflection. A significant
                 component of the system is its extensible
                 object-oriented software architecture, which allows new
                 simulation capabilities and robot modules to be added
                 without impacting the synthesis algorithm. The
                 architecture also encourages re-use of existing toolkit
                 components, allowing task-specific simulators to be
                 quickly constructed. Darwin2K's synthesis algorithm,
                 simulation capabilities, and extensible architecture
                 combine to allow synthesis of robots for a wide range
                 of tasks. Results are presented for nearly 150
                 synthesis experiments for six different applications,
                 including synthesis of a free-flying 22-DOF robot with
                 multiple manipulators and a walking machine for
                 zero-gravity truss walking. The synthesis system and
                 results represent a significant advance in the
                 state-of-the-art in automated synthesis for robotics.",
}

@InProceedings{lei:1999:T,
  author =       "Wang Lei and Jiao Licheng",
  title =        "The immune evolutionary programming and its
                 convergence",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "175--183",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "immune evolutionary programming, antibody, TSP",
  notes =        "GECCO-99LB",
}

@InProceedings{lenaerts:1998:GPfavdp,
  author =       "Tom Lenaerts and Bernard Manderick",
  title =        "Building a Genetic Programming Framework: The
                 Added-Value of Design Patterns",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "196--208",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "A large body of public domain software exists which
                 addresses standard implementations of the Genetic
                 Programming paradigm. Nevertheless researchers are
                 frequently confronted with the lack of flexibility and
                 reusability of the tools when for instance one wants to
                 alter the genotype representation or the overall
                 behavior of the evolutionary process. This paper
                 addresses the construction of a object-oriented Genetic
                 Programming framework using on design patterns to
                 increase its flexibility and reusability.",
  notes =        "EuroGP'98",
}

@InProceedings{Lensberg:1997:GPeibuku,
  author =       "Terje Lensberg",
  title =        "A Genetic Programming Experiment on Investment
                 Behavior under Knightian Uncertainty",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "231--239",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InCollection{lent:1994:trade,
  author =       "Brian Lent",
  title =        "Evolution of Trade Strategies using Genetic Algorithms
                 and Genetic Programming",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "87--98",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InCollection{leong:1998:GSRC,
  author =       "Kian Fai Leong",
  title =        "Genetically Solving a Rubik's Cube",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "58--67",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{lerena:1999:CMC,
  author =       "Patricio Lerena and Michele Courant",
  title =        "Complexity in Mate Choice",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1446",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{lesko:1999:BBWHEMWWUGP,
  author =       "Jim Lesko",
  title =        "Building a Better Wumpuus Hunter: Evaluating Memory in
                 the World of the Wumpus Using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "115--121",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{leung:2002:epmpfamp,
  author =       "Kwong Sak Leung and Kin Hong Lee and Sin Man Cheang",
  title =        "Evolving Parallel Machine Programs for a Multi-{ALU}
                 Processor",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "1703--1708",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstrct =      "a novel Genetic Parallel Programming (GPP) paradigm
                 for evolving optimal parallel programs running on a
                 multi-ALU processor by Linear Genetic Programming. GPP
                 uses a two-phase evolution approach. It evolves
                 completely correct solution programs in the first
                 phase. Then it optimizes execution speeds of solution
                 programs in the second phase. Besides, GPP also employs
                 a new genetic operation that swaps sub-instructions of
                 a solution program. Three experiments (Sextic,
                 Fibonacci and Factorial) are given as examples to show
                 that GPP could discover novel parallel programs that
                 fully use the processor's parallelism.",
}

@InProceedings{levenick:1999:SI,
  author =       "James R. Levenick",
  title =        "Swappers: Introns promote flexibility, diversity and
                 invention",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "361--368",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{levitt:1995:TGAGS,
  author =       "Jeremy R. Levitt",
  title =        "The Genetic Algorithm applied to Gate Sizing",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "191--198",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{lewis:1992:gpnnwr,
  author =       "M. Anthony Lewis and Andrew H. Fagg and Alan Solidum",
  title =        "Genetic Programming Approach to the Construction of a
                 Neural Network Control of a Walking Robot",
  booktitle =    "Proceedings of the 1992 IEEE InternationalConference
                 on Robotics and Automation",
  year =         "1992",
  pages =        "2618--2623",
  address =      "Nice, France",
  month =        may,
  organisation = "IEEE",
  publisher =    "Electronica Bks",
  keywords =     "genetic algorithms",
  notes =        "NOT a Koza style GP but a conventional 65 bit binary
                 string using Genesis. Evolves ANN controller for real 6
                 legged robot, 2 stages first uses human to score 2
                 neuron controller as ocsillator, when 50% of pop can do
                 this nest stage evolves 6 such oscilators together to
                 control robot. All runs produced tripod gait
                 eventually, intermediate states wave gait and marked
                 tendancy to walk backwards.",
}

@Article{li:1992:irkc,
  author =       "Ming Li and Paul M. B. Vitanyi",
  title =        "Inductive Reasoning and Kolmogorov Complexity",
  journal =      "Journal of Computer and System Sciences",
  publisher =    "Academic Press",
  year =         "1992",
  volume =       "44",
  number =       "2",
  pages =        "343--384",
  month =        apr,
  notes =        "April's issue was devoted to proceedings of the fourth
                 annual conference on Structure in Complexity Theory,
                 IEEE Computer Society, held in University or Oregon,
                 19-22 June 1989.",
}

@InProceedings{li:1999:MGARPP,
  author =       "Y. Li and K. F. Man and K. S. Tang",
  title =        "Multiobjective Genetic Algorithm for Rolling-Horizon
                 Production Planning",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1789",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{li:1999:IDMUFACS,
  author =       "Jin Li and Edward P. K. Tsang",
  title =        "Investment Decision Making Using {FGP}: {A} Case
                 Study",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1253--1259",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, forecasting",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@Misc{li:1999:FAGPTFP,
  author =       "Jin Li",
  title =        "{FGP}: {A} Genetic Programming Tool for Financial
                 Prediction",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, stock market,
                 prediction",
  URL =          "http://privatewww.essex.ac.uk/~jli/GPTool.htm",
}

@InProceedings{JinLi:2000:CEF,
  author =       "Jin Li and Edward P. K. Tsang",
  title =        "Reducing Failures in Investment Recommendations using
                 Genetic Programming",
  booktitle =    "Computing in Economics and Finance",
  year =         "2000",
  address =      "Universitat Pompeu Fabra, Barcelona, Spain",
  month =        "6-8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://enginy.upf.es/SCE/papers/paper332.ps.gz",
  abstract =     "FGP (Financial Genetic Programming) is a genetic
                 programming based system that specialises in financial
                 forecasting. In the past, we have reported that FGP-1
                 (the first version of FGP) is capable of producing
                 accurate predictions in a variety of data sets. It can
                 accurately predict whether a required rate of return
                 can be achieved within a user-specified period. This
                 paper reports further development of FGP, which is
                 motivated by realistic needs as described below: a
                 recommendation {"}not to invest{"} is often less
                 interesting than a recommendation {"}to invest{"}. The
                 former leads to no action. If it is wrong, the user
                 loses an investment opportunity, which may not be
                 serious if other investment opportunities are
                 available. On the other hand, a recommendation to
                 invest leads to commitment of funds. If it is wrong,
                 the user fails to achieve the target rate of return.
                 Our objective is to reduce the rate of failure when FGP
                 recommends to invest. In this paper, we present a
                 method of tuning the rate of failure by FGP to reflect
                 the user's preference. This is achieved by introducing
                 a novel constraint-directed fitness function to FGP.
                 The new system, FGP-2, was extensively tested on
                 historical Dow Jones Industrial Average (DJIA) Index.
                 Trained with data from a seven-and-a-half-years period,
                 decision trees generated by FGP-2 were tested on data
                 from a three-and-a-half-years out-of-sample period.
                 Results confirmed that one can tune the rate of failure
                 by adjusting a constraint parameter in FGP-2. Lower
                 failure rate can be achieved at the cost of missing
                 opportunities, but without affecting the overall
                 accuracy of the system. The decision trees generated
                 were further analysed over three sub-periods with down
                 trend, side-way trend and up trend, respectively.
                 Consistent results were achieved. This shows the
                 robustness of FGP-2. We believe there is scope to
                 generalise the constrained fitness function method to
                 other applications.",
  notes =        "http://enginy.upf.es/SCE/index2.html",
}

@InProceedings{IkSooLim:1998:imgphvGP,
  author =       "Ik Soo Lim and Daniel Thalmann",
  title =        "Indexed Memory as a Generic Protocol for Handling
                 Vectors of Data in Genetic Programming",
  booktitle =    "Fifth International Conference on Parallel Problem
                 Solving from Nature",
  year =         "1998",
  editor =       "Agoston E. Eiben and Thomas Back and Marc Schoenauer
                 and Hans-Paul Schwefel",
  volume =       "1498",
  series =       "LNCS",
  pages =        "325--334",
  address =      "Amsterdam",
  publisher_address = "Berlin",
  month =        "27-30 " # sep,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65078-4",
  abstract =     "Indexed memory is used as a generic protocol for
                 handling vectors of data in genetic programming. Using
                 this simple method, a single program can generate many
                 outputs. It eliminates the complexity of maintaining
                 different trees for each desired parameter and avoid
                 problem-specific function calls for handling the
                 vectors. This allows a single set of programming
                 language primitives applicable to wider range of
                 problems. For a test case, the technique is appliedto
                 evolution of behavioural control programs for a
                 simulated 2d vehicle in a corridor following problem.",
  notes =        "PPSN-V",
}

@InProceedings{lim:1999:HNBBEGHB,
  author =       "Ik Soo Lim and Daniel Thalmann",
  title =        "How Not to Be a Black-Box: Evolution and
                 Genetic-Engineering of High-Level Behaviours",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1329--1335",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming,artificial
                 life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  abstract =     "computer animations, foraging task, non-transparency",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{lin:1999:IPOSMGA,
  author =       "Wen-Yang Lin",
  title =        "Improving Parallel Ordering of Sparse Matrices using
                 Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1790",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{lindblad:2002:emioiugh,
  author =       "Fredrik Lindblad and Peter Nordin and Krister Wolff",
  title =        "Evolving {3D} model interpretation of images using
                 graphics hardware",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "225--230",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We present a novel approach for 3d-scene
                 interpretation with numerous applications, for instance
                 in robotics. The models are rendered using 3d graphics
                 hardware and DirectX. Both artificial and real images
                 were used to test the system. More than one target
                 image can be used, allowing stereoscopic vision. These
                 experiments present results of interesting
                 generalization.",
}

@InProceedings{lindhorst:1998:rGAsmtpm,
  author =       "Gwenda Lindhorst",
  title =        "Relational Genetic Algorithms: With application to
                 Surface Mount Technology Placement Machines",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "543--550",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@Article{Lipson:2000:admrl,
  author =       "Hod Lipson and Jordan B. Pollack",
  title =        "Automatic design and manufacture of robotic
                 lifeforms",
  journal =      "Nature",
  year =         "2000",
  number =       "406",
  pages =        "974--978",
  month =        "31 " # aug,
  keywords =     "genetic programming, evolutionary programming,
                 evolutionstrategies",
  URL =          "http://www.nature.com/cgi-taf/DynaPage.taf?file=/nature/journal/v406/n6799/full/406974a0_fs.html&content_filetype=pdf",
  size =         "5 pages",
  abstract =     "Biological life is in control of its own means of
                 reproduction, which generally involves complex,
                 autocatalysing chemical reactions. But this autonomy of
                 design and manufacture has not yet been realized
                 artificially. Robots are still laboriously designed and
                 constructed by teams of human engineers, usually at
                 considerable expense. Few robots are available because
                 these costs must be absorbed through mass production,
                 which is justified only for toys, weapons and
                 industrial systems such as automatic teller machines.
                 Here we report the results of a combined computational
                 and experimental approach in which simple
                 electromechanical systems are evolved through
                 simulations from basic building blocks (bars, actuators
                 and artificial neurons); the 'fittest' machines
                 (defined by their locomotive ability) are then
                 fabricated robotically using rapid manufacturing
                 technology. We thus achieve autonomy of design and
                 construction using evolution in a 'limited universe'
                 physical simulation coupled to automatic fabrication.",
  notes =        "Note I have filed as GP even though the authors state
                 they are not using GP (their genetic search uses only
                 mutation) however (as far as I can tell) the
                 representation of the genome is variable length.

                 Nice mpeg videos online at www.nature.com",
}

@InProceedings{Littman:1994:mp,
  author =       "Michael L. Littman",
  title =        "Memoryless Policies: Theoretical limitations and
                 practical results",
  booktitle =    "Simulation of Adaptive Behaviour (SAB-94)",
  year =         "1994",
  pages =        "238--245",
  organisation = "Brown University / Bellcore",
  keywords =     "genetic algorithms",
  size =         "8 pages",
  notes =        "Discusses designing agents to solve completely known
                 problems. Agents 1) are entriely reactive or 2) finite
                 state machines (1 bit memory). Determinstic.

                 Proof given: satisfactory determinsistic memory less
                 agent is NP complete problem. So too is design of
                 optimal agent.

                 Presents method which _may_ be able to find optimal
                 solution in polynomial time. Shown producing optimal or
                 near optimal agents in almost all runs on three
                 differnt problems.",
}

@InProceedings{QinghuaLiu:1998:sbDNAc:1sp,
  author =       "Qinghua Liu and Anthony G. Frutos and Liman Wang and
                 Andrew J. Thiel and Susan D. Gillmor and Todd Strother
                 and Anne E. Condon and Robert M. Corn and Max G.
                 Lagally and Lloyd M. Smith",
  title =        "Progress Toward Demonstration of a Surface Based {DNA}
                 Computation: a One Word Approach to Solve a Model
                 Satisfiability Problem",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "709--717",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{liu:1997:ehoadtcATM,
  author =       "Weixin Liu and Masahiro Murakawa and Tetsuya Higuchi",
  title =        "Evolvable Hardware for On-line Adaptive Traffic
                 Control in {ATM} Networks",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Evolvable Hardware",
  pages =        "504--509",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InCollection{liu:1998:SRCUGP,
  author =       "Richard Liu",
  title =        "Solving the Rubik's Cube Using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "68--73",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InCollection{liu:2000:DGSDGUGA,
  author =       "David Liu",
  title =        "Development of Game-Playing Strategies in a
                 Darwinistic Game Using Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "261--268",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InCollection{lo:1998:GMSASSARL,
  author =       "Paul C. K. Lo",
  title =        "Genetically-Evolved Mastermind Strategy: {A}
                 Self-Simplifying Symbolic Approach to Reinforcement
                 Learning",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "74--83",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InCollection{lo:1999:AGASBPP,
  author =       "Lawrence K. Lo",
  title =        "A Genetic Algorithm to Solve the 2-{D} Bin Packing
                 Problem",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "122--130",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{lobo:1998:cillGA,
  author =       "Fernando G. Lobo and Kalyanmoy Deb and David E.
                 Goldberg and Georges R. Harik and Liwei Wang",
  title =        "Compressed Introns in a Linkage Learning Genetic
                 Algorithm",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "551--558",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{lobo:1998:spbdGA,
  author =       "Fernando Lobo",
  title =        "Solving Problems of Bounded Difficulty Using Genetic
                 Algorithms",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms",
  notes =        "GP-98LB",
}

@Proceedings{lohn:2000:eh,
  title =        "he Second {NASA}/Do{D} Workshop on Evolvable
                 Hardware",
  year =         "2000",
  editor =       "Didier Keymeulen {Jason Lohn, Adrian Stoica}",
  address =      "Palo Alto, California",
  publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC,
                 20036-1992, USA",
  month =        "13-15 " # jul,
  organisation = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, evolvable hardware",
  URL =          "http://ic-www.arc.nasa.gov/ic/eh2000/",
}

@Article{lohr:2002:PCM,
  author =       "Steve Lohr",
  title =        "The Programming Gene",
  journal =      "PC Magazine",
  year =         "2002",
  month =        "3 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.pcmag.com/article2/0,4149,429437,00.asp",
  abstract =     "As part of a series on future technologies, surveys
                 genetic programming and related research.",
}

@InProceedings{loizides:2001:wsc6,
  author =       "A. Loizides and M. Slater and W. B. Langdon",
  title =        "Measuring Facial Emotional Expressions Using Genetic
                 Programming",
  booktitle =    "Soft Computing and Industry Recent Applications",
  year =         "2001",
  editor =       "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and
                 Takeshi Furuhashi and Frank Hoffmann",
  pages =        "545--554",
  month =        "10--24 " # sep,
  publisher =    "Springer-Verlag",
  note =         "Published 2002",
  keywords =     "genetic algorithms, genetic programming, data
                 visualisation, symbolic regression",
  ISBN =         "1-85233-539-4",
  size =         "5 pages",
  notes =        "WSC6
                 http://www.springer.de/cgi/svcat/search_book.pl?isbn=1-85233-539-4",
}

@InProceedings{LonTyr01,
  author =       "Michael A. Lones and Andy M. Tyrrell",
  title =        "Enzyme Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation, CEC 2001",
  year =         "2001",
  pages =        "1183--1190",
  month =        "27--30 " # may,
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, biomimetic
                 representations, Metabolic Pathways, Evolutionary
                 Electronics",
  ISBN =         "0-7803-6657-3",
  URL =          "http://www.bioinspired.com/publications/lones_cec2001.pdf",
  abstract =     "The work reported in this paper follows from the
                 hypothesis that better performance in artificial
                 evolution can be achieved by adhering more closely to
                 the features that make natural evolution effective
                 within biological systems. An important issue in
                 evolutionary computation is the choice of solution
                 representation. Genetic programming, whilst borrowing
                 from biology in the evolutionary axis of behaviour,
                 remains firmly rooted in the artificial domain with its
                 use of a parse tree representation. Following concerns
                 that this approach does not encourage solution
                 evolvability, this paper presents an alternative method
                 modelled upon representations used by biology. Early
                 results are encouraging; demonstrating that the method
                 is competitive when applied to problems in the area of
                 combinatorial circuit design. Whilst too early to gauge
                 its suitability to a more general domain of
                 programming, these results do indicate that the concept
                 of bringing ideas from biological representations to
                 genetic programming is a promising one.",
  size =         "8 pages",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@InProceedings{lones:2001:brgp,
  author =       "Michael A. Lones and Andy M. Tyrrell",
  title =        "Biomimetic Representation in Genetic Programming",
  booktitle =    "Computation in Gene Expression",
  year =         "2001",
  editor =       "Hillol Kargupta",
  pages =        "199--204",
  address =      "San Francisco, California, USA",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}

@InProceedings{lones:2001:pgp,
  author =       "Michael A. Lones and Andy M. Tyrrell",
  title =        "Pathways into Genetic Programming",
  booktitle =    "Graduate Student Workshop",
  year =         "2001",
  editor =       "Conor Ryan",
  pages =        "425--428",
  address =      "San Francisco, California, USA",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}

@Article{lones:2002:GPEM,
  author =       "Michael A. Lones and Andy M. Tyrrell",
  title =        "Biomimetic Representation with Genetic Programming
                 Enzyme",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "2",
  pages =        "193--217",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, biomimetic
                 representation",
  ISSN =         "1389-2576",
  URL =          "http://www.kluweronline.com/issn/1389-2576/current",
  abstract =     "The standard parse tree representation of genetic
                 programming, while a good choice from a generative
                 viewpoint, does not capture the variational demands of
                 evolution. This paper addresses the issue of whether
                 representations in genetic programming might be
                 improved by mimicry of biological behaviors,
                 particularly those thought to be important in the
                 evolution of metabolic pathways, computational
                 structures of the cell. This issue is broached through
                 a presentation of enzyme genetic programming, a form of
                 genetic programming which uses a biomimetic
                 representation. Evaluation upon problems in
                 combinational logic design does not show any
                 significant performance advantage over other
                 approaches, though does demonstrate a number of
                 interesting behaviors including the preclusion of
                 bloat.",
  notes =        "Special issue on Gene Expression
                 Kargupta:2002:GPEM

                 Title of paper should be {"}Biomimetic Representation
                 with Enzyme Genetic Programming{"} Also see paper in
                 WCCI 2002. This article subsumes LonTyr01,
                 lones:2001:brgp and lones:2001:pgp",
}

@InProceedings{lones:2002:cabitfmoegp,
  author =       "Michael Lones and Andy Tyrrell",
  title =        "Crossover and Bloat in the Functionality Model of
                 Enzyme Genetic Programming",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "986--991",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The functionality model is a new approach in enzyme
                 genetic programming which enables the evolution of
                 variable length solutions whilst preserving local
                 context. This paper introduces the model and presents
                 an analysis of crossover and the evolution of program
                 size.",
}

@InProceedings{Longshaw:1997:ellg,
  author =       "Tom Longshaw",
  title =        "Evolutionary learning of large Grammars",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "evolutionary programming and evolution strategies",
  pages =        "445",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{Lopez:2002:WSC,
  author =       "A. M. Lopez and H. Lopez and L. Sanchez",
  title =        "{GA}-{P} based search of structures and parameters of
                 dynamical process models",
  year =         "2002",
  month =        sep # " 23 - " # oct # " 4",
  note =         "on line",
  keywords =     "genetic algorithms, genetic programming, GA-P
                 algorithms, System Identification, Hierarchical
                 models",
  abstract =     "The most effective approaches for evolutionary
                 identifying dynamical processes depend on iterative
                 trial-error searches in a hierarchical fashion: a new
                 structure is proposed first; then, its set of
                 parameters is numerically determined, and the process
                 is repeated until a model accurate enough is
                 found.

                 Canonical Genetic Programming has been used to automate
                 this search; but its output can be diffcult to
                 interpret. Because of this reason, the use of
                 hierarchical learning methods, that combine GP search
                 of structures with deterministic optimisation
                 algorithms, has been proposed. We will show in this
                 paper that the output of such methods can be further
                 improved with non hierarchical algorithms. In
                 particular, we will show that the use of GA-P improves
                 the interpretability of the models and does a better
                 model search than previous approaches.",
  notes =        "WSC7 http://wsc7.ugr.es

                 Fig. 2. SMOG evolution. Canonical GP is used for
                 structural search and Hooke-Jeeves method is used for
                 parameter tuning. Modeling direct electrical current
                 motor.",
}

@InCollection{lorenzen:1998:CEACFCPRGP,
  author =       "Peter J. Lorenzen",
  title =        "Comparing the Evaluation of Antiderivatives of Complex
                 Functions with Cartesian versus Polar Representations
                 via Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "84--93",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InCollection{lott:1994:tfar,
  author =       "Christopher G. Lott",
  title =        "Terrain Flattening by Autonomous Robot: {A} Genetic
                 Programming Application",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "99--109",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming, agents",
  ISBN =         "0-18-187263-3",
  notes =        "{"}Successfully used GP to ... robot control programs
                 which can transform any random 12 x 12 grid into
                 basically a flat plane.{"} {"}rudimentary cooperation
                 between robots in acheiving the same goal{"}. This
                 volume contains 20 papers written and submitted by
                 students describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@Article{louchet:2001:GPEM,
  author =       "Jean Louchet",
  title =        "Using an Individual Evolution Strategy for
                 Stereovision",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "2",
  pages =        "101--109",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, artificial
                 evolution, individual evolution strategy, flies,
                 computer vision, image processing, stereovision,
                 software engineering",
  ISSN =         "1389-2576",
  URL =          "http://ipsapp009.lwwonline.com/content/getfile/4723/5/2/fulltext.pdf",
  abstract =     "The fly algorithm is an individual evolution strategy
                 developed for parameter space exploration in computer
                 vision applications. In the application described, each
                 individual represents a geometrical point in the scene
                 and the population itself is used as a
                 three-dimensional model of the scene. A fitness
                 function containing all pixel-level calculations is
                 introduced to exploit simple optical and geometrical
                 properties and evaluate the relevance of each
                 individual as taking part to the scene representation.
                 Classical evolutionary operators (sharing, mutation,
                 crossover) are used. The combined individual approach
                 and low complexity fitness function allow fast
                 processing. Test results and extensions to real-time
                 image sequence processing, mobile objects tracking and
                 mobile robotics are presented.",
}

@InProceedings{loughlin:1999:CGA,
  author =       "Daniel H. Loughlin and S. Ranji Ranjithan",
  title =        "Chance-Constrained Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "369--376",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  abstract =     "latin square, latin hypercube sampling",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{louis:1999:ASSMCIGAT,
  author =       "Sushil J. Louis and Yongmian Zhang",
  title =        "A Sequential Similarity Metric for Case Injected
                 Genetic Algorithms applied to {TSP}s",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "377--384",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{louis:1999:IGATSP,
  author =       "Sushil J. Louis and Rilun Tang",
  title =        "Interactive Genetic Algorithms for the Traveling
                 Salesman Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "385--392",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{LovCie01,
  author =       "Thomas Loveard and Victor Ciesielski",
  title =        "Representing Classification Problems in Genetic
                 Programming",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "2001",
  volume =       "2",
  pages =        "1070--1077",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  email =        "toml@cs.rmit.edu.au",
  keywords =     "genetic algorithms, genetic programming,
                 Classification",
  ISBN =         "0-7803-6657-3",
  URL =          "http://goanna.cs.rmit.edu.au/~toml/cec2001.ps",
  abstract =     "In this paper five alternative methods are proposed to
                 perform multi-class classification tasks using genetic
                 programming. These methods are: Binary decomposition,
                 in which the problem is decomposed into a set of binary
                 problems and standard genetic programming methods are
                 applied; Static range selection, where the set of real
                 values returned by a genetic program is divided into
                 class boundaries using arbitrarily chosen division
                 points; Dynamic range selection in which a subset of
                 training examples are used to determine where, over the
                 set of reals, class boundaries lie; Class enumeration
                 which constructs programs similar in syntactic
                 structure to a decision tree; and evidence accumulation
                 which allows separate branches of the program to add to
                 the certainty of any given class. Results showed that
                 the dynamic range selection method was well suited to
                 the task of multi-class classification and was capable
                 of producing classifiers more accurate than the other
                 methods tried when comparable training times were
                 allowed. Accuracy of the generated classifiers was
                 comparable to alternative approaches over several
                 datasets.",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 Tested on UCI machine learning testsets. STGP. 5
                 approaches to multiclass classifications: binary
                 decomposition, static range, dynamic range, class
                 enumeration (additional data type {"}ClassType{"} (cf
                 C4.5), evidence accumulation cf {"}AddToClass{"}, cf
                 Teller",
}

@InCollection{lowsky:1999:UCFFCOSIPDG,
  author =       "David Lowsky",
  title =        "Using a Cooperative Fitness Function to Coevolve
                 Optimal Strategies in the Iterated Prisoner's Dilemma
                 Game",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "131--139",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InCollection{lu:1998:SMPASVSGP,
  author =       "Hui-Ling Lu",
  title =        "Search the Model Parameters of the Articulatory
                 Singing Voice Synthesizer via Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "94--100",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{lucas:2002:esmatfges,
  author =       "Simon Lucas",
  title =        "Evolving spring-mass models: a test-bed for graph
                 encoding schemes",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "1952--1957",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "For many interesting design problems the solution is
                 most naturally represented as a type of graph. This
                 paper proposes that the problem of evolving spring-mass
                 models for a set of design challenges makes an
                 excellent test-bed for evaluating the performance of
                 various graph encoding schemes. We describe how the
                 problem is set up, and intro-duce a planar graph coding
                 scheme. Results demonstrate that the planar graph
                 encoding scheme significantly out-performs a simple
                 direct encoding scheme on a height-challenge design
                 problem.",
}

@InProceedings{lucas-gonzalez:2001:gpsvmpgp,
  author =       "Socrates A. Lucas-Gonzalez and Hugo Terashima-Marin",
  title =        "Generating Programs for Solving Vector and Matrix
                 Problems using Genetic Programming",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "260--266",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, GP-BNF",
  notes =        "GECCO-2001LB. Implementation based on horner-class GP
                 Kernel. GP-BNF uses C. Element on an array, dot
                 product, adding two matrices, inverse of matrix.
                 iteration (loop) 6 test cases. popsize=100. No details
                 of grammar.",
}

@InProceedings{lucier:1998:pofGP,
  author =       "Bradley J. Lucier and Sudhakar Mamillapalli and Jens
                 Palsberg",
  title =        "Program Optimization for Faster Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "202--207",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{luiz:1994:sppd,
  author =       "Gerald Luiz",
  title =        "Sufficient Parameters for Population Dynamics
                 Simulations with Adaptation",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "91--98",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{luke:1996:etc,
  author =       "Sean Luke and Lee Spector",
  title =        "Evolving Teamwork and Coordination with Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "150--156",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.cs.gmu.edu/~sean/papers/cooperation.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/cooperation.ps.gz",
  size =         "9 pages",
  abstract =     "Some problems can be solved only by multi-agent teams.
                 In using genetic programming to produce such teams, one
                 faces several design decisions. First, there are
                 questions of team diversity and of breeding strategy.
                 In one commonly used scheme, teams consist of clones of
                 single individuals; these individuals breed in the
                 normal way and are cloned to form teams during fitness
                 evaluation. In contrast, teams could also consist of
                 distinct individuals. In this case one can either allow
                 free interbreeding between members of different teams,
                 or one can restrict interbreeding in various ways. A
                 second design decision concerns the types of
                 coordination-facilitating mechanisms provided to
                 individual team members; these range from sensors of
                 various sorts to complex communication systems. This
                 paper examines three breeding strategies (clones, free,
                 and restricted) and three coordination mechanisms
                 (none, deictic sensing, and name-based sensing) for
                 evolving teams of agents in the Serengeti world, a
                 simple predator/prey environment. Among the conclusions
                 are the fact that a simple form of restricted
                 interbreeding outperforms free interbreeding in all
                 teams with distinct individuals, and the fact that
                 name-based sensing consistently outperforms deictic
                 sensing.",
  notes =        "GP-96",
}

@InProceedings{luke:1996:egnee,
  author =       "Sean Luke and Lee Spector",
  title =        "Evolving Graphs and Networks with Edge Encoding:
                 Preliminary Report",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "117--124",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.gmu.edu/~sean/papers/graph-paper.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/graph-paper.ps.gz",
  abstract =     "We present an alternative to the cellular encoding
                 technique [Gruau 1992] for evolving graph and network
                 structures via genetic programming. The new technique,
                 called edge encoding, uses edge operators rather than
                 the node operators of cellular encoding. While both
                 cellular encoding and edge encoding can produce all
                 possible graphs, the two encodings bias the genetic
                 search process in different ways; each may therefore be
                 most useful for a different set of problems. The
                 problems for which these techniques may be used, and
                 for which we think edge encoding may be particularly
                 useful, include the evolution of recurrent neural
                 networks, finite automata, and graph-based queries to
                 symbolic knowledge bases. In this preliminary report we
                 present a technical description of edge encoding and an
                 initial comparison to cellular encoding. Experimental
                 investigation of the relative merits of these encoding
                 schemes is currently in progress.",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{luke:1997:ccmGP,
  author =       "Sean Luke and Lee Spector",
  title =        "A Comparison of Crossover and Mutation in Genetic
                 Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "240--248",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www.cs.gmu.edu/~sean/papers/comparison/comparison.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/comparison/comparison.ps.gz",
  abstract =     "This paper presents a large and systematic body of
                 data on the relative effectiveness of mutation,
                 crossover, and combinations of mutation and crossover
                 in genetic programming (GP). The literature of
                 traditional genetic algorithms contains related
                 studies, but mutation and crossover in GP differ from
                 their traditional counterparts in significant ways. In
                 this paper we present the results from a very large
                 experimental data set, the equivalent of approximately
                 12,000 typical runs of a GP system, systematically
                 exploring a range of parameter settings. The resulting
                 data may be useful not only for practitioners seeking
                 to optimize parameters for GP runs, but also for
                 theorists exploring issues such as the role of
                 {"}building blocks{"} in GP.",
  notes =        "GP-97. 6-mux, lawn mower, symbolic regression, Santa
                 Fe trail artificial ant. SEE ALSO luke:1998:rcxmGP.

                 The Gzipped PostScript version (.ps.gz) does not come
                 with figures; to get the figures for the PostScript
                 version, use the figures URLs below",
  figures =      "http://www.cs.gmu.edu/~sean/papers/comparison/figures1-2.ps.gz",
  figures2 =     "http://www.cs.gmu.edu/~sean/papers/comparison/figures3-4.ps.gz",
}

@InProceedings{luke:1997:csstcGP,
  author =       "Sean Luke and Charles Hohn and Jonathan Farris and
                 Gary Jackson and James Hendler",
  title =        "Co-evolving Soccer Softbot Team Coordination with
                 Genetic Programming",
  booktitle =    "Proceedings of the First International Workshop on
                 RoboCup, at the International Joint Conference on
                 Artificial Intelligence",
  year =         "1997",
  address =      "Nagoya, Japan",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.gmu.edu/~sean/papers/robocupc.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/robocupc.ps.gz",
  size =         "4 pages",
  abstract =     "Genetic Programming is a promising new method for
                 automatically generating functions and algorithms
                 through natural selection. In contrast to other
                 learning methods, Genetic Programming's automatic
                 programming makes it a natural approach for developing
                 algorithmic robot behaviors. In this paper we present
                 an overview of how we apply Genetic Programming to
                 behavior-based team coordination in the RoboCup Soccer
                 Server domain. The result is not just a hand-coded
                 soccer algorithm, but a team of softbots which have
                 learned on their own how to play a reasonable game of
                 soccer.",
  notes =        "IJCAI-97

                 Given the acknowledged challenges of applying Genetic
                 Programming to robot soccer, we were happy to just show
                 up at Nagoya with an entry in the RoboCup simulation
                 track. However, Maryland's Genetic Programming entry in
                 in fact beat its first two competitors (5-2 against U
                 British Columbia, Canada and 17-0 over Toyohashi
                 University of Science and Technology, Japan) before
                 losing to University of Tokyo (last year's champion,
                 6-1) and subsequently Tokyo Institute of Technology
                 (16-4) in the single-elimination round. For its
                 research achievement in demonstrating the feasibility
                 of evolutionary computation in a very difficult domain,
                 Maryland's entry also won the RoboCup Scientific
                 Challenge Award.
                 http://ci.etl.go.jp/~noda/soccer/RoboCup97/result.html
                 Part of Email from John Koza Fri, 29 Aug 1997 21:37:50
                 PDT to genetic-programming@cs.stanford.edu {"}The
                 Maryland entry competed against various hand-written
                 robot controllers (all of which are very good examples
                 of clever human programming) and its success
                 demonstrated, I think, that GP is precisely the right
                 way to create programmers when the task really gets
                 difficult. {"}

                 Too short to give full technical details: STGP, 50ish
                 problem dependant functions. team composed of 2-3
                 squads of identical players. Each squad 2 trees (used
                 for possetion and non-possetion of ball. 6 or 12 trees
                 per GP indivdual. Co-evolution. lil-gp. Stepped
                 evolution (like seeding?) build squad from good
                 players, team from good squads.",
}

@InProceedings{luke:1998:rcxmGP,
  author =       "Sean Luke and Lee Spector",
  title =        "A Revised Comparison of Crossover and Mutation in
                 Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "208--213",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www.cs.gmu.edu/~sean/papers/revisedgp98.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/revisedgp98.ps.gz",
  abstract =     "In [Luke and Spector 1997] we presented a
                 comprehensive suite of data comparing GP crossover and
                 point mutation over four domains and a wide range of
                 parameter settings. Unfortunately, the results were
                 marred by statistical flaws. This revision of the study
                 eliminates these flaws, with three times as much the
                 data as the original experiments had. Our results again
                 show that crossover does have some advantage over
                 mutation given the right parameter settings (primarily
                 larger population sizes), though the difference between
                 the two surprisingly small. Further, the results are
                 complex, suggesting that the big picture is more
                 complicated than is commonly believed.",
  notes =        "GP-98 This paper is a revision of a previous paper
                 luke:1997:ccmGP, with statistical correction and a
                 considerable new set of data. However, the original
                 also has some data that does not appear here, so you
                 may want to consider getting both. Also: Figures 1
                 through 4 are separated from the rest of the paper in
                 the Gzipped PostScript version (not the PDF version).
                 The figures are listed in the figure URLs below.
                 Finally: if you downloaded a copy of this paper prior
                 to May 20, 1998, its graphs were wrong; get the revised
                 revised version. :-",
  figures =      "http://www.cs.gmu.edu/~sean/papers/revisedgp98graphs.ps.gz",
}

@InProceedings{luke:1998:RoboCup97,
  author =       "Sean Luke",
  title =        "Genetic Programming Produced Competitive Soccer
                 Softbot Teams for RoboCup97",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "214--222",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www.cs.gmu.edu/~sean/papers/robocupgp98.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/robocupgp98.ps.gz",
  abstract =     "At RoboCup, teams of autonomous robots or software
                 softbots compete in simulated soccer matches to
                 demonstrate cooperative robotics techniques in a very
                 difficult, real-time, noisy environment. At the
                 IJCAI/RoboCup97 softbot competition, all entries but
                 ours used human-crafted cooperative decision-making
                 behaviors. We instead entered a softbot team whose
                 high-level decision making behaviors had been entirely
                 evolved using genetic programming. Our team won its
                 first two games against human-crafted opponent teams,
                 and received the RoboCup Scientific Challenge Award.
                 This report discusses the issues we faced and the
                 approach we took to use GP to evolve our robot soccer
                 team for this difficult environment.",
  notes =        "GP-98 This paper is similar to an earlier workshop
                 paper luke:1997:csstcGP. The key difference being that
                 the workshop paper, which was not for a Genetic
                 Programming audience, is short on experimental details
                 and long on introductions to how GP works. There also
                 exists a short invited paper luke:1998:sretro detailing
                 how this experiment could have been improved. Also
                 available is a short sidebar for an AI Magazine
                 article.",
}

@Article{luke:1998:firsttime,
  author =       "Sean Luke and Shugo Hamahashi and Koji Kyoda and
                 Hiroki Ueda",
  title =        "Biology: See It Again -- for the First Time",
  journal =      "IEEE Intelligent Systems",
  year =         "1998",
  volume =       "13",
  number =       "5",
  month =        sep # "/" # oct,
  keywords =     "genetic algorithms, genetic programming, biological
                 modelling, DNA",
  URL =          "http://www.cs.gmu.edu/~sean/papers/biology.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/biology.ps.gz",
  abstract =     "Computer science owes a huge debt to biological
                 systems. The field itself came about largely as an
                 attempt to understand and replicate the function and
                 abilities of the brain, a complex biological creation.
                 From this early lineage has sprung many subfields
                 derived largely from biological metaphors: computer
                 vision, neural networks, evolutionary computation,
                 robotics, multi-agent studies, and much of artificial
                 intelligence. In some areas, the computer has bested
                 its biological counterparts in efficiency and
                 simplicity. But for many domains, even after decades of
                 hard work, the biological {"}real thing{"} is still
                 superior to the artificial algorithms inspired by it.",
  size =         "3 pages",
  notes =        "Invited Article. Argues for a revisitation of the
                 biological roots behind artificial intelligence and
                 evolutionary computation",
}

@InProceedings{luke:1998:sretro,
  author =       "Sean Luke",
  title =        "Evolving SoccerBots: {A} Retrospective",
  booktitle =    "Proceedings of the 12th Annual Conference of the
                 Japanese Society for Artificial Intelligence",
  year =         "1998",
  URL =          "http://www.cs.gmu.edu/~sean/papers/robocupShort.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/robocupShort.ps.gz",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In the RoboCup97 robot soccer tournament, we entered a
                 team of softbot programs whose player strategies had
                 been entirely learned by computer. Our team beat other
                 human-coded competitors and received the RoboCup97
                 Scientific Challenge award. This paper discusses our
                 approach, and details various ways that, in retrospect,
                 it could have been improved.",
  notes =        "Invited Article. This short invited paper was meant to
                 complement the more complete GP98 and RoboCup97 papers,
                 and an AI Magazine sidebar, by discussing things that
                 could have been improved from our previous attempt.",
}

@InProceedings{luke:1999:P,
  author =       "Sean Luke and Shugo Hamahashi and Hiroaki Kitano",
  title =        "``Genetic'' Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1098--1105",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  URL =          "http://www.cs.gmu.edu/~sean/papers/gene-gecco99.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/gene-gecco99.ps.gz",
  abstract =     "Much of evolutionary computation was inspired by
                 Mendelian genetics. But modern genetics has since
                 advanced considerably, revealing that genes are not
                 simply parameter settings, but interactive cogs in a
                 complex chemical machine. At the same time, an
                 increasing number of evolutionary computation domains
                 are evolving non-parameterized mechanisms such as
                 neural networks or symbolic computer programs. As such,
                 we think modern biological genetics offers much in
                 helping us understand how to evolve such things. In
                 this paper, we present a gene regulation model for
                 Drosophila melanogaster. We then apply gene regulation
                 to evolve deterministic finite-state automata, and show
                 that our approach does well compared to past examples
                 from the literature.",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{luke:2000:2ftcaGP,
  author =       "Sean Luke",
  title =        "Two Fast Tree-Creation Algorithms for Genetic
                 Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2000",
  volume =       "4",
  number =       "3",
  pages =        "274--283",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Population
                 Initialization, Tree Creation, Subtree Mutation, Tree
                 Growth, Introns, Bloat",
  size =         "9 pages",
  URL =          "http://ieeexplore.ieee.org/iel5/4235/18897/00873237.pdf",
  URL =          "http://www.cs.gmu.edu/~sean/papers/treecreation.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/treecreation.ps.gz",
  abstract =     "Genetic programming is an evolutionary optimization
                 method that produces functional programs to solve a
                 given task. These programs commonly take the form of
                 trees representing LISP s-expressions, and a typical
                 evolutionary run produces a great many of these trees.
                 For this reason, a good tree generation algorithm is
                 very important to genetic programming. This paper
                 presents two new tree-generation algorithms for genetic
                 programming and for strongly-typed genetic programming,
                 a common variant. These algorithms are fast, allow the
                 user to request specific tree sizes, and guarantee
                 probabilities of certain nodes appearing in trees. The
                 paper analyzes these two algorithms and compares them
                 with traditional and recently proposed approaches.",
}

@InProceedings{luke:2000:cgnci,
  author =       "Sean Luke",
  title =        "Code Growth is Not Caused by Introns",
  pages =        "228--235",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming, bloat,
                 introns, ineffective code",
  URL =          "http://www.cs.gmu.edu/~sean/papers/intronpaper.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/intronpaper.ps.gz",
  size =         "8 pages",
  abstract =     "Genetic programming trees have a strong tendency to
                 grow rapidly and relatively independent of fitness, a
                 serious flaw which has received considerable attention
                 in the genetic programming literature. Much of this
                 literature has implicated introns, subtree structures
                 with no effect on the an individual's fitness
                 assessment. The propagation of inviable code, a certain
                 kind of intron, has been especially linked to tree
                 growth. However this paper presents evidence which
                 shows that denying inviable code the opportunity to
                 propagate actually increases tree growth. The paper
                 argues that rather than causing tree growth, a rise in
                 inviable code is in fact an expected result of tree
                 growth. Lastly, this paper proposes a more general
                 theory of growth for which introns are merely a
                 symptom.",
  notes =        "Part of whitley:2000:GECCOlb",
}

@PhdThesis{luke:dissertation,
  author =       "Sean Luke",
  title =        "Issues in Scaling Genetic Programming: Breeding
                 Strategies, Tree Generation, and Code Bloat",
  school =       "Department of Computer Science, University of
                 Maryland",
  address =      "A. V. Williams Building, University of Maryland,
                 College Park, MD 20742 USA",
  year =         "2000",
  size =         "178 pages",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.gmu.edu/~sean/papers/thesis2p.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/thesis2p.ps.gz",
  abstract =     "Genetic Programming is an evolutionary computation
                 technique which searches for those computer programs
                 that best solve a given problem. As genetic programming
                 is applied to increasingly difficult problems, its
                 effectiveness is hampered by the tendency of candidate
                 program solutions to grow in size independent of any
                 corresponding increases in quality. This bloat in
                 solutions slows the search process, interferes with
                 genetic programming's searching, and ultimately
                 consumes all available memory. The challenge for
                 scaling up genetic programming is to find the best
                 solutions possible before bloat puts a stop to
                 evolution. This can be tackled either by finding better
                 solutions more rapidly, or by taking measures to delay
                 bloat as long as possible.

                 This thesis discusses issues both in speeding the
                 search process and in delaying bloat in order to scale
                 genetic programming to tackle harder problems. It
                 describes evolutionary computation and genetic
                 programming, and details the application of genetic
                 programming to cooperative robot soccer and to language
                 induction. The thesis then compares genetic programming
                 breeding strategies, showing the conditions under which
                 each strategy produces better individuals with less
                 bloating. It then analyzes the tree growth properties
                 of the standard tree generation algorithms used, and
                 proposes new, fast algorithms which give the user
                 better control over tree size. Lastly, it presents
                 evidence which directly contradicts existing bloat
                 theories, and gives a more general theory of code
                 growth, showing that the issue is more complicated than
                 it first appears.",
  errata =       "1. In Algorithm 2 (p. 6), the line P<-P\\[q] should
                 read P<-P\\[s]. 2. Figures 5.2 through 5.5 (p. 38-39)
                 are not in proper evolutionary-time order. The proper
                 order is 5.4, 5.5, 5.2, 5.3.",
}

@InProceedings{Luke1:2001:GECCO,
  title =        "A Survey and Comparison of Tree Generation
                 Algorithms",
  author =       "Sean Luke and Liviu Panait",
  pages =        "81--88",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, tree
                 generation algorithms, initalization",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{luke2:2001:gecco,
  title =        "When Short Runs Beat Long Runs",
  author =       "Sean Luke",
  pages =        "74--80",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, schedules,
                 restarts, run length, critical points",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{luke2:2002:gecco,
  author =       "Sean Luke and Liviu Panait",
  title =        "Lexicographic Parsimony Pressure",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "829--836",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 parsimony pressure",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{luke:2002:gecco,
  author =       "Sean Luke and Liviu Panait",
  title =        "Is The Perfect The Enemy Of The Good?",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "820--828",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, computational
                 effort, cumulative probability of success",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)

                 Nominated for best at GECCO award",
}

@InProceedings{luke:ppsn2002:pp411,
  author =       "Sean Luke and Liviu Panait",
  title =        "Fighting Bloat with Nonparametric Parsimony Pressure",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "411 ff.",
  year =         "2002",
  editor =       "J.-J. Merelo Guerv\'os and P. Adamidis and H.-G. Beyer
                 and J.-L. Fern\'andez-Villaca\~nas and H.-P. Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Keywords: Technique::Genetic programming - general",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900411.pdf",
}

@InProceedings{lukschandl:1998:1java,
  author =       "Eduard Lukschandl and Mangus Holmlund and Eirk Moden",
  title =        "Automatic Evolution of {Java} Bytecode: First
                 experience with the {Java} virtual machine",
  booktitle =    "Late Breaking Papers at EuroGP'98: the First European
                 Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and Marc Schoenauer
                 and Terry Fogarty and Wolfgang Banzhaf",
  pages =        "14--16",
  address =      "Paris, France",
  publisher_address = "School of Computer Science",
  month =        "14-15 " # apr,
  publisher =    "CSRP-98-10, The University of Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  size =         "3 pages",
  notes =        "EuroGP'98LB part of Poli:1998:egplb",
}

@InProceedings{lukschandl:1998:ijbGP,
  author =       "Eduard Lukschandl and Magnus Holmlund and Eric Moden
                 and Mats Nordahl and Peter Nordin",
  title =        "Induction of {Java} Bytecode with Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{lukschandl:1999:eraJBGP,
  author =       "Eduard Lukschandl and Henrik Borgvall and Lars Nohle
                 and Mats Nordahl and Peter Nordin",
  title =        "Evolving Routing Algorithms with the {JBGP-System}",
  booktitle =    "Evolutionary Image Analysis, Signal Processing and
                 Telecommunications: First European Workshop, EvoIASP'99
                 and EuroEcTel'99",
  year =         "1999",
  editor =       "Riccardo Poli and Hans-Michael Voigt and Stefano
                 Cagnoni and Dave Corne and George D. Smith and Terence
                 C. Fogarty",
  volume =       "1596",
  series =       "LNCS",
  pages =        "193--202",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "28-29 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65837-8",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-65837-8",
  abstract =     "This paper describes work in progress where we apply
                 genetic programming to the problem of finding routing
                 algorithms in telecommunications networks, using a
                 network simulator and the Java Bytecode Genetic
                 Programming System being developed at EHPT lab.",
  notes =        "EvoIASP99'99 and EuroEcTel'99",
}

@Misc{lukschandl:1999:EBCEUGP,
  author =       "Eduard Lukschandl",
  title =        "Evolving the Behavior of Collaborating Entities Using
                 Genetic Programming",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, agents, java,
                 telecommunications",
  URL =          "http://www.ai.mit.edu/people/unamay/phd-final/GECCO-99-Student.html",
}

@InProceedings{lukschandl:2000:DJBGP,
  author =       "Eduard Lukschandl and Henrik Borgvall and Lars Nohle
                 and Mats Nordahl and Peter Nordin",
  title =        "Distributed Java Bytecode Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "316--325",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "This paper describes a method for evolutionary program
                 induction of binary Java bytecode. Like many other
                 machine code based methods it uses a linear genome. The
                 genetic operators are adapted to the stack architecture
                 and preserve stack depth during crossover. In this work
                 we have extended a previous system to run in a
                 distributed manner on several different physical
                 machines. We call our new system Distributed Java
                 Bytecode Genetic Programming (DJBGP). We use the
                 Voyager package for migration of Java individuals. The
                 system's feasibility is demonstrated on a telecom
                 routing problem.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{Lukschandl:2000:GECCOlb,
  author =       "Eduard Lukschandl and Peter Nordin and Mats Nordahl",
  title =        "Using the Java Method Evolver for Load Balancing in
                 Communication Networks",
  pages =        "236--239",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Part of whitley:2000:GECCOlb",
}

@InCollection{luman:2002:DKAGA,
  author =       "Ron {Luman II}",
  title =        "Dynamic Keystroke Analysis via Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "129--138",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@Article{lundberg:1999:elvis,
  author =       "Borje Lundberg",
  title =        "Elvis ror pa sig",
  journal =      "Expressen",
  year =         "1991",
  pages =        "17",
  month =        "21 " # aug,
  note =         "Largest circulation swedish newspaper",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.expressen.se/article.asp?id=21927",
  notes =        "Peter Nordin and Mats Nordahl, Chalmers Unversity of
                 Technology humanoid robot Elvis",
}

@TechReport{lutton:1995:IFS,
  author =       "Evelyne Lutton and and Jacques Levy-Vehel and
                 Guillaume Cretin and Philippe Glevarec and Cidric
                 Roll",
  title =        "Mixed {IFS}: Resolution of the Inverse Problem Using
                 Genetic Programming",
  institution =  "Inria",
  year =         "1995",
  type =         "Research Report",
  number =       "No 2631",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-rocq.inria.fr/Publications/RR-2631.ps.gz",
  abstract =     "We address here the resolution of the so-called
                 inverse problem for IFS. This problem has already been
                 widely considered, and some studies have been performed
                 for affine IFS, using deterministic or stochastic
                 methods (Simulated Annealing or Genetic Algorithm).
                 When dealing with non affine IFS, the usual techniques
                 do not perform well, except if some <i> a priori</I>
                 hypotheses on the structure of the IFS (number and type
                 functions) are made. In this work, a Genetic
                 Programming method is investigated to solve the
                 &quot;general&quot; inverse problem, which permits to
                 perform at the same time a numeric and a symbolic
                 optimization. The use of &quot;mixed IFS&quot;, as we
                 call them, may enlarge the scope of some applications,
                 as for example image compression, because they allow to
                 code a wider range of shapes.",
  notes =        "Mainly in english, abstract also en francaise Use
                 distance masks for deciding how close GP is to target
                 image (part of fitness function). Says {"}The distance
                 images are very efficient{"} [page 12]. Mutation of
                 constants by +/-10% and variables to constants. Notes
                 constants {"}disappear{"} from the population. popsize
                 20 to 50 and 1000 to 2000 generations [page 10]. GP
                 functions {"}does not resemble the one [used to create]
                 the target images{"} [page 12]. {"}GP algorith, which
                 seems to perform a more efficient search in a large
                 space.{"} [page 16]",
  size =         "17 pages. See also lutton:1995:IFScs and
                 Cretin:al:EA95
                 http://www-syntim.inria.fr/fractales/fractales-eng.html",
}

@Article{lutton:1995:IFScs,
  author =       "Evelyne Lutton and and Jacques Levy-Vehel and
                 Guillaume Cretin and Philippe Glevarec and Cidric
                 Roll",
  title =        "Mixed {IFS}: Resolution of the Inverse Problem Using
                 Genetic Programming",
  journal =      "Complex Systems",
  year =         "1995",
  volume =       "9",
  pages =        "375--398",
  keywords =     "genetic algorithms, genetic programming, fractals",
  abstract =     "We address here the resolution of the so-called
                 inverse problem for the iterated functions system
                 (IFS). This problem has already been widely considered,
                 and some studies have been performed for the affine
                 IFS, using deterministic or stochastic methods
                 (simulated annealing or genetic algorithm). In dealing
                 with the nonaffine IFS, the usual techniques do not
                 perform well unless some a priori hypotheses on the
                 structure of the IFS (number and type of functions) are
                 made. In this work, a genetic programming method is
                 investigated to solve the ``general'' inverse problem,
                 which allows the simultaneous performance of a numeric
                 and a symbolic optimization. The use of a ``mixed IFS''
                 may enlarge the scope of some applications, for
                 example, image compression, because it allows a wider
                 range of shapes to be coded.",
  notes =        "see also Cretin:al:EA95
                 http://www-syntim.inria.fr/fractales/fractales-eng.html",
}

@Proceedings{lutton:2002:GP,
  title =        "Genetic Programming, Proceedings of the 5th European
                 Conference, Euro{GP} 2002",
  year =         "2002",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  volume =       "2278",
  series =       "LNCS",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  size =         "336 pages",
  notes =        "EuroGP'2002",
}

@InProceedings{mabu:2002:olognp,
  author =       "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu and
                 Junichi Murata",
  title =        "Online learning of Genetic Network Programming
                 ({GNP})",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "321--326",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "A new evolutionary computation method named Genetic
                 Network Programming (GNP) was proposed recently. In
                 this paper, an online learning method for GNP is
                 proposed. This method uses Q learning to improve its
                 state transition rules so that it can make GNP adapt to
                 the dynamic environments efficiently.",
}

@InProceedings{machado:1999:bbir,
  author =       "Penousal Machado and Francisco B. Pereira and Amilcar
                 Cardoso and Ernesto Costa",
  title =        "Busy Beaver -- the Influence of Representation",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "29--38",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Penousal Machado won special jury prize.

                 Busy beaver = Turing machine which generates longest
                 pattern of 1s and terminates. Solutions only known for
                 very small Turing machines.",
}

@InProceedings{Maeda:2000:GECCO,
  author =       "Yoichiro Maeda and Satomi Kawaguchi",
  title =        "Redundant Node Pruning and Adaptive Search Method for
                 Genetic Programming",
  pages =        "535",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{Maeshiro:1997:gceo,
  author =       "Tetsuya Maeshiro and Masayuki Kimura",
  title =        "Genetic Code as an Evolving Organism",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Artifical life and evolutionary robotics",
  pages =        "413",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@Article{Harman:2002:GPEM,
  author =       "Kiarash Mahdavi and Mark Harman",
  title =        "Book Review: Automatic Re-Engineering of Software
                 Using Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "2",
  pages =        "219--221",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1389-2576",
  notes =        "Review of ryan:book Cites:

                 M.D.Ernst, Jake Cockrell, William G. Griswold and David
                 Notkin, Dynamically discovering likely program
                 invariants to support program evolution, IEEE
                 Transactions on Software Engineering, Vol. 27, No. 2,
                 pp. 1-25, 2001.

                 Kenneth Peter Williams, Evolutionary algorithms for
                 automatic parallelization, PhD Thesis, University of
                 Reading, UK, Department of Computer Science, September
                 1998.

                 ",
}

@Article{makarov:1999:fpes:sfsdGP,
  author =       "Dmitrii E. Makarov and Horia Metiu",
  title =        "Fitting potential-energy surfaces: {A} search in the
                 function space by directed genettic programming",
  journal =      "Journal of Chemical Physics",
  year =         "1998",
  volume =       "108",
  number =       "2",
  pages =        "590--598",
  month =        "8 " # jan,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{maley:1999:FSTOE,
  author =       "C. C. Maley",
  title =        "Four Steps Toward Open-Ended Evolution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1336--1343",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{MallBent99,
  author =       "Hugh Mallinson and Peter Bentley",
  title =        "Evolving Fuzzy Rules for Pattern Classification",
  booktitle =    "Computational Integration for Modelling, Control and
                 Automation '99",
  year =         "1999",
  editor =       "Masoud Mohammadian",
  volume =       "1",
  address =      "Hotel Marriott, Vienna, Austria",
  month =        "17-19 " # feb,
  publisher =    "IOS Press",
  keywords =     "genetic algorithms, genetic programming, fuzzy
                 classification",
  ISBN =         "90-5199-473-7",
  notes =        "CIMCA'99
                 http://www.gscit.monash.edu.au/conferences/cimca99/

                 UCI Wisconsin Breast Cancer",
}

@InCollection{malmer:1994:hive,
  author =       "Daniel Malmer",
  title =        "Hive: Development of a Language Among Artificial Life
                 Forms",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "99--107",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, Finite State Machine, Agents,
                 Communication",
  ISBN =         "0-18-182105-2",
  notes =        "Bees, Genesys This volume contains 22 papers written
                 and submitted by students describing their term
                 projects for the course in artificial life (Computer
                 Science 425) at Stanford University offered during the
                 spring quarter quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@Book{mange:1998:bicm,
  editor =       "Daniel Mange and Marco Tomassini",
  title =        "Bio-Inspired Computing Machines",
  publisher =    "Presses Polytechniques et Universitaires Romandes",
  year =         "1998",
  ISBN =         "2-88074-371-0",
  URL =          "http://lslwww.epfl.ch/pages/publications/books/1998_1/contents.html",
  abstract =     "This volume, written by experts in the field, gives a
                 modern, rigorous and unified presentation of the
                 application of biological concepts to the design of
                 novel computing machines and algorithms. While science
                 has as its fundamental goal the understanding of
                 Nature, the engineering disciplines attempt to use this
                 knowledge to the ultimate benefit of Mankind. Over the
                 past few decades this gap has narrowed to some extent.
                 A growing group of scientists has begun engineering
                 artificial worlds to test and probe their theories,
                 while engineers have turned to Nature, seeking
                 inspiration in its workings to construct novel systems.
                 The organization of living beings is a powerful source
                 of ideas for computer scientists and engineers. This
                 book studies the construction of machines and
                 algorithms based on natural processes: biological
                 evolution, which gives rise to genetic algorithms,
                 cellular development, which leads to self-replicating
                 and self-repairing machines, and the nervous system in
                 living beings, which serves as the underlying
                 motivation for artificial learning systems, such as
                 neural networks.",
  notes =        "Contents

                 An Introduction to Bio-Inspired Machines - An
                 Introduction to Digital Systems - An Introduction to
                 Cellular Automata - Evolutionary Algorithms and their
                 Applications - Programming Cellular Machines by
                 Cellular Programming - Multiplexer-Based Cells -
                 Demultiplexer-Based Cells - Binary Decision
                 Machine-Based Cells - Self-Repairing Molecules and
                 Cells - L-hardware: Modeling and Implementing Cellular
                 Development - Using L-systems - Artificial Neural
                 Networks: Algorithms and Hardware Implementation -
                 Evolution and Learning in Autonomous Robotic Agents -
                 Bibliography - Index.

                 Reviewed in greenwood:2001:bicm",
  size =         "384 pages",
}

@InProceedings{marchesi:1997:deeGP,
  author =       "Bruno Marchesi and Alvaro Luiz Stelle and Heitor
                 Silverio Lopes",
  title =        "Detection of Epileptic Events using Genetic
                 Programming",
  booktitle =    "Proceedings - 19th International Conference -
                 IEEE/EMBS",
  year =         "1997",
  pages =        "1198--1201",
  address =      "Chicago, IL. USA",
  month =        oct # " 30 - " # nov # " 2",
  organisation = "IEEE",
  keywords =     "genetic algorithms, genetic programming, signal
                 processing, EEG",
  file =         "embs98.pdf",
  size =         "4 pages",
  abstract =     "This paper presents a method using genetic programming
                 for automatic detection of 3 Hz spike-and-slow- wave
                 complexes, that are a characteristic of typical
                 absences, in electroencephalogram (EEG) signals.
                 Training features are extracted from 1s EEG frames,
                 randomly chosen from pre-recorded files. The frames are
                 visually classified as spike-and-slow-wave complexes
                 (SASWC) or non-spike- and-slow-wave complexes (NSASWC).
                 Genetic programming techniques are then applied to
                 these data to build a program capable of recognizing
                 such complexes.",
  notes =        "

                 ",
}

@InProceedings{marchiori:1999:AFGAHP,
  author =       "Elena Marchiori and Claudio Rossi",
  title =        "A Flipping Genetic Algorithm for Hard 3-{SAT}
                 Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "393--400",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{marconi:1998:hpGAfckg,
  author =       "Jamie Marconi and James A. Foster",
  title =        "A Hard Problem for Genetic Algorithms: Finding Cliques
                 in Keller Graphs",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "650--655",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Keller
                 conjecture, Keller graphs, maximum clique, hardness,
                 complexity",
  file =         "c112.pdf",
  size =         "6 pages",
  abstract =     "We present evidence that finding the maximum clique in
                 Keller graphs is an example of a family of problems
                 which are both natural and inherently difficult for
                 genetic algorithms. Specifically, we employ a hybrid
                 genetic algorithm to find the largest clique in Keller
                 graphs. We present theoretical reasons why this problem
                 is likely to be particularly hard for this family of
                 graphs. Our results confirm this suspicion. We then
                 discuss several characteristics of this graph family
                 which confound genetic algorithms: its uniformity, edge
                 density and small diameter.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@InProceedings{marek:2002:gecco:lbp,
  title =        "Learning Visual Feature Detectors for Obstacle
                 Avoidance Using Genetic Programming",
  author =       "Andrew J. Marek and William D. Smart and Martin C.
                 Martin",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "330--336",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 Suggests negative impact of seed in initial population
                 p335",
}

@Article{marenbach:1995:at,
  author =       "Peter Marenbach and Kurt Dirk Bettenhausen and Bernd
                 Cuno",
  journal =      "at -- Automatisierungstechnik",
  number =       "6",
  pages =        "277--288",
  title =        "{Selbstorganisierende Generierung strukturierter
                 Prozemodelle}",
  volume =       "43",
  keywords =     "genetic algorithms, genetic programming,
                 Selbstorganisierende Modellbildung",
  year =         "1995",
  email =        "mali@rt.e-technik.tu-darmstadt.de",
  notes =        "In German",
}

@TechReport{marenbach:1995:tr01,
  author =       "Peter Marenbach",
  address =      "Landgraf-Georg-Str.~4, D-64283 Darmstadt, Germany",
  institution =  "FG Regelsystemtheorie \& Robotik, TH Darmstadt",
  title =        "{Status und Perspektiven der strukturierten
                 Modellbildung mit Hilfe Genetischer Algorithmen}",
  year =         "1995",
  size =         "26 pages",
  keywords =     "genetic algorithms, genetic programming,
                 Selbstorganisierende Modellbildung",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/trsmog9501.ps.gz",
  email =        "mali@rt.e-technik.tu-darmstadt.de",
  notes =        "In German",
}

@InProceedings{marenbach:1996:spdpm,
  author =       "Peter Marenbach and Kurt D. Betterhausen and Stephan
                 Freyer",
  title =        "Signal Path Oriented Approach for Generation of
                 Dynamic Process Models",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms, process
                 engineering, modelling, SMOG",
  pages =        "327--332",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/gp96.ps.gz",
  size =         "6 pages",
  abstract =     "This paper discusses our tool for automatic generation
                 of structured models for complex dynamic processes by
                 means of genetic programming. In contrast to other
                 techniques which use genetic programming to find an
                 appropriate arithmetic expression in order to describe
                 the input-output behaviour of a process, this tool is
                 based on a block oriented approach with a transparent
                 description of signal paths. A short survey on other
                 techniques for computer based system identification is
                 given and the basic concept of SMOG (Structured MOdel
                 Generator) is described. Furthermore latest extensions
                 of the system are presented in detail, including
                 automatic defined sub-models and qualitative fitness
                 criteria.",
  notes =        "GP-96 onject oriented GP OOGP",
}

@InProceedings{marenbach:1997:Evicnf,
  author =       "P. Marenbach and M. Brown",
  title =        "Evolutionary versus inductive construction of
                 neurofuzzy systems for bioprocess modelling",
  booktitle =    "Second International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1997",
  editor =       "Ali Zalzala",
  address =      "University of Strathclyde, Glasgow, UK",
  publisher_address = "Savoy Place, London WC2R 0BL, UK",
  month =        "1-4 " # sep,
  publisher =    "Institution of Electrical Engineers",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GALESIA'97",
}

@Article{Marenbach1998,
  author =       "Peter Marenbach",
  title =        "Using Prior Knowledge and Obtaining Process Insight in
                 Data Based Modelling of Bioprocesses",
  journal =      "System Analysis Modelling Simulation",
  year =         "1998",
  howpublished = "Overseas Publishers association",
  volume =       "31",
  pages =        "39--59",
  keywords =     "genetic algorithms, genetic programming,
                 biotechnology, bioprocesses, data based modelling,
                 SMOG",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/publications.html#SAMS98",
  notes =        "

                 ",
}

@Article{Marenbachetal1997,
  author =       "Peter Marenbach and Kurt D. Bettenhausen and Stephan
                 Freyer and Ulrich Nieken and Hans Rettenmaier",
  title =        "Data-driven Structured Modelling of a Biotechnological
                 Fed-batch Fermentation by Means of Genetic
                 Programming",
  journal =      "Proc.\ of the Institution of Mechanical Engineers Part
                 I",
  year =         "1997",
  volume =       "211",
  pages =        "325--332",
  keywords =     "genetic algorithms, genetic programming,
                 biotechnology, modelling, system identification,
                 fermentation processes, SMOG",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/publications.html#MEP97",
  notes =        "

                 ",
}

@InCollection{MarenbachFreyerRST09/98,
  booktitle =    "Industrielle Anwendung Evolution{\"a}rer Algorithmen",
  editor =       "S. Hafner",
  publisher =    "R.\ Oldenbourg Verlag",
  title =        "Generierung von Modellen biotechnologischer Prozesse",
  pages =        "91--102",
  author =       "Peter Marenbach and Stephan Freyer",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming, bioprocess,
                 modelling, SMOG",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/LIT",
  email =        "pmarenbach@gmx.net",
  notes =        "In German",
}

@PhdThesis{Marenbach:thesis,
  author =       "Peter Marenbach",
  title =        "Rechnergestützte Methoden zur interaktiven
                 Modellierung biotechnologischer Prozesse",
  school =       "TU Darmstadt",
  year =         "1999",
  type =         "Dissertation, Berichte aus der
                 Automatisierungstechnik",
  address =      "Aachen, Germany",
  month =        oct,
  publisher =    "Shaker Verlag",
  ISBN =         "3-8265-6574-6",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/LIT",
  email =        "pmarenbach@gmx.net",
  keywords =     "genetic algorithms, genetic programming,
                 Automatisierungstechnik, Modellbildung, Evolutionäre
                 Algorithmen, Biotechnologie, Neuro-Fuzzy-Systeme,
                 datengetriebene Modellbildung",
  URL =          "http://www.shaker.de/Online-Gesamtkatalog/Details.idc?ISBN=3-8265-6574-6",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/LIT/diss/Marenbach1999.html",
  ISBN =         "3-8265-6574-6",
  notes =        "In German",
}

@InProceedings{margetts:2001:EuroGP,
  author =       "Steve Margetts and Antonia J. Jones",
  title =        "An Adaptive Mapping for Developmental Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "97--107",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Developmental
                 Genetic Programming, Adaptive Genotype to Phenotype
                 Mappings, MAX Problem",
  ISBN =         "3-540-41899-7",
  size =         "11 pages",
  abstract =     "In this article we introduce a general framework for
                 constructing an adaptive genotype-to-phenotype mapping,
                 and apply it to developmental genetic programming. In
                 this preliminary investigation, we run a series of
                 comparative experiments on a simple test problem. Our
                 results show that the adaptive algorithm is able to
                 outperform its non-adaptive counterpart.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{mariano:1999:MAAMOOP,
  author =       "Carlos E. Mariano and Eduardo Morales M.",
  title =        "{MOAQ} an Ant-{Q} Algorithm for Multiple Objective
                 Optimization Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "894--901",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{marin:1999:EOTED,
  author =       "Jesus Marin and Ricard V. Sole",
  title =        "Evolutionary Optimization Through Extinction
                 Dynamics",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1344--1349",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{markose:2001:eafiof,
  author =       "Sheri Markose and Edward Tsang and Hakan Er and Abdel
                 Salhi",
  title =        "Evolutionary Arbitrage For {FTSE}-100 Index Options
                 and Futures",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "275--282",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, FGP, Machine
                 Discovery, Arbitrage, Options, Futures",
  ISBN =         "0-7803-6658-1",
  URL =          "http://privatewww.essex.ac.uk/~scher/EDDIE%20PROJ/TsangCEE2001.doc",
  abstract =     "The objective in this paper is to develop and
                 implement FGP-2 (Financial Genetic Programming) on
                 intra daily tick data for stock index options and
                 futures arbitrage in a manner that is suitable for
                 online trading when windows of profitable arbitrage
                 opportunities exist for short periods from one to ten
                 minutes. Our benchmark for FGP-2 is the textbook rule
                 for detecting arbitrage profits. This rule has the
                 drawback that it awaits a contemporaneous profitable
                 signal to implement an arbitrage in the same direction.
                 A novel methodology of randomised sampling is used to
                 train FGP-2 to pick up the fundamental arbitrage
                 patterns. Care is taken to fine tune weights in the
                 fitness function to enhance performance. As arbitrage
                 opportunities are few, missed opportunities can be as
                 costly as wrong recommendations to trade. Unlike
                 conventional genetic programs, FGP-2 has a constraint
                 satisfaction feature supplementing the fitness function
                 that enables the user to train the FGP to specify a
                 minimum and a maximum number of profitable arbitrage
                 opportunities that are being sought. Historical sample
                 data on arbitrage opportunities enables the user to set
                 these minimum and maximum bounds. Good FGP rules for
                 arbitrage are found to make a 3-fold improvement in
                 profitability over the textbook rule. This application
                 demonstrates the success of FGP-2 in its interactive
                 capacity that allows experts to channel their knowledge
                 into machine discovery",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@TechReport{markoso:2001:CFtr,
  author =       "Sheri M. Markose",
  title =        "The new evolutionary computational paradigm of complex
                 adaptive systems. Challenges and prospects for
                 economics and finance",
  institution =  "Department of Economics, University of Essex",
  year =         "2001",
  type =         "Discussion paper series",
  number =       "532",
  month =        jul,
  note =         "Forthcoming in Kluwer series on Computational Finance,
                 Jan 2002",
  email =        "scher@essex.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  size =         "53 pages",
}

@InProceedings{marks:1999:CGARDO,
  author =       "Robert E. Marks and David F. Midgley and Lee G. Cooper
                 and G. M. Shiraz",
  title =        "Coevolution with the Genetic Algorithm: Repeated
                 Differentiated Oligopolies",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1609--1615",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{marmelstein:1998:pchGPpdta,
  author =       "Robert E. Marmelstein and Gary B. Lamont",
  title =        "Pattern Classification using a Hybrid Genetic Program
                 Decision Tree Approach",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "223--231",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://en.afit.af.mil/hpc/students/rmarmels/gp98.ps.gz",
  size =         "9 pages",
  notes =        "GP-98. Pima Indians, Wisconsin Breast Cancer, SCUD
                 missile FLIR",
}

@InProceedings{marmelstein:1998:GRaCCE,
  author =       "Robert E. Marmelstein",
  title =        "{GR}a{CCE}: {A} Genetic Environment for Data Mining",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB, GP-98PhD Student Workshop",
}

@InProceedings{marmelstein:1998:ecdrs,
  author =       "Robert E. Marmelstein and Gary B. Lamont",
  title =        "Evolving Compact Decision Rule Sets",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming, GRaCCE",
  URL =          "http://en.afit.af.mil/hpc/students/rmarmels/gp98pp.ps.gz",
  size =         "7 pages",
  notes =        "GP-98LB",
}

@PhdThesis{marmelstein:thesis,
  author =       "Robert Evan Marmelstein",
  title =        "Evolving Compact Decision Rule Sets",
  school =       "Faculty of the Graduate School of Engineering of the
                 Air Force Institute of Technology Air University",
  year =         "1999",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, GRaCCE,
                 Matlab",
  URL =          "http://en.afit.af.mil/hpc/students/rmarmels/AFIT-DS-ENG-99-05.ps.gz",
  URL =          "http://en.afit.af.mil/hpc/students/rmarmels/AFIT-DS-ENG-99-05.pdf",
  size =         "271 pages",
  abstract =     "With the increased proliferation of computing
                 equipment, there has been a corresponding explosion in
                 the number and size of databases. Although a great deal
                 of time and effort is spent building and maintaining
                 these databases, it is nonetheless rare that this
                 valuable resource is exploited to its fullest. The
                 principle reason for this paradox is that many
                 organizations lack the insight and/or expertise to
                 effectively translate this information into usable
                 knowledge. While data mining technology holds the
                 promise of automatically extracting useful patterns
                 (such as decision rules) from data, this potential has
                 yet to be realized. One of the major technical
                 impediments is that the current generation of data
                 mining tools produce decision rule sets that are very
                 accurate, but extremely complex and difficult to
                 interpret. As a result, there is a clear need for
                 methods that yield decision rule sets that are both
                 accurate and compact.

                 The development of the Genetic Rule and Classifier
                 Construction Environment (GRaCCE) is proposed as an
                 alternative to existing decision rule induction (DRI)
                 algorithms. GRaCCE is a multi-phase algorithm which
                 harnesses the power of evolutionary search to mine
                 classification rules from data. These rules are based
                 on piece-wise linear estimates of the Bayes decision
                 boundary within a winnowed subset of the data. Once a
                 sufficient set of these hyper-planes are generated, a
                 genetic algorithm (GA) based {"}0/1{"} search is
                 performed to locate combinations of them that enclose
                 class homogeneous regions of the data. It is shown that
                 this approach enables GRaCCE to produce rule sets
                 significantly more compact than those of other DRI
                 methods while achieving a comparable level of accuracy.
                 Since the principle of Occam's razor tells us to always
                 prefer the simplest model that its the data, the rules
                 found by GRaCCE are of greater utility than those
                 identified by existing methods.",
  notes =        "AFIT/DS/ENG/99-05 Approved for public release;
                 distribution unlimited Appendix B. GRaCCE User's
                 Guide",
}

@Article{marney:2000:jasss,
  author =       "John Paul Marney and Heather F. E. Tarbert",
  title =        "Why do simulation? Towards a working epistemology for
                 practitioners of the dark arts",
  journal =      "Journal of Artificial Societies and Social
                 Simulation",
  year =         "2000",
  volume =       "3",
  number =       "3",
  keywords =     "genetic algorithms, genetic programming, reciprocal
                 altruism, group living, segmentation",
  URL =          "http://jasss.soc.surrey.ac.uk/3/4/4.html",
  abstract =     "The purpose of this paper is to argue for clarity of
                 methodology in social science simulation. Simulation is
                 now at a stage in the social sciences where it is
                 important to be clear why simulation should be used and
                 what it is intended to achieve. The paper goes on to
                 discuss a particularly important source of opposition
                 to simulation in the social sciences which arises from
                 perceived threats to the orthodox hard-core. This is
                 illustrated by way of a couple of case studies. The
                 paper then goes on to discuss defences to standard
                 criticisms of simulation and the various positive
                 reasons for using simulation in preference to other
                 methods of theorising in particular situations.",
  notes =        "GP mentioned as an example",
}

@InProceedings{marney:2000:CEF,
  author =       "John Paul Marney and Heather F. E. Tarbert and Colin
                 Fyfe",
  title =        "Technical Trading versus Market Efficiency-{A} Genetic
                 Programming Approach",
  booktitle =    "Computing in Economics and Finance",
  year =         "2000",
  address =      "Universitat Pompeu Fabra, Barcelona, Spain",
  month =        "6-8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In this paper genetic programming is used to
                 investigate a number of long time series of price data
                 for a stock exchange quoted share, in order to discern
                 whether there are any patterns in the data which could
                 be used for technical trading purposes. This extends
                 the work done by the authors in a previous paper (Fyfe
                 et al. 1999) which suggested that, although it was
                 possible to find a rule which did outperform simple buy
                 and hold, there were insufficient grounds for the
                 rejection of the efficient market hypothesis. The
                 purpose of the present paper is to investigate the
                 robustness and generalisability of the conclusion
                 reached by Fyfe et. al.",
  notes =        "http://enginy.upf.es/SCE/index2.html",
}

@InProceedings{marney:2001:SCE,
  author =       "John Paul Marney and D. Miller and Colin Fyfe and
                 Heather F. E. Tarbert",
  title =        "Risk Adjusted Returns to Technical Trading Rules: a
                 Genetic Programming Approach",
  booktitle =    "7th International Conference of Society of
                 Computational Economics",
  year =         "2001",
  address =      "Yale",
  month =        "28-29 " # jun,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.econ.yale.edu/sce01/confpage.html",
}

@InCollection{martens:2000:ACXDCSGP,
  author =       "Scott Martens",
  title =        "Automatic Creation of {XML} Document Conversion
                 Scripts by Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "269--278",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{Martin:1998:RDM,
  author =       "Lionel Martin and Frederic Moal and Christel Vrain",
  title =        "A Relational Data Mining Tool Based on Genetic
                 Programming",
  booktitle =    "Proceedings of the 2nd European Symposium on
                 Principles of Data Mining and Knowledge Discovery
                 ({PKDD}-98)",
  year =         "1998",
  editor =       "Jan M. {\.{Z}}ytkow and Mohamed Quafafou",
  volume =       "1510",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "130--138",
  address =      "Nantes, France",
  publisher_address = "Berlin",
  month =        "23--26 " # sep,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, data mining",
  notes =        "p134 Nice discussion of keeping data mining tree
                 queries valid under subtree crossover

                 ",
}

@InProceedings{martin:1999:DGP,
  author =       "Lionel Martin and Frederic Moal and Christel Vrain",
  title =        "Declarative expression of biases in Genetic
                 Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "401--408",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems, contex free grammars",
  ISBN =         "1-55860-611-4",
  abstract =     "context free grammars, data mining application, SQL",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Misc{Martin_1998_3062,
  author =       "Martin C. Martin",
  title =        "Breaking Out of the Black Box: {A} New Approach to
                 Robot Perception",
  school =       "Robotics Institute, Carnegie Mellon University",
  month =        jan,
  year =         "1998",
  address =      "Pittsburgh, PA, USA",
  note =         "Thesis proposal",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ri.cmu.edu/pub_files/pub2/martin_martin_c_1998_1/martin_martin_c_1998_1.pdf",
  URL =          "http://www.ri.cmu.edu/pub_files/pub2/martin_martin_c_1998_1/martin_martin_c_1998_1.ps.gz",
  size =         "28 pages",
  abstract =     "Surprisingly, the state of the art in avoiding
                 obstacles using only vision--not sonar or laser
                 rangefinders--is roughly half an hour between
                 collisions (at 30 cm/s, in an office environment).
                 After review ing the design and failure modes of
                 several current systems, I compare psychology's
                 understanding of perception to current computer/robot
                 perception. There are fundamental differences--which
                 lead to fundamental limitations with current computer
                 perception. The key difference is that robot software
                 is built out of {"}black boxes{"}, which have very
                 restricted interactions with each other. In contrast,
                 the human perceptual system is much more integrated.
                 The claim is that a robot that performs any significant
                 task, and does it as well as a person, can not be
                 created out of {"}black boxes.{"} In fact, it would
                 probably be too interconnected to be designed by
                 hand--instead, tools will be needed to create such
                 designs. To illustrate this idea, I propose to create a
                 visual obstacle avoidence system on the Uranus mobile
                 robot. The system uses a number of visual depth cues at
                 each pixel, as well as depth cues from neighbouring
                 pixels and previous depth estimates. Genetic
                 Programming is used to combine these into a new depth
                 estimate. The system learns by predicting both sonar
                 readings and the next image. The design of the system
                 is described, and design decisions are rationalized.",
}

@InProceedings{martin2:2001:gecco,
  title =        "Visual Obstacle Avoidance Using Genetic Programming:
                 First Results",
  author =       "Martin C. Martin",
  pages =        "1107--1113",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 robotics, Obstacle Avoidance, Computer Vision",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@MastersThesis{PeterMartin:masters,
  author =       "Peter Martin",
  title =        "An Investigation into the use of Genetic Programming
                 for Intelligent Network Service Creation",
  school =       "Bournemouth University",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://homepage.ntlworld.com/petemartin/dissertation_ps.zip",
  abstract =     "Service creation is crucial to the success of
                 Intelligent Networks (IN). However, the time required
                 to develop complex services is increasing. By reducing
                 the elapsed time needed to generate the service logic
                 and by reducing the opportunity for implementation
                 errors to appear in the service logic, a higher quality
                 IN service can be delivered. This project explores an
                 alternative method to the existing manual service
                 creation, by exploiting the properties of Genetic
                 Programming (GP). Genetic Programming is a powerful
                 method for evolving computer programs via the process
                 of natural selection. [Koz92]. The use of Genetic
                 Programming to produce service logic programs for IN is
                 analysed and a number of key features identified.
                 Principally for GP to be of benefit to IN it must be
                 able to reduce the time to create a service and reduce
                 the number of implementation errors in the resultant
                 program. Experimental evidence is presented that shows
                 that using Genetic Programming is a viable method for
                 service creation in Intelligent Networks, and can
                 reduce the time to create a program by several orders
                 of magnitude compared to a human. The case is also
                 argued that since GP needs a fitness function to be
                 developed, the initial specification should be of a
                 higher quality than one produced for a human
                 programmer, thereby reducing the number of errors in
                 the final program. To implement the experimental
                 prototype, existing methods of evolving complex systems
                 using GP were researched. A new method of ensuring the
                 property of closure is presented that does not
                 constrain the development of novel service logic
                 implementations, in contrast to existing methods
                 commonly employed in GP.",
}

@InProceedings{martin:2000:GPscin,
  author =       "Peter Martin",
  title =        "Genetic Programming for Service Creation in
                 Intelligent Networks",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "106--120",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  URL =          "http://homepage.ntlworld.com/petemartin/martin.ps",
  size =         "15 pages",
  abstract =     "Intelligent Networks are used by telephony systems to
                 offer services to customers. The creation of these
                 services has traditionally been performed by hand, and
                 has required substantial effort, despite the advanced
                 tools employed. An alternative to manual service
                 creation using Genetic Programming is proposed that
                 addresses some of the limitations of the manual process
                 of service creation. The main benefit of using GP is
                 that by focussing on what a service is required to do,
                 as opposed to its implementation, it is more likely
                 that the generated programs will be available on time
                 and to budget, when compared to traditional software
                 engineering techniques. The problem of closure is
                 tackled by presenting a new technique for ensuring
                 correct program syntax that maintains genetic
                 diversity.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{martin:2001:gecco,
  title =        "Building a Taxonomy of Genetic Programming",
  author =       "Peter Martin",
  pages =        "182",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 Taxonomy",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@Article{martin:2001:GPEM,
  author =       "Peter Martin",
  title =        "A Hardware Implementation of a Genetic Programming
                 System Using {FPGAs} and {Handel-C}",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "4",
  pages =        "317--343",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware, FPGA, Handel-C, parallel genetic algorithm",
  ISSN =         "1389-2576",
  abstract =     "This paper presents an implementation of Genetic
                 Programming using a Field Programmable Gate Array. This
                 novel implementation uses a high level language to
                 hardware compilation system, called Handel-C, to
                 produce a Field Programmable Logic Array capable of
                 performing all the functions required of a Genetic
                 Programming System. Two simple test problems
                 demonstrate that GP running on a Field Programmable
                 Gate Array can outperform a software version of the
                 same algorithm by exploiting the intrinsic parallelism
                 available using hardware, and the geometric
                 parallelisation of Genetic Programming.",
  notes =        "Xilinx BG560 FPGA XCV2000e, Celoxica RC1000 FPGA
                 board. See also martin:2002:EuroGP",
}

@InProceedings{martin:2002:EuroGP,
  title =        "A Pipelined Hardware Implementation of Genetic
                 Programming using {FPGA}s and {Handel-C}",
  author =       "Peter Martin",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "1--12",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "A complete Genetic Programming (GP) system implemented
                 in a single FPGA is described in this paper. The GP
                 system is capable of solving problems that require
                 large populations and by using parallel fitness
                 evaluations can solve problems in a much shorter time
                 that a conventional GP system in software. A high level
                 language to hardware compilation system called Handel-C
                 is used for implementation.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{martin2:2002:gecco,
  author =       "Peter Martin and Riccardo Poli",
  title =        "Crossover Operators For {A} Hardware Implementation Of
                 {GP} Using {FPGAs} And {H}andel-{C}",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "845--852",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{martin:2002:gecco,
  author =       "Peter Martin",
  title =        "An Analysis Of Random Number Generators For {A}
                 Hardware Implementation Of Genetic Programming Using
                 {FPGAs} And {H}andel-{C}",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "837--844",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InCollection{kinnear:masand,
  author =       "Brij Masand",
  institution =  "Thinking Machines Corporation",
  title =        "Optimising Confidence of Text Classification by
                 Evolution of Symbolic Expressions",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "445--458",
  chapter =      "21",
  notes =        "Presented at Genetic Programming Workshop of ICGA-93",
  notes =        "Classification of New Stories, Very simple formulae
                 evolved which do better than existing human attempts at
                 automatic coding. Automatic results comparable to human
                 success rates",
  keywords =     "genetic algorithms, genetic programming",
  size =         "13 pages",
}

@InCollection{masand:1996:aigp2,
  author =       "Brij Masand and Gregory Piatesky-Shapiro",
  title =        "Discovering Time Oriented Abstractions in Historical
                 Data to Optimize Decision Tree Classification",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "489--498",
  chapter =      "24",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
}

@InCollection{masonis:1999:VPCE,
  author =       "J. Todd Masonis",
  title =        "Valve Paradigm {"}{C}{"} Code Evolution",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "140--146",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@TechReport{mataric:1995:cecprTR,
  author =       "Maja Mataric and Dave Cliff",
  title =        "Challenges in Evolving Controllers for Physical
                 Robots",
  institution =  "Computer Science Department, Brandeis University",
  year =         "1995",
  number =       "CS-95-184",
  keywords =     "genetic algorithms, genetic programming, robots",
  URL =          "http://www.cogs.susx.ac.uk/users/davec/brandeis_CS_95_184.ps.Z",
  abstract =     "Feasibility of applying evolutionary methods to
                 automatically generating controllers for physical
                 mobile robots. Overview state of the art, main
                 approaches, key challenges, unanswered problems,
                 promising directions",
  notes =        "GP and other approaches surveyed",
  size =         "34 pages",
}

@Article{mataric:1995:cecpr,
  author =       "Maja J. Mataric and Dave Cliff",
  title =        "Challenges in Evolving Controllers for Physical
                 Robots",
  journal =      "Journal of Robotics and Autonomous Systems",
  year =         "1996",
  volume =       "19",
  number =       "1",
  pages =        "67--83",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, robots",
  abstract =     "Feasibility of applying evolutionary methods to
                 automatically generating controllers for physical
                 mobile robots. Overview state of the art, main
                 approaches, key challenges, unanswered problems,
                 promising directions",
  notes =        "GP and other approaches surveyed",
  notes =        "see also mataric:1995:cecprTR

                 ",
}

@InProceedings{mattfeld:1999:SSSSP,
  author =       "Dirk C. Mattfeld",
  title =        "Scalable Search Spaces for Scheduling Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1616--1621",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{matthews:2001:idm,
  author =       "Robert Matthews",
  title =        "The Ideas Machine",
  journal =      "New Scientist",
  year =         "2001",
  number =       "2274",
  pages =        "26--29",
  month =        "20 " # jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.newscientist.com/",
  size =         "4 pages",
  notes =        "{"}Computers that invent the future{"} cover.

                 {"}Human inventiveness has reached the end of the road.
                 Something far smarter is about to take over, says
                 Robert Matthews{"}. page 26

                 Glossy overview of GA, GP. Concentrates upon John
                 Koza's work on using GP to {"}invent{"} designs and
                 patent infringement.",
}

@InProceedings{mautner:1999:CMCSR,
  author =       "Craig Mautner and Richard K. Belew",
  title =        "Coupling Morphology and Control in a Simulated Robot",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1350--1357",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{icec94:maxwell,
  author =       "Sidney R. {Maxwell III}",
  title =        "Experiments with a Coroutine Model for Genetic
                 Programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence, Orlando, Florida, USA",
  year =         "1994",
  pages =        "413--417a",
  volume =       "1",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  organisation = "IEEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, parallel
                 programming, subroutines, iterative methods, coroutine
                 execution model, synchronous parallel program
                 execution, fitness comparison, execution time limits,
                 iteration limits, infinite loops, infinite recursion,
                 evolutionary progress, population tolerance",
  ISBN =         "0-7803-1899-4",
  size =         "6 pages",
  URL =          "http://ieeexplore.ieee.org/iel2/1125/8059/00349915.pdf?isNumber=8059",
  abstract =     "The genetic programming methodology is expanded with a
                 coroutine model for the synchronous, parallel execution
                 of the individual programs in the population. For
                 certain classes of problem, namely those that support
                 fitness comparison between individuals which are in a
                 state of execution, this model allows the removal of
                 execution time and iteration limits. Populations can
                 then tolerate individuals with infinite loops (or in a
                 suitable environment, infinite recursion), while still
                 allowing evolutionary progress.",
  notes =        "Earlier version 11 pages available electronically. See
                 genetic-programming mailing list 14/12/93, 4/1/94 and
                 5/1/94

                 coroutine _model_ is described in terms of real program
                 runtimes. Actually achieved by defining psuedo elapse
                 time for each instruction (which is zero in some cases)
                 and interrupting execution of the program after a
                 certain number of these timesteps. Makes things
                 controlable.

                 Run on Artificial Ant Santa Fe Trail and claims better
                 programs produced with less effort than Koza
                 (GP1).

                 Steady state pop of 1000, with 100 new individuals per
                 cycle. Limit of 600 ticks (when comparing with
                 koza:book) Faster programs preferred. {"}The coroutine
                 model found individuals which were more efficient
                 (faster?) in solving the problem than the generational
                 model{"} p417

                 Date: Mon, 24 Apr 2000 09:36:34 -0700 From: {"}Sidney R
                 Maxwell III{"} > 1-How did Maxwell implement his
                 method?

                 Basically, I executed each individual a fixed number of
                 steps (a 'configurable' number N, with a value of as
                 little as1). Individuals added to the population were
                 pre-executed an appropraite number of steps to ensure
                 that all individuals in the population had executed the
                 same number of steps.

                 The problem that I was tackling was the Artificial Ant,
                 for which evaluating fitness on partially executed
                 individuals was meaningful.

                 In early experiments, I executed all individuals in the
                 population N steps. Later, as a run-time performance
                 enhancement, I [simply] ensured that individuals being
                 evaluated had executed the same number of steps before
                 comparing their fitness.

                 ",
}

@InProceedings{maxwell:1996:why,
  author =       "S. R. Maxwell",
  title =        "Why Might Some Problems Be Difficult for Genetic
                 Programming to Find Solutions?",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "125--128",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB mutation operator swaps order of agruments of
                 binary non-commutative functions The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@InProceedings{maxwell:1999:THMMPL,
  author =       "Bruce Maxwell and Sven Anderson",
  title =        "Training Hidden Markov Models using Population-Based
                 Learning",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "944",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming,
                 poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{may:1999:EECTHGP,
  author =       "Damon May",
  title =        "Evolution of Effective Communication Techniques for
                 Hunting using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "147--154",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@Unpublished{mayer:1997:ptga,
  author =       "Helmut A. Mayer",
  title =        "pt{GA}s - Genetics algorithms using promoter/teminator
                 sequences",
  note =         "Position paper at the Workshop on Exploring Non-coding
                 Segments and Genetics-based Encodings at ICGA-97",
  month =        "21 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, introns",
  URL =          "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/helmut.ps",
  notes =        "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/",
  size =         "3 pages",
}

@Article{mayer:1998:ptga,
  author =       "Helmut A. Mayer",
  title =        "pt{GA}s--Genetic Algorithms Evolving Noncoding
                 Segments by Means of Promoter/Terminator Sequences",
  journal =      "Evolutionary Computation",
  year =         "1998",
  volume =       "6",
  number =       "4",
  pages =        "361--386",
  month =        "Winter",
  keywords =     "genetic algorithms, chromosome structures,
                 promoter/terminator sequences, noncoding segments,
                 spontaneous crossover, combinatorial optimization.",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=6&issue=4",
  abstract =     "In this article we present work on chromosome
                 structures for genetic algorithms (GAs) based on
                 biological principles. Mainly, the influence of
                 noncoding segments on GA behavior and performance is
                 investigated. We compare representations with noncoding
                 sequences at predefined, fixed locations with
                 {"}junk{"} code induced by the use of
                 promoter/terminator sequences (ptGAs) that define start
                 and end of a coding sequence, respectively. As one of
                 the advantages of noncoding segments a few researchers
                 have identified the reduction of the disruptive effects
                 of crossover, and we solidify this argument by a formal
                 analysis of crossover disruption probabilities for
                 noncoding segments at fixed locations. The additional
                 use of promoter/terminator sequences not only enables
                 evolution of parameter values, but also allows for
                 adaptation of number, size, and location of genes
                 (problem parameters) on an artificial chromosome.
                 Randomly generated chromosomes of fixed length carry
                 different numbers of promoter/terminator sequences
                 resulting in genes of varying size and location.
                 Evolution of these ptGA chromosomes drives the number
                 of parameters and their values to (sub)optimal
                 solutions. Moreover, the formation of tightly linked
                 building blocks is enhanced by self-organization of
                 gene locations. We also introduce a new, nondisruptive
                 crossover operator emerging from the ptGA gene
                 structure with adaptive crossover rate, location, and
                 number of crossover sites. For experimental comparisons
                 of this genetic operator to conventional crossover in
                 GAs, as well as properties of different ptGA chromosome
                 structures, an artificial problem from the literature
                 is used. Finally, the potential of ptGA is demonstrated
                 on an NP-complete combinatorial optimization problem.",
  notes =        "Special Issue: Variable-Length Representation and
                 Noncoding Segments for Evolutionary Algorithms Edited
                 by Annie S. Wu and Wolfgang Banzhaf",
}

@InCollection{mayer:1999:GCGAAEGC,
  author =       "Marissa A. Mayer",
  title =        "Graph Coloring using Genetic Algorithms: An
                 Exploration of Genetic Clustering",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "155--163",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{McConaghy:1998:GPlsfpmpts,
  author =       "Trent McConaghy and Henry Leung",
  title =        "Genetic Programming with Least Squares for Fast,
                 Precise Modeling of Polynomial Time Series",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InCollection{McConnell:1997:msGA,
  author =       "K. John McConnell",
  title =        "An Attempt to Determine Molecular Structure via
                 Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "138--146",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms",
  ISBN =         "0-18-205981-2",
  abstract =     "poor performamce of this algorithm suggest it is not
                 an effective technique for accurately estimateing
                 structure",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{mckay:1995:tsgasr,
  author =       "Ben McKay and Mark J. Willis and Geoffrey W. Barton",
  title =        "Using a Tree Structured Genetic Algorithm to Perform
                 Symbolic Regression",
  booktitle =    "First International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1995",
  editor =       "A. M. S. Zalzala",
  volume =       "414",
  pages =        "487--492",
  address =      "Sheffield, UK",
  publisher_address = "London, UK",
  month =        "12-14 " # sep,
  publisher =    "IEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-85296-650-4",
  abstract =     "Three examples demonstrate the applicability of the
                 technique within the domain of process engineering",
  notes =        "12--14 September 1995, Halifax Hall, University of
                 Sheffield, UK see also
                 http://www.iee.org.uk/LSboard/Conf/program/galprog.htm

                 Uses correletion coefficient in fitness function as
                 advocated by M. C. South Phd 1994 {"}The application of
                 GAs to rule finding in data analysis{"}, Newcastle upon
                 Tyne, UK

                 Final fixup? {"}Mutation...replaces a node in the tree
                 with another of the same degree{"}. Elistist. Popsize
                 20, G=100, Pcross=0.8 Pmut=0.5 {"}found to give good
                 performance to date{"}

                 {"}non linear least-squares optimization to obtain
                 'best' value of the (new) constant(s) in the
                 expression{"}. {"}the fitness of a tree is wieghted
                 according to its size{"} (penalise bigger)

                 2nd example {"}Near Infra-red reflectance instrument
                 for the inference of the protien contents of ground
                 wheat{"} (old data, (1983, T.Fearn {"}A misuse of ridge
                 regression in the clibration of near infrared
                 relectance instrument{"}, Appl Statistics, 32, 1,
                 73-79), various techniques already tried). GP
                 {"}provide simple non-linear model that provides far
                 greater insight into the input-output model structure
                 than other non-lenear modelling techniques such as
                 neural networks{"} RMS error also better than cited in
                 literature (traditional stats and ANN).

                 3rd: recovery of contaminated transformer oil. GP
                 solution robust to measurement error.",
}

@InProceedings{mckay:1995:cps,
  author =       "Ben McKay and Mark J. Willis and Geoffrey W. Barton",
  title =        "On the Application of Genetic Programming to Chemical
                 Process Systems",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "701--706",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In this contribution a genetic programming approach is
                 used to develop mathematical models of chemical process
                 systems. Having discussed genetic programming in
                 general, two examples are used to reveal the utility of
                 the technique. It is shown how the method can
                 discriminate between relevant and irrelevant process
                 inputs, evolving to yield parsimonious model structures
                 that accurately represent process characteristics. This
                 removes the need for restrictive assumptions about the
                 form of the data and the structure of the required
                 model. In addition, as the technique determines complex
                 nonlinear relationships in the data, non-intuitive
                 process features are revealed with comparative ease.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html",
}

@InProceedings{mckay:1996:GPidea,
  author =       "Ben McKay and Mark Willis and Gary Montague and
                 Geoffrey W. Barton",
  title =        "Using Genetic Programming to Develop Inferential
                 Estimation Algorithms",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "157--165",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper2.ps",
  size =         "9 pages",
  abstract =     "Genetic Programming (GP) is used to develop
                 inferential estimation algorithms for two industrial
                 chemical processes. Within this context, dynamic
                 modelling procedures (as opposed to static or
                 steady-state modelling) are often required if accurate
                 inferential models are to be developed. Thus, a simple
                 procedure is suggested so that the GP technique may be
                 used for the development of dynamic process models.
                 Using measurements from a vacuum distillation column
                 and an industrial plasticating extrusion process, it is
                 then demonstrated how the GP methodology can be used to
                 develop reliable ‘cost’ effective process models. A
                 statistical analysis procedure is used to aid in the
                 assessment of GP algorithm settings and to guide in the
                 selection of the final model structure.",
  notes =        "GP-96, MSWord postscript not cmpatible with Unix",
}

@TechReport{mckay:1996:ehmffp,
  author =       "B. McKay and C. Sanderson and M. J. Willis and J.
                 Barford and G. Barton",
  title =        "Evolving a Hybrid Model of a Fed-batch Fermentation
                 Process",
  institution =  "Chemical Engineering, Newcastle University",
  year =         "1996",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper6.ps",
  abstract =     "This paper presents a novel method for the system
                 identification of the fed-batch fermentation process
                 defined in the problem statement of the
                 Biotechnological Control Forum Modelling and Control
                 Competition. The identification methodology involves a
                 hybrid of mechanistic modelling and Genetic Programming
                 techniques. It provides an accurate model of the system
                 which should be extremely useful in both the
                 optimisation and control of this process. The
                 performance of the model as a 25 time unit ahead
                 predictor of product concentration (on unseen
                 verification data) is such that the root mean square
                 error between the actual and predicted output is less
                 than 5% over the range of interest.",
  notes =        "MSword postscript not camptible with unix",
  size =         "12 pages",
}

@TechReport{mckay:1996:cmc2p,
  author =       "B. McKay and B. Lennox and M. J. Willis and G. W.
                 Barton and G. A. Montague",
  title =        "Extruder Modelling: {A} Comparison of two Paradigms",
  institution =  "Chemical Engineering, Newcastle University",
  year =         "1996",
  address =      "UK",
  note =         "Appears in Control '96",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper5.ps",
  abstract =     "In this contribution two data based modelling
                 paradigms are compared. Using measurements from an
                 industrial plasticating extrusion process, a locally
                 recurrent neural network and a genetic programming
                 algorithm are used to develop inferential models of the
                 polymer viscosity. It is demonstrated that both
                 techniques produce adequate non-linear dynamic
                 inferential models. However, for this application the
                 genetic programming technique adopted produces models
                 that perform better than the locally recurrent neural
                 network. Moreover, the final model produced by the
                 algorithm has a simple transparent structure.",
  notes =        "MSword postscript not compatible with unix, see also
                 mckay:1996:exmc2p",
  size =         "6 pages",
}

@InProceedings{mckay:1996:iipGP,
  author =       "B. McKay and M. J. Willis and H. G. Hiden and G. A.
                 Montague and G. W. Barton",
  title =        "Identification of Industrial Processes using Genetic
                 Programming",
  booktitle =    "Identification in Engineering Systems",
  year =         "1996",
  volume =       "1",
  address =      "Swansea, UK",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper4.ps",
  size =         "10 pages",
  abstract =     "Complex processes are often modelled using
                 input-output data from experimental tests. Regression
                 and neural network modelling techniques address this
                 problem to some extent and are being increasingly used
                 to develop optimisation or model-based control
                 algorithms. Unfortunately, the latter methods provide
                 no physical insight into the underlying structural
                 relationships inherent within the data. Genetic
                 Programming (GP) is currently finding application in
                 the modelling of processes from experimental data. The
                 nature of GP-based modelling is that solutions are
                 ‘evolved’ from a set of potential solutions in an
                 environment which mimics Darwinian {"}survival of the
                 fittest{"}. GP performs symbolic regression,
                 determining both the structure and the complexity of
                 the model during its evolution. In this contribution
                 two examples are used to demonstrate the utility of the
                 GP technique as a process modelling tool. It is
                 concluded that GP techniques may have further
                 applications in the modelling and identification of
                 complex processes from experimental input-output
                 data.",
  notes =        "MSWord postscript not compatible with unix",
}

@InProceedings{mckay:1996:eiocps,
  author =       "Ben McKay and Justin Elsey and Mark J. Willis and
                 Geoffrey W. Barton",
  title =        "Evolving Input-Output Models of Chemical Process
                 Systems Using Genetic Programming",
  booktitle =    "IFAC '96",
  year =         "1996",
  volume =       "1",
  address =      "San-Fransisco",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper3.ps",
  size =         "7 pages",
  abstract =     "Complex processes are often modelled using
                 input-output data from experimental tests. Regression
                 and neural network modelling techniques are commonly
                 used for this purpose. Unfortunately, these methods
                 provide minimal structural insight into process
                 characteristics. In this contribution, we propose the
                 use of Genetic Programming (GP) as a method for
                 developing input-output process models from
                 experimental data. GP performs symbolic regression,
                 determining both the structure and the complexity of
                 the model during its evolution. This has the advantage
                 that no a priori modelling assumptions have to be made.
                 Moreover, the technique can discriminate between
                 relevant and irrelevant process inputs, yielding
                 parsimonious model structures that accurately represent
                 process characteristics. Two examples are used to
                 demonstrate the utility of the GP technique as a
                 process modelling tool.",
  notes =        "MSWord postscript not compatible with unix",
}

@Article{mckay:1996:ssmcps,
  author =       "Ben McKay and Mark Willis and Geoffrey Barton",
  title =        "Steady-state Modelling of Chemical Process System
                 using Genetic Programming",
  journal =      "Computers and Chemical Engineering",
  year =         "1997",
  volume =       "21",
  number =       "9",
  pages =        "981--996",
  keywords =     "genetic algorithms, genetic programming",
  size =         "16 pages",
  abstract =     "Complex processes are often modelled using
                 input-output data from experimental tests. Regression
                 and neural network modelling techniques are commonly
                 used for this purpose. Unfortunately, these methods
                 provide minimal information about the model structure
                 required to accurately represent process
                 characteristics. In this contribution, we propose the
                 use of Genetic Programming (GP) as a method for
                 developing input-output process models from
                 experimental data. GP performs symbolic regression,
                 determining both the structure and the complexity of
                 the model during its evolution. This has the advantage
                 that no a priori modelling assumptions have to be made.
                 Moreover, the technique can discriminate between
                 relevant and irrelevant process inputs, yielding
                 parsimonious model structures that accurately represent
                 process characteristics. Following a tutorial example,
                 the usefulness of the technique is demonstrated by the
                 development of steady-state models for two typical
                 processes, a vacuum distillation column and a chemical
                 reactor system. A statistical analysis procedure is
                 used to aid in the assessment of GP algorithm settings
                 and to guide in the selection of the final model
                 structure.",
}

@InProceedings{mckay:1996:exmc2p,
  author =       "Ben McKay and Barry Lennox and Mark Willis and
                 Geoffrey W. Barton and Gary Montague",
  title =        "Extruder Modelling: {A} Comparison of two Paradigms",
  booktitle =    "UKACC International Connference on Control'96",
  year =         "1996",
  volume =       "2",
  pages =        "734--739",
  address =      "Exeter, UK",
  publisher_address = "Savoy House, London, UK",
  month =        "2-5 " # sep,
  publisher =    "IEE",
  note =         "Conference publication No. 427",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  abstract =     "In this contribution two data based modelling
                 paradigms are compared. Using measurements from an
                 industrial plasticating extrusion process, a locally
                 recurrent neural network and a genetic programming
                 algorithm are used to develop inferential models of the
                 polymer viscosity. It is demonstrated that both
                 techniques produce adequate non-linear dynamic
                 inferential models. However, for this application the
                 genetic programming technique adopted produces models
                 that perform better than the locally recurrent neural
                 network. Moreover, the final model produced by the
                 algorithm has a simple transparent structure.",
  notes =        "see also tech report mckay:1996:cmc2p",
}

@InProceedings{mckay:1999:NCRUGP,
  author =       "Ben McKay and Mark Willis and Dominic Searson and Gary
                 Montague",
  title =        "Non-Linear Continuum Regression Using Genetic
                 Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1106--1111",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{McKay:2000:GECCO,
  author =       "R I (Bob) McKay",
  title =        "Fitness Sharing in Genetic Programming",
  pages =        "435--442",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{mckay:2000:pffgp,
  author =       "Bob McKay",
  title =        "Partial Functions in Fitness-Shared Genetic
                 Programming",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "349--356",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, fitness",
  ISBN =         "0-7803-6375-2",
  abstract =     "This paper investigates the use of partial functions
                 and fitness sharing in genetic programming. Fitness
                 sharing is applied to populations of either partial or
                 total functions and the results compared. Applications
                 to two classes of problem are investigated: learning
                 multiplexer definitions, and learning (recursive) list
                 membership functions. In both cases, fitness sharing
                 approaches outperform the use of raw fitness, by
                 generating more accurate solutions with the same
                 population parameters. On the list membership problem,
                 variants using fitness sharing on populations of
                 partial functions outperform variants using total
                 functions, whereas populations of total functions give
                 better performance on some variants of multiplexer
                 problems.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@Article{McKay:2001:EM,
  author =       "R. I. (Bob) McKay",
  title =        "Variants of genetic programming for species
                 distribution modelling -- fitness sharing, partial
                 functions, population evaluation",
  year =         "2001",
  journal =      "Ecological Modelling",
  volume =       "146",
  pages =        "231--241",
  number =       "1-3",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6VBS-44HYNCP-N/1/a4ef72e29b6f89efd2ddb1b22258ef06",
  abstract =     "We investigate the use of partial functions, fitness
                 sharing and committee learning in genetic programming.
                 The primary intended application of the work is in
                 learning spatial relationships for ecological
                 modelling. The approaches are evaluated using a
                 well-studied ecological modelling problem, the greater
                 glider population density problem. Combinations of the
                 three treatments (partial functions, fitness sharing
                 and committee learning) are compared on the dimensions
                 of accuracy and computational cost. Fitness sharing
                 significantly improves learning accuracy, and
                 populations of partial functions substantially reduce
                 computational cost. The results of committee learning
                 are more equivocal, and require further investigation.
                 The learned models are highly predictive, but also
                 highly explanatory.",
}

@InCollection{mcmilin:2000:AIDSA,
  author =       "Emily McMilin",
  title =        "Adaptation of Internet Data Sending Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "279--285",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InCollection{mcnames:1994:fnnGP,
  author =       "James {McNames}",
  title =        "Faster Neural Network Architectures from Genetic
                 Algorithms",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "108--117",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InCollection{mcnutt:1997:crrc,
  author =       "Greg McNutt",
  title =        "Using Co-Evolution to Produce Robust Robot Control",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "159--167",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "mobile robots navigate test course, coures evolve
                 vased upon ability to cause robots to crash",
  notes =        "part of koza:1997:GAGPs simulation coevolution
                 effective DGPC

                 ",
}

@InProceedings{mcnutt:1997:crrcLB,
  author =       "Greg McNutt",
  title =        "Using Co-Evolution to Produce Robust Control",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "141--149",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{McPhee:1995:acrep,
  author =       "Nicholas Freitag McPhee and Justin Darwin Miller",
  title =        "Accurate Replication in Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "303--309",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms, bloat",
  ISBN =         "1-55860-370-0",
  size =         "7 pages",
  abstract =     "Presents theoretical analysis that, in some cases, the
                 preasure for acurate replication (ie for children to be
                 as fit as their parents) induces and increase in size.
                 INC-IGNORE, INC, (PLUS-IGNORE, PLUS, INC_DEC and
                 INC-ID) problems.

                 Claims presence of large semanticall inert subtrees
                 inhits discovery of solution but once found they help
                 population to converge to this solution. Suggests
                 {"}one should avoid function sets which can easily be
                 manipulated to build semantically irrelevant
                 subtrees{"}.",
}

@InProceedings{mcphee:1998:itetpGP,
  author =       "Nicholas Freitag McPhee and Nicholas J. Hopper and
                 Mitchell L. Reierson",
  title =        "Impact of types on essentially typeless problems in
                 {GP}",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "232--240",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{mcphee:1998:sutherland,
  author =       "Nicholas Freitag McPhee and Nicholas J. Hopper and
                 Mitchell L. Reierson",
  title =        "Sutherland: An extensible object-oriented software
                 framework for evolutionary computation",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "241",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{mcphee:1999:A,
  author =       "Nicholas Freitag McPhee and Nicholas J. Hopper",
  title =        "Analysis of genetic diversity through population
                 history",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1112--1120",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{mcphee:1999:AAASRG,
  author =       "Nicholas Freitag McPhee and Nicholas J. Hopper",
  title =        "App{GP}: An Alternative Structural Representation for
                 {GP}",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1377--1383",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, models of
                 evolutionary computation",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@TechReport{McPhee00-22,
  author =       "Nicholas Freitag McPhee and Riccardo Poli",
  title =        "A schema theory analysis of the evolution of size in
                 genetic programming with linear representations",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-00-22",
  month =        nov,
  year =         "2000",
  email =        "N.F.McPhee@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/2000/CSRP-00-22.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-22.ps.gz",
  abstract =     "In this paper we use the schema theory presented in
                 [Poli and McPhee, 2000] to better understand the
                 changes in size distribution when using GP with
                 standard crossover and linear structures. Applications
                 of the theory to problems both with and without fitness
                 suggest that standard crossover induces specific biases
                 in the distributions of sizes, with a strong tendency
                 to over sample small structures, and indicate
                 the

                 existence of strong redistribution effects that may be
                 a major force in the early stages of a GP run. We also
                 present two important theoretical results: An exact
                 theory of bloat, and a general theory of how average
                 size changes on flat landscapes with glitches. The
                 latter implies the surprising result that a single
                 program glitch in an otherwise flat fitness landscape
                 is sufficient to drive the average program size of an
                 infinite population, which may have

                 important implications for the control of code
                 growth.",
  notes =        "published as mcphee:2001:EuroGP",
}

@TechReport{McPhee00-24,
  author =       "Nicholas Freitag McPhee and Riccardo Poli and Jon E
                 Rowe",
  title =        "A schema theory analysis of mutation size biases in
                 genetic programming with linear representations",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-00-24",
  month =        nov,
  year =         "2000",
  email =        "N.F.McPhee@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk
                 N.F.McPhee@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/2000/CSRP-00-24.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-24.ps.gz",
  abstract =     "In recent work we showed how developments in GP schema
                 theory can be used to better understand the biases
                 induced by the standard subtree crossover when genetic
                 programming is applied to variable length linear
                 structures. In this paper we use the schema theory to
                 better understand the biases induced on linear
                 structures by two common GP subtree mutation operators:
                 FULL and GROW mutation. In both cases we find that the
                 operators do have quite specific biases and typically
                 strongly oversample shorter strings.",
}

@InProceedings{mcphee:2001:EuroGP,
  author =       "Nicholas Freitag McPhee and Riccardo Poli",
  title =        "A schema theory analysis of the evolution of size in
                 genetic programming with linear representations",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "108--125",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Schema
                 theory, Linear representations, Bloat, Length
                 distributions, Fitness landscape glitches,
                 One-then-zeros problem",
  ISBN =         "3-540-41899-7",
  size =         "18 pages",
  abstract =     "In this paper we use the schema theory presented
                 elsewhere in this volume to better understand the
                 changes in size distribution when using GP with
                 standard crossover and linear structures. Applications
                 of the theory to problems both with and without fitness
                 suggest that standard crossover induces specific biases
                 in the distributions of sizes, with a strong tendency
                 to over sample small structures, and indicate the
                 existence of strong redistribution effects that may be
                 a major force in the early stages of a GP run. We also
                 present two important theoretical results: An exact
                 theory of bloat, and a general theory of how average
                 size changes on flat landscapes with glitches. The
                 latter implies the surprising result that a single
                 program glitch in an otherwise flat fitness landscape
                 is sufficient to drive the average program size of an
                 infinite population, which may have important
                 implications for the control of code growth.",
  notes =        "EuroGP'2001, part of miller:2001:gp Update of
                 McPhee00-22",
}

@InProceedings{mcphee:2001:astamsbgplr,
  author =       "Nicholas Freitag McPhee and Riccardo Poli and Jonathan
                 E. Rowe",
  title =        "A Schema Theory Analysis of Mutation Size Biases in
                 Genetic Programming with Linear Representations",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1078--1085",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, schema
                 theory, mutation, linear representation, size bias",
  ISBN =         "0-7803-6658-1",
  abstract =     "typically strongly oversample shorter strings",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 linear (unary) tree schemata. flat fitness landscape.
                 biases of full mutation, grow mutation,

                 No fitness. Full(unary) average length = 2*D-1.
                 Limiting size distribution: 0 for size < D, flat region
                 size < 2D, rapid falling size>=2D. Similar to subtree
                 crossover. Grow(unary) discrete gamma distribution (cf.
                 Rowe01 ) cf subtree crossover.

                 {"}ones then zeros{"} unary problem. Subtree crossover
                 bloat (at least to 75 generations). full no bloat,
                 actually as with no fitness, {"}artifact of this
                 particular problem{"}. Grow similar to no fitness.",
}

@InProceedings{mcphee:2002:gecco,
  author =       "Nicholas Freitag {McPhee} and Riccardo Poli",
  title =        "Using Schema Theory To Explore Interactions Of
                 Multiple Operators",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "853--860",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, operator
                 bias, operator interaction, operator proportion, schema
                 theory, variable length linear structures",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{meeden:1998:bgrsrimsn,
  author =       "Lisa Meeden",
  title =        "Bridging the gap between robot simulations and reality
                 with improved models of sensor noise",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "824--831",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Evolutionary Robotics",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{Mendes:2001:PKDD,
  author =       "Roberto R. F. Mendes and Fabricio B. Voznika and Alex
                 A. Freitas and Julio C. Nievola",
  title =        "Discovering Fuzzy Classification Rules with Genetic
                 Programming and Co-Evolution",
  booktitle =    "5th European Conference on Principles and Practice of
                 Knowledge Discovery in Databases (PKDD'01)",
  year =         "2001",
  editor =       "L. {de Raedt} and Arno Siebes",
  volume =       "2168",
  series =       "LNAI",
  pages =        "314--325",
  address =      "Freiburg, Germany",
  month =        "3-7 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 classification",
  abstract =     "In essence, data mining consists of extracting
                 knowledge from data. This paper proposes a
                 co-evolutionary system for discovering fuzzy
                 classification rules. The system uses two evolutionary
                 algorithms: a genetic programming (GP) algorithm
                 evolving a population of fuzzy rule sets and a simple
                 evolutionary algorithm evolving a population of of
                 membership function definitions. The two populations
                 co-evolve, so that the final result of the
                 co-evolutionary process is a fuzzy rule set and a set
                 of membership function definitions which are well
                 adapted to each other. In addition, our system also has
                 some innovative ideas with respect to the encoding of
                 GP individuals representing rule sets. The basic idea
                 is that our individual encoding scheme incorporates
                 several syntactical restrictions that facilitate the
                 handling of rule sets in disjunctive normal form. We
                 have also adapted GP operators to better work with the
                 proposed individual encoding scheme.",
  notes =        "http://www.informatik.uni-freiburg.de/~ml/ecmlpkdd/index.html
                 PKDD-2001
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42534-9",
}

@InProceedings{mendes:2001:gecco,
  title =        "Discovering Fuzzy Classification Rules with Genetic
                 Programming and Co-Evolution",
  author =       "Roberto R. F. Mendes and Fabricio de B. Voznika and
                 Julio C. Nievola and Alex A. Freitas",
  pages =        "183",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 evolution strategy, co-evolution, data mining,
                 classification, fuzzy rules",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{mendes:2001:dfcrgpc,
  author =       "Roberto R. F. Mendes and Fabricio {de B. Voznika} and
                 Julio C. Nievola and Alex A. Freitas",
  title =        "Discovering Fuzzy Classification Rules with Genetic
                 Programming and Co-Evolution",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "287--294",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, CEFR-MINER",
  notes =        "GECCO-2001LB Coevolution of two populations: GP
                 population of fuzzy rule sets. Simple evolutionary
                 algorithm evolving a population of membership function
                 definitions. cf: delgado:1999:MHEDFS",
}

@InProceedings{mengshoel:1998:dubn,
  author =       "Ole J. Mengshoel and Daniel E. Goldberg and David C.
                 Wilkins",
  title =        "Deceptive and Other Functions of Unitation as Bayesian
                 Networks",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "559--566",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{mengshoel:1998:ecbn,
  author =       "Ole Mengshoel",
  title =        "Evolutionary Computation in Bayesian Networks",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-kbs.ai.uiuc.edu/kbs-publications/pub-173/gp98-abstract.ps",
  abstract =     "This abstract discusses issues in using genetic
                 algorithms for computing the most probable explanations
                 in Bayesian networks.",
  notes =        "GP-98LB, GP-98PhD Student Workshop",
}

@InProceedings{mengshoel:1999:PCDCPR,
  author =       "Ole J. Mengshoel and David E. Goldberg",
  title =        "Probabilistic Crowding: Deterministic Crowding with
                 Probabilisitic Replacement",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "409--416",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{mercure:2001:AIChE,
  author =       "Peter Kip Mercure and Guido F. Smits and Arthur
                 Kordon",
  title =        "Empirical Emulators for First Principle Models",
  booktitle =    "AIChE Fall Annual Meeting",
  year =         "2001",
  address =      "Reno Hilton",
  month =        "6 " # nov,
  organisation = "AIChe",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.aiche.org/conferences/techprogram/paperdetail.asp?PaperID=2373&DSN=annual01",
  abstract =     "Empirical emulators mimic the performance of first
                 principle models by using various data-driven modeling
                 techniques. The driving force for developing empirical
                 emulators is the push for reducing the time and cost
                 for new product development. Empirical emulators are
                 especially effective when hard real-time optimization
                 of a variety of complex fundamental models is needed.
                 The increased robustness of the modern data-driven
                 techniques (analytic neural networks, support vector
                 machines, genetic programming, etc.) is a reliable
                 basis for accurate representation of fundamental models
                 and gives many opportunities for effective synergy
                 between these two key modeling approaches.

                 The main schemes for building empirical emulators are
                 discussed in the paper. Several contemporary techniques
                 for robust empirical emulator design are explored
                 including analytic neural networks, recurrent neural
                 networks and Genetic Programming (GP), and the
                 capabilities of the proposed approach are illustrated
                 with a case study for a simple first principle model.

                 A key feature of empirical emulators is that the
                 training data for empirical model building is generated
                 by design of experiments from first principle models
                 called simulators. This allows a high degree of freedom
                 for development of reliable data-driven models. The
                 most obvious scheme for implementation of empirical
                 emulators is as accelerator of computational time for
                 fundamental models (the gain is 10<SUP>3</SUP> to
                 10<SUP>5 </SUP>times faster). Another possible scheme
                 is to use the empirical emulator as an estimator of
                 fundamental model performance. Of special importance to
                 on-line optimization is a scheme using the empirical
                 emulator to integrate different types of fundamental
                 models (steady-state, dynamic, fluid, kinetic, thermal,
                 etc). Most of the known empirical emulators are
                 implemented as {"}classical{"} neural networks based on
                 back-propagation learning algorithm. Their property of
                 being universal approximators is a key theoretical
                 result for successful emulation. At the same time
                 {"}classical{"} neural networks suffer from a number of
                 problems like: long computational time for training,
                 convergence to local minima, sensitivity to weight
                 generalization, too many tunable parameters, etc. These
                 problems put serious limitations on the quality of the
                 developed empirical model, increase development time,
                 and require experienced model developers. An
                 alternative empirical emulator based on analytic neural
                 networks is described in the paper. A key advantage of
                 analytic neural networks is that the function to be
                 optimized is a quadratic function of the weights of the
                 hidden-to-output layer error and has one global
                 optimum. It is no longer possible to get stuck in local
                 minima and the learning algorithm is not iterative. As
                 a result, the data-driven modeling process is
                 significantly reduced and the developed empirical
                 models are parsimonious. Of special importance to
                 empirical emulator's performance is the ability of
                 analytic neural networks to deliver multiple-model
                 solution with confidence limits. Empirical emulators
                 with confidence limits are aware of their own
                 performance which is essential for any data-driven
                 model application, especially in real-time. In the case
                 of emulating process dynamics a different type of
                 recurrent neural networks are needed. Recurrent
                 networks are neural networks with one or more local or
                 global feedback loops. The application of feedback
                 enables neural networks to acquire state
                 representations, making them suitable for emulation of
                 dynamic fundamental models. A proper structure of an
                 empirical emulator to mimic dynamic behavior is based
                 on a recurrent version of the analytic neural
                 networks.",
  abstract =     "Another approach to build a successful empirical
                 emulator is genetic programming. By simulation of
                 natural evolution and using genetic operators like
                 crossover and mutation, genetic programming delivers
                 empirical models in a form of explicit analytic
                 functions between process inputs and outputs. The
                 non-black-box form is a significant advantage of this
                 type of empirical emulator. In principle, a functional
                 relationship has better generalization capability and
                 is a more reliable indicator of model performance
                 outside the training range. This unique capability
                 makes empirical emulators designed by genetic
                 programming a promising modeling solution for process
                 scale-up. The performance of empirical emulators based
                 on analytic neural networks, analytic recurrent neural
                 networks, and genetic programming is illustrated in a
                 case study of emulating a phase change propagation in a
                 solid.",
  notes =        "American Institute of Chemical Engineers, 3 Park Ave,
                 New York, N.Y., 10016-5991, U.S.A.",
}

@InCollection{meredith:2002:SMIRPGP,
  author =       "Jeremy Meredith",
  title =        "Solving the Material Interface Reconstruction Problem
                 using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "139--147",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{merelo:1999:FTGM,
  author =       "J. J. Merelo and J. Carpio and P. Castillo and V. M.
                 Rivas and G. Romero",
  title =        "Finding a needle in a haystack using hints and
                 evolutionary computation: The case of Genetic
                 Mastermind",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "184--192",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@Article{merkle:2002:GPEM,
  author =       "Daniel Merkle and Martin Middendorf",
  title =        "Fast Ant Colony Optimization on Runtime Reconfigurable
                 Processor Arrays",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "4",
  pages =        "345--361",
  month =        dec,
  keywords =     "ACO, reconfigurable architectures, quadratic
                 assignment",
  ISSN =         "1389-2576",
  abstract =     "Ant Colony Optimization (ACO) is a metaheuristic used
                 to solve combinatorial optimisation problems. As with
                 other metaheuristics, like evolutionary methods, ACO
                 algorithms often show good optimization behaviour but
                 are slow when compared to classical heuristics. Hence,
                 there is a need to find fast implementations for ACO
                 algorithms. In order to allow a fast parallel
                 implementation, we propose several changes to a
                 standard form of ACO algorithms. The main new features
                 are the non-generational approach and the use of a
                 threshold based decision function for the ants. We show
                 that the new algorithm has a good optimization behavior
                 and also allows a fast implementation on reconfigurable
                 processor arrays. This is the first implementation of
                 the ACO approach on a reconfigurable architecture. The
                 running time of the algorithm is quasi-linear in the
                 problem size n and the number of ants on a
                 reconfigurable mesh with n2 processors, each provided
                 with only a constant number of memory words.",
  notes =        "Article ID: 5103873",
}

@InProceedings{merz:1999:GABQP,
  author =       "Peter Merz and Bernd Freisleben",
  title =        "Genetic Algorithms for Binary Quadratic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "417--424",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 Linear binary string representation",
}

@InCollection{mettler:1999:ETMSNUGA,
  author =       "Michael Mettler",
  title =        "Evolution of a Time-Optimal Minimal Spanning Network
                 Using Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "164--173",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@Article{meyer:2001:pourlascience,
  author =       "Jean-arcady Meyer and Agnes Guillot",
  title =        "La robotique evolutionniste",
  journal =      "Pour la Science",
  year =         "2001",
  month =        "juin",
  keywords =     "genetic algorithms, genetic programming, robotique,
                 robot, algorithmes evolutionnistes, Aibo, Elvis",
  URL =          "http://www.pourlascience.com/numeros/pls-284/art-5.htm",
  abstract =     "Des robots concus automatiquement par evolution et
                 selection artificielles sont parfois plus performants
                 que ceux concus par des etres humains.",
}

@InProceedings{meysenburg:1999:RGPR,
  author =       "Mark M. Meysenburg and James A. Foster",
  title =        "Randomness and {GA} Performance, Revisited",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "425--432",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{meysenburg:1999:RGQGP,
  author =       "Mark M. Meysenburg and James A. Foster",
  title =        "Random Generator Quality and {GP} Performance",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1121--1126",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  abstract =     "... We found no evidence to support the notion that
                 higher qulaity PRNGs (psuedo random number generators)
                 caused improved GP performance",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{meysenburg:2000:TG,
  author =       "Mark M. Meysenburg",
  title =        "The computational complexity of simple {GA} problems",
  booktitle =    "Graduate Student Workshop",
  year =         "2000",
  editor =       "Conor Ryan and Una-May O'Reilly and William B.
                 Langdon",
  pages =        "293--296",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{miagkikh:1999:AASCOPUPRLA,
  author =       "Victor V. Miagkikh and William F. Punch III",
  title =        "An Approach to Solving Combinatorial Optimization
                 Problems Using a Population of Reinforcement Learning
                 Agents",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1358--1365",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{miccio:1995:pGPiBDD,
  author =       "Christian Miccio and Eduardo Sanchez and Marco
                 Tomassini",
  title =        "Parallel Genetic Programming Induction of Binary
                 Decision Diagrams",
  institution =  "Ecole Polytechnique Federal de Lausanne, EPFL",
  year =         "1995",
  number =       "7",
  address =      "Switzerland",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://sawww.epfl.ch/SIC/SA/publications/SCR95/7-95-24a.html",
  abstract =     "Genetic programming is a new technique for machine
                 learning, program induction and optimization loosely
                 based on an evolutionary paradigm. Genetic programming
                 is easily amenable to parallel computing which help
                 relieve the intrinsic slowness of the approach. We
                 describe a parallel implementation of genetic
                 programming on the T3D computer. We apply the system to
                 a problem of induction of binary decision diagrams used
                 in logical circuit design. It is shown that the results
                 depend in a critical way on the representation of the
                 decision diagrams and that the parallel implementation
                 is able to find the correct solution with less
                 computational effort than the sequential version.",
}

@InCollection{mided:2002:MLPRCA,
  author =       "Zachary Mided",
  title =        "Machine Learning and Pattern Recognition using
                 Cellular Automata",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "148--157",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp",
}

@InCollection{miller:1997:poenn,
  author =       "Graham Miller",
  title =        "Preventing Overfitting of Evolved Neural Networks",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "147--158",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{miller:1998:edcrfgGA,
  author =       "Julian F. Miller and Peter Thomson",
  title =        "Evolving Digital Electronic Circuits for Real-Valued
                 Function Generation using a Genetic Algorithm",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "863--868",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Algorithms, Evolvable Hardware",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{miller:1998:gp98,
  author =       "Julian Miller",
  title =        "{GP98} Conference Report",
  journal =      "EvoNews",
  year =         "1998",
  volume =       "1",
  number =       "8",
  pages =        "10",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "EvoNews - The Newsletter of EvoNet",
  size =         "1 page",
  notes =        "review of koza:gp98",
}

@InProceedings{miller:1999:ACGP,
  author =       "Julian F. Miller",
  title =        "An empirical study of the efficiency of learning
                 boolean functions using a Cartesian Genetic Programming
                 approach",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1135--1142",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{miller:1999:DFDGEA,
  author =       "Julian F. Miller",
  title =        "Digital Filter Design at Gate-level using Evolutionary
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1127--1134",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{miller:2000:CGP,
  author =       "Julian F. Miller and Peter Thomson",
  title =        "Cartesian Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "121--132",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "This paper presents a new form of Genetic Programming
                 called Cartesian Genetic Programming in which a program
                 is represented as an indexed graph. The graph is
                 encoded in the form of a linear string of integers. The
                 inputs or terminal set and node outputs are numbered
                 sequentially. The node functions are also separately
                 numbered. The genotype is just a list of node
                 connections and functions. The genotype is then mapped
                 to an indexed graph that can be executed as a program.
                 Evolutionary algorithms are used to evolve the genotype
                 in a symbolic regression problem (sixth order
                 polynomial) and the Santa Fe Ant Trail. The
                 computational effort is calculated for both cases. It
                 is suggested that hit effort is a more reliable measure
                 of computational efficiency. A neutral search strategy
                 that allows the fittest genotype to be replaced by
                 another equally fit genotype (a neutral genotype) is
                 examined and compared with non-neutral search for the
                 Santa Fe ant problem. The neutral search proves to be
                 much more effective.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@Article{miller:2000:,
  author =       "J. F. Miller",
  title =        "Review: First {NASA/DOD} Workshop on Evolvable
                 Hardware 1999",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "1/2",
  pages =        "171--174",
  month =        apr,
  keywords =     "genetic algorithms, evolvable hardware",
  ISSN =         "1389-2576",
}

@Article{miller:2000:peddg1,
  author =       "Julian F. Miller and Dominic Job and Vesselin K.
                 Vassilev",
  title =        "Principles in the Evolutionary Design of Digital
                 Circuits-Part {I}",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "1/2",
  pages =        "7--35",
  month =        apr,
  keywords =     "genetic algorithms, evolvable hardware, evolutionary
                 computing, circuit design",
  ISSN =         "1389-2576",
  abstract =     "An evolutionary algorithm is used as an engine for
                 discovering new designs of digital circuits,
                 particularly arithmetic functions. These designs are
                 often radically different from those produced by
                 top-down, human, rule-based approaches. It is argued
                 that by studying evolved designs of gradually
                 increasing scale, one might be able to discern new,
                 efficient, and generalizable principles of design. The
                 ripple-carry adder principle is one such principle that
                 can be inferred from evolved designs for one and
                 two-bit adders. Novel evolved designs for three-bit
                 binary multipliers are given that are 20 percent more
                 efficient (in terms of number of two-input gates used)
                 than the most efficient known conventional design.",
}

@Article{miller:2000:peddg2,
  author =       "Julian F. Miller and Dominic Job and Vesselin K.
                 Vassilev",
  title =        "Principles in the Evolutionary Design of Digital
                 Circuits-Part {II}",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "3",
  pages =        "259--288",
  month =        jul,
  keywords =     "genetic algorithms, evolvable hardware, evolutionary
                 computing, circuit design, evolutionary algorithms,
                 digital circuits, fitness landscapes, case based
                 reasoning, principle extraction",
  ISSN =         "1389-2576",
  abstract =     "In a previous work it was argued that by studying
                 evolved designs of gradually increasing scale, one
                 might be able to discern new, efficient, and
                 generalisable principles of design. These ideas are
                 tested in the context of designing digital circuits,
                 particularly arithmetic circuits. This process of
                 discovery is seen as a principle extraction loop in
                 which the evolved data is analysed both phenotypically
                 and genotypically by processes of data mining and
                 landscape analysis. The information extracted is then
                 fed back into the evolutionary algorithm to enhance its
                 search capabilities and hence increase the likelihood
                 of identifying new principles which explain how to
                 build systems which are too large to evolve.",
}

@Proceedings{miller:2001:gp,
  title =        "Genetic Programming, Proceedings of Euro{GP}'2001",
  year =         "2001",
  editor =       "Julian Miller and Marco Tomassini and Pier Luca Lanzi
                 and Conor Ryan and Andrea G. B. Tettamanzi and William
                 B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Program
                 Trees, BDD, VHDL, Linear representations, Parallel
                 programming, Bloat, Image processing, Evolvability,
                 Controller design, MAX Problem, modular robot, Pattern
                 Recognition, Fixed points, Feature Extraction, VLSI
                 CAD, Multipopulation structures, Cellular model,
                 Boolean function landscape, Machine Learning, Robotic
                 Arm, Iterated Function Systems, Evolution of size, DNA,
                 Length distributions, Computational Complexity, Time
                 Series prediction, Process modelling, Causality,
                 Animat, Crossover bias, Multi-expression individuals,
                 Symbolic Regression, One-then-zeros problem, Intrinsic
                 Polymorphism, STGP, Knowledge Discovery, Dynamic
                 Fitness, Grammatical Evolution, Genotype-Phenotype
                 Mapping, Problem Generator, Turing machines, Genetic
                 Reasoning, Artificial Retina, Block-oriented
                 representation, distributed control, robust, Neutral
                 mutation, Active Character Recognition, Inverse
                 Kinematics, Evolvable Hardware, Layered Learning,
                 Contour detection, Developmental Genetic Programming,
                 Structure Optimisation, Strongly Typed GP, Linear tree
                 structure, Distributed Genetic Programming,
                 Polymorphism, Subtree-swapping Crossover, Grammatical
                 evolution, Humanoid Robotics, Variable-length Genetic
                 Algorithms, GP representation, PolyGP, Parallel
                 evolutionary algorithms, Robot soccer, Multiple
                 Sequence aligment, Quantum Computing, Robots,
                 self-reconfigurable, ROC, Fitness landscape glitches,
                 smart membrane, Multi-objective optimisation, Genetic
                 operators, EASEA, Digit Recognition, Heuristic
                 Learning, Hierarchical abstractions, Context Free
                 Grammars, Subtree Encapsulation, Neutrality, Typed GP,
                 Code Reuse, Crossover, Biotechnology, Graph-based
                 Genetic Programming, Grammar, Multiagent systems,
                 Discipulus, Digital Filters, Stereo Vision, Receiver
                 Operating Characteristics, Handwritten digit
                 classification, Evolution Strategies, Brain Building,
                 Constraint handling, Bioinformatics, Adaption,
                 Exploration vs. Exploitation, Image Processing, Linear
                 Genome, Adaptation, scalable, Color Constancy,
                 Electronic Design, Data Fusion, Binary Decision
                 Diagrams, Adaptive Genotype to Phenotype Mappings,
                 Combining Classifiers, Evolutionary Algorithms,
                 Financial markets, Standard Crossover, Modularisation,
                 Parallelism, Schema theory, Data Mining, Global
                 Memory",
  ISBN =         "3-540-41899-7",
  URL =          "http://link.springer.de/link/service/series/0558/tocs/t2038.htm",
  size =         "391 pages approx",
  notes =        "EuroGP'2001",
}

@InProceedings{miller:2001:gecco,
  title =        "Evolution of Program Size in Cartesian Genetic
                 Programming",
  author =       "Julian Miller",
  pages =        "184",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{miller:2001:eh,
  author =       "Julian F. Miller and Morten Hartmann",
  title =        "Evolving messy gates for fault tolerance: some
                 preliminary findings",
  booktitle =    "The Third NASA/DoD workshop on Evolvable Hardware",
  year =         "2001",
  editor =       "Didier Keymeulen and Adrian Stoica and Jason Lohn and
                 Ricardo S. Zebulum",
  pages =        "116--123",
  address =      "Long Beach, California",
  publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC,
                 20036-1992, USA",
  month =        "12-14 " # jul,
  organisation = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-1180-5",
  notes =        "EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/",
}

@InProceedings{miller:2001:wbcgpbp,
  author =       "Julian Miller",
  title =        "What Bloat? Cartesian Genetic Programming on Boolean
                 Problems",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "295--302",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, bloat,
                 graph-based genetic programming, genotype-phenotype",
  notes =        "GECCO-2001LB",
}

@TechReport{miller:2002:sees,
  author =       "J. F. Miller",
  title =        "What is a Good Genotype-Phenotype Mapping for the
                 Evolution of Computer Programs?",
  institution =  "University of Hertfordshire, Computer Science",
  booktitle =    "Software Evolution and Evolutionary Computation
                 Symposium Abstracts",
  year =         "2002",
  editor =       "C. L. Nehaniv and M. Loomes and P. Marrow and P.
                 Wernick",
  number =       "364",
  type =         "Technical Report",
  pages =        "16",
  address =      "University of Hertfordshire",
  month =        "2 " # feb,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://homepages.feis.herts.ac.uk/~nehaniv/EN/seec.html",
  size =         "1 page",
}

@InCollection{milstein:2000:CCCS,
  author =       "Ido Milstein",
  title =        "Co-Evolution of Communication in a Competitive
                 Setting",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "286--295",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InCollection{min:1994:constraint,
  author =       "Sherman L. Min",
  title =        "Feasibility of evolving self-learned pattern
                 recognition applied towards the solution of a
                 constrained system using genetic programming",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "110--119",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming, Rubik's
                 Cube",
  ISBN =         "0-18-187263-3",
  notes =        "2 by 2 by 2 cube

                 This volume contains 20 papers written and submitted by
                 students describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InCollection{minns:1996:hmihc,
  author =       "A. W. Minns and V. Babovic",
  title =        "Hydrological Modelling in a Hydroinformatics Context",
  booktitle =    "Distributed Hydrological Modelling",
  publisher =    "Kluwer Academic Publishers",
  year =         "1996",
  editor =       "M. B. Abbott and J. C. Refsgaard",
  pages =        "297--312",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@Book{mitchell:1996:iga,
  author =       "Melanie Mitchell",
  title =        "An Introduction to Genetic Algorithms",
  publisher =    "MIT Press",
  year =         "1996",
  keywords =     "genetic algorithms",
  ISBN =         "0-262-13316-4",
  URL =          "http://www-mitpress.mit.edu/mitp/recent-books/cog/mitnh.html",
  url_2 =        "http://www.santafe.edu/~mm/books.html",
  abstract =     "Genetic algorithms have been used in science and
                 engineering as adaptive algorithms for solving
                 practical problems and as computational models of
                 natural evolutionary systems. This brief, accessible
                 introduction describes some of the most interesting
                 research in the field and also enables readers to
                 implement and experiment with genetic algorithms on
                 their own. It focuses in depth on a small set of
                 important and interesting topics -- particularly in
                 machine learning, scientific modeling, and artificial
                 life -- and reviews a broad span of research, including
                 the work of Mitchell and her colleagues. The
                 descriptions of applications and modeling projects
                 stretch beyond the strict boundaries of computer
                 science to include dynamical systems theory, game
                 theory, molecular biology, ecology, evolutionary
                 biology, and population genetics, underscoring the
                 exciting {"}general purpose{"} nature of genetic
                 algorithms as search methods that can be employed
                 across disciplines. An Introduction to Genetic
                 Algorithms is accessible to students and researchers in
                 any scientific discipline. It includes many thought and
                 computer exercises that build on and reinforce the
                 reader's understanding of the text. The first chapter
                 introduces genetic algorithms and their terminology and
                 describes two provocative applications in detail. The
                 second and third chapters look at the use of genetic
                 algorithms in machine learning (computer programs, data
                 analysis and prediction, neural networks) and in
                 scientific models (interactions among learning,
                 evolution, and culture; sexual selection; ecosystems;
                 evolutionary activity). Several approaches to the
                 theory of genetic algorithms are discussed in depth in
                 the fourth chapter. The fifth chapter takes up
                 implementation, and the last chapter poses some
                 currently unanswered questions and surveys prospects
                 for the future of evolutionary computation.",
  notes =        "First 10 pages of Chapter 2 reviews genetic
                 programming and page of Chapter 5 discusses tree
                 encodings",
  size =         "205 pages",
}

@Book{mitchell:1997:MLbook,
  author =       "Tom M. Mitchell",
  title =        "Machine Learning",
  publisher =    "McGraw-Hill",
  year =         "1997",
  ISBN =         "0-07-042807-7",
  notes =        "Chapter 9 deals with genetic algorithms including a
                 nice short survey of genetic programming (5 pages) but
                 fails to intergrate GAs and GP into the main text on
                 Machine learning",
  size =         "414 pages",
}

@InProceedings{Miyashita:2000:GECCO,
  author =       "Kazuo Miyashita",
  title =        "Job-Shop Scheduling with {GP}",
  pages =        "505--512",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{mock:1998:welp,
  author =       "Kenrick J. Mock",
  title =        "Wildwood: The Evolution of {L}-System Plants for
                 Virtual Environments",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "476--480",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c082.pdf",
  size =         "5 pages",
  abstract =     "This paper describes the Wildwood project. In this
                 work, a genetic algorithm was applied to a simplified
                 L-system representation in or&r to generate
                 art@cial-life style plants for virtual work&. Acting as
                 a virtual gardener, a human selects which plants to
                 breed, prodking a unique new generation of plants. An
                 experiment involving a simulation-style jitness
                 function was also performed, and the virtual plants
                 adapted to maximize the fitness function.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@InCollection{mogensen:1994:macbeth,
  author =       "Christian L. Mogensen",
  title =        "{MacBeth} meets {A-Life}",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "118--128",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, game playing, hammurabi, {libGA}",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@Article{molnar:1996:bits,
  author =       "Darin Molnar",
  title =        "Genetic Programming: Will Bill Gates become Billy
                 Appleseed?",
  journal =      "Computer Bits",
  year =         "1996",
  volume =       "6",
  number =       "6",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.computerbits.com/archive/9606/genetic.htm",
  notes =        "Newspaper style chat article",
}

@InProceedings{monsieurs:2001:gecco,
  title =        "Increasing the diversity of a population in genetic
                 programming",
  author =       "Patrick Monsieurs and Eddy Flerackers",
  pages =        "185",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 Diversity, Code reuse",
  ISBN =         "1-55860-774-9",
  size =         "1 page",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{monsieurs:2001:FD,
  author =       "Patrick Monsieurs and Eddy Flerackers",
  title =        "Reducing Bloat in Genetic Programming",
  booktitle =    "Computational Intelligence : Theory and Applications",
  year =         "2001",
  editor =       "Bernd Reusch",
  volume =       "2206",
  series =       "LNCS",
  pages =        "471--478",
  address =      "Dortmund, Germany",
  month =        "1-3 " # oct,
  organization = "7th Fuzzy Days",
  publisher =    "Springer-Verlag",
  email =        "patrick.monsieurs@luc.ac.be",
  keywords =     "genetic algorithms, genetic programming, bloat",
  ISBN =         "3-540-42732-5",
  size =         "8 pages",
  abstract =     "In this paper, several techniques will be presented to
                 constrain the growth of solutions that are constructed
                 by genetic programming. The most successful technique
                 imposes a maximum size on the created individuals of
                 the population that depends solely on the size of the
                 best individual of the population. This method will be
                 compared with other methods to reduce bloat,
                 demonstrating that this method reduces bloat
                 significantly better than the other methods.",
  notes =        "http://ls1-www.cs.uni-dortmund.de/fd7/",
}

@Misc{monsieurs:2001:dricGP,
  author =       "Patrick Monsieurs and Eddy Flerackers",
  title =        "Detecting and Removing Inactive Code in Genetic
                 Programs",
  howpublished = "www",
  year =         "2001",
  month =        "7 " # nov,
  keywords =     "genetic algorithms, genetic programming, intron,
                 bloat",
  URL =          "http://alpha.luc.ac.be/~lucp1089/DetectingAndRemovingInactiveCode.pdf",
  size =         "10 pages",
  abstract =     "This paper presents a technique to measure the
                 influence a child node has on the result of its parent
                 nodes in a program generated by genetic programming.
                 Child nodes that have no influence are inactive, and
                 can be removed from the individual without affecting
                 the result of that individual, thus reducing its size.
                 This technique is described for several types of
                 non-terminal nodes, and the effect of the operation on
                 the size of individuals and convergence speed of the
                 population is tested experimentally.",
}

@PhdThesis{monsieurs:thesis,
  author =       "Patrick Monsieurs",
  title =        "Evolving Virtual Agents using Genetic Programming",
  school =       "Limburg University",
  year =         "2002",
  address =      "Diepenbeek, Belgium",
  month =        dec,
  note =         "Pending",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://alpha.luc.ac.be/~lucp1089/Doctoraatsthesis.pdf",
  size =         "179 pages",
  notes =        "Summary in flemish Nederlaans Robocup, parity, stgp,
                 bloat, santafe ant, map construction, synthetic
                 vision",
}

@TechReport{montana:stgp,
  author =       "David J. Montana",
  title =        "Strongly Typed Genetic Programming",
  institution =  "Bolt Beranek and Newman, Inc.",
  year =         "1993",
  type =         "BBN Technical Report",
  number =       "\#7866",
  address =      "10 Moulton Street, Cambridge, MA 02138, USA",
  month =        "7 " # may,
  notes =        "Superceeded by montana:stgpEC See also montana:stgp2",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/stgp.ps.Z",
  keywords =     "genetic algorithms, genetic programming",
}

@TechReport{montana:stgp2,
  author =       "David J. Montana",
  title =        "Strongly Typed Genetic Programming",
  institution =  "Bolt Beranek and Newman, Inc.",
  year =         "1994",
  type =         "BBN Technical Report",
  number =       "\#7866",
  address =      "10 Moulton Street, Cambridge, MA 02138, USA",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/stgp2.ps.Z",
  notes =        "Superceeded by montana:stgpEC Replaces montana:stgp
                 add generic and void data types, local variables,
                 run-time error trapping and reporting. Allows for
                 non-protected operations, eg inversion of singular
                 matrix is not protected.",
  size =         "31 pages",
}

@Article{montana:stgpEC,
  author =       "David J. Montana",
  title =        "Strongly Typed Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "1995",
  volume =       "3",
  number =       "2",
  pages =        "199--230",
  keywords =     "genetic algorithms, genetic programming, memory",
  notes =        "This superceeds montana:stgp and montana:stgp2

                 ",
}

@InProceedings{montana:1996:ecl4nts,
  author =       "David J. Montana and Steven Czerwinski",
  title =        "Evolving Control Laws for a Network of Traffic
                 Signals",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "333--338",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96 Java demo at
                 http://asd.bbn.com/papers/traffic/traffic.html",
}

@InProceedings{montana:1998:,
  author =       "David Montana and Robert Popp and Suraj Iyer and
                 Gordon Vidaver",
  title =        "EvolvaWare: Genetic Programming for Optimal Design of
                 Hardware-Based Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "869--874",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms, Evolvable
                 Hardware",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{moore:1997:GPasox2pep,
  author =       "Frank W. Moore and Oscar N. Garcia",
  title =        "A Genetic Programming Approach to Strategy
                 Optimization in the Extended Two-Dimensional
                 Pursuer/Evader Problem",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "249--254",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/moore/moore.gp97.ps.gz",
  abstract =     "This paper describes a genetic programming system that
                 evolves optimized solutions to the extended
                 two-dimensional pursuer/evader problem. The
                 pursuer/evader problem is a competitive zero-sum game
                 in which an evader attempts to perform maneuvers to
                 escape a faster, more agile pursuer. The extended
                 problem is more realistic than previous formulations
                 because the evader and pursuer are modeled as point
                 masses that are capable of limited thrusting and
                 turning forces, and are subject to drag forces and
                 momentum. The pursuer initially aims at a predicted
                 capture point, and uses proportional navigation to
                 attempt to maintain a constant line-of-sight angle with
                 the evader. The game ends favorably for the evader if
                 it manages to stay outside the lethal radius of the
                 pursuer for the duration of the encounter (limited by
                 the effective range of the pursuer). To solve the
                 extended two-dimensional pursuer/evader problem, a
                 strategy must be identified by which an evader (such as
                 an F-16C fighter aircraft) may maneuver to successfully
                 evade pursuers (such as surface-to-air missiles)
                 starting from a wide range of potentially lethal
                 relative initial positions.",
  notes =        "GP-97",
}

@InProceedings{Moore:1997:GPmsouu,
  author =       "Frank W. Moore",
  title =        "A Genetic Programming Methodology for Strategy
                 Optimization Under Uncertainty",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "294",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/moore/moore.gp97pp.ps.gz",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670

                 Poster at
                 http://www.cs.wright.edu/people/faculty/agoshtas/gp97pp.html",
}

@InProceedings{moore:1997:mrbGP,
  author =       "F. W. Moore and O. N. Garcia",
  title =        "New Methodology for Reducing Brittleness in Genetic
                 Programming",
  booktitle =    "Proceedings of the National Aerospace and Electronics
                 1997 Conference (NAECON-97)",
  year =         "1997",
  editor =       "E. Pohl",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/moore/moore.naecon97.ps.gz",
  notes =        "

                 ",
}

@InProceedings{moore:1997:souux2dpe,
  author =       "F. W. Moore and O. N. Garcia",
  title =        "A Methodology for Strategy Optimization Under
                 Uncertainty in the Extended Two-Dimensional
                 Pursuer/Evader Problem",
  booktitle =    "Proceedings: Eighth Midwest Artificial Intelligence
                 and Cognitive Science Conference (MAICS-97)",
  year =         "1997",
  editor =       "E. {Santos Jr.}",
  pages =        "58--65",
  publisher =    "AAAI Press",
  note =         "AAAI Technical Report CF-97-01",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/moore/moore.maics97.ps.gz",
  notes =        "Some formatting difficulties with moore.maics97.ps

                 ",
}

@InProceedings{moore:1997:noemuux2dpe,
  author =       "F. W. Moore and O. N. Garcia",
  title =        "A New Methodology for Optimizing Evasive Maneuvers
                 Under Uncertainty in the Extended Two-Dimensional
                 Pursuer/Evader Problem",
  booktitle =    "Proceedings of the Ninth IEEE International Conference
                 on Tools with Artificial Intelligence (ICTAI-97)",
  year =         "1997",
  editor =       "E. {Santos Jr.}",
  month =        nov,
  note =         "to be published",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/moore/moore.ictai.ps.gz",
  notes =        "moore.ictai.ps.gz some formating difficulties, fig 6
                 lost

                 ",
}

@PhdThesis{moore:thesis,
  author =       "Frank William Moore",
  title =        "A methodology for Strategy Optimization Under
                 Uncertainty",
  school =       "Department of Computer Scienece and Engineering,
                 Wright State University",
  year =         "1997",
  month =        "11 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  size =         "173 pages",
  abstract =     "The resulting genetic programming system evolves
                 programs that combine maneuvers with electronic
                 countermeasures to optimize aircraft survivability
                 [from anti-aircraft missile]",
  notes =        "160 Mbyte

                 In my dissertation research and related work, I evolved
                 strategies by which an aircraft could evade
                 anti-aircraft missiles. The approach I took to fitness
                 evaluation was to simulate an encounter between a
                 missile (using proportional navigation) and an aircraft
                 (controlled by stick and throttle commands issued by a
                 control program). The simulation ran at 50 Hz (typical
                 of aircraft flight control computers) Fitness was
                 equated to aircraft survivability. The training
                 population consisted of missiles launched from numerous
                 potentially lethal positions. Aggregate program fitness
                 reflected aircraft survivability against each missile
                 in the training population (i.e., program X survived 25
                 out of 50 missiles in the training population; etc.).
                 Best-of-run programs optimized survivability against
                 the training population, and were subsequently tested
                 against a large, representative test population of
                 missiles to see how well the evolved solutions
                 generalized.

                 The problem with using simulation to evaluate fitness
                 is that one has to execute each program from the
                 evolved program population over N simulated time
                 intervals, just to determine fitness against a single
                 training case. (For my missile problem, typical
                 simulated encounters lasted 20 seconds, thus entailing
                 1000 program executions PER FITNESS CASE.) So, we're
                 talking about 2-3 orders of magnitude more computation
                 than is typical for GP fitness evaluation. For the CPUs
                 available to me, it was not uncommon for a run to take
                 several days to complete. BUT the best-of-run program
                 was an embedded real-time controller that executed
                 specific aircraft maneuvers (and, later on, deployed
                 specific countermeasures) to optimize aircraft
                 survivability. What makes that significant is the fact
                 that, for the general missile countermeasures
                 optimization problem under conditions of uncertainty
                 about missile type and/or state, NO ANALYTICAL SOLUTION
                 METHODOLOGY currently exists. I believe that by
                 combining genetic programming with sophisticated
                 simulators, we will be able to optimize programs that
                 solve a wide range of control problems for which
                 analytical solutions are difficult or impossible to
                 identify. I'd like to see GP research move away from
                 toy problems and onward to complex real-world
                 applications, and I think this approach could help
                 further that process.

                 Regards to all.",
}

@InProceedings{moore:1998:imvbrpGP,
  author =       "Frank W. Moore",
  title =        "Improving Means and Variances of Best-of-Run Programs
                 in Genetic Programming",
  booktitle =    "Proceedings of the Ninth Midwest Artificial
                 Intelligence and Cognitive Science Conference
                 (MAICS-98)",
  year =         "1998",
  editor =       "M. W. Evens",
  pages =        "95--101",
  address =      "Russ Engineering Center, Wright State University,
                 Dayton, Ohio, USA",
  month =        "20-22 " # mar,
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic programming (GP) systems have traditionally
                 used a fixed training population to evolve best-of-run
                 programs according to problem-specific fitness
                 criteria. The ideal GP training population would be
                 sufficiently representative of each of the potentially
                 difficult situations encountered during subsequent
                 program use to allow the resulting best-of-run programs
                 to handle each test situation in an optimized manner.
                 Practical considerations limit the size of the training
                 population, thus reducing the percentage of situations
                 explicitly anticipated by that population. As a result,
                 best-of-run programs may fail to exhibit sufficiently
                 optimized performance during subsequent program
                 testing. This paper summarizes an investigation into
                 the effects of creating a new randomly generated
                 training population prior to the fitness evaluation of
                 each generation of programs. Test results suggest that
                 this alternative approach to training can bolster
                 generalization of evolved solutions, improving the mean
                 program performance while significantly reducing
                 variance in the fitness of best-of-run programs.",
  notes =        "http://www.iue.indiana.edu/csci/maics98/",
}

@InProceedings{moore:1998:GPmmcouu,
  author =       "F. W. Moore and O. N. Garcia",
  title =        "A Genetic Programming Methodology for Missile
                 Countermeasures Optimization Under Uncertainity",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "367--376",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/papers/moore/moore.ep98.doc.gz",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 Wright State University. moore.ep98.doc.gz is a gzipped
                 A Microsoft Word 8.0 of this paper",
}

@InProceedings{moore:1998:GPs3dmcopuu,
  author =       "Frank W. Moore",
  title =        "Genetic Programming Solves the Three-dimensional
                 Missile Countermeasures Optimization Problem Under
                 Uncertainty",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "242--245",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{moore:1995:LabVIEW,
  author =       "Jason H. Moore",
  title =        "Artificial intelligence programming with {LabVIEW:}
                 genetic algorithms for instrumentation control and
                 optimization",
  journal =      "Computer Methods and Programs in Biomedicine",
  year =         "1995",
  volume =       "47",
  number =       "1",
  pages =        "73--79",
  email =        "jhm@superh.hg.med.umich.edu",
  keywords =     "genetic algorithms, artificial intelligence, labview,
                 graphical programming languages, instrumentation
                 control, optimization",
  abstract =     "A genetic algorithm for instrumentation control and
                 optimization was developed using the LabVIEW graphical
                 programming environment. The usefulness of this
                 methodology for the optimization of a closed loop
                 control instrument is demonstrated with minimal
                 complexity and the programming is presented in detail
                 to facilitate its adaptation to other LabVIEW
                 applications. Closed loop control instruments have
                 variety of applications in the biomedical sciences
                 including the regulation of physiological processes
                 such as blood pressure. The program presented here
                 should provide a useful starting point for those
                 wishing to incorporate genetic algorithm approaches to
                 LabVIEW mediated optimization of closed loop control
                 instruments.",
  notes =        "NOT a GP. Fixed structure: 12 bit string. PMID:
                 7554864, UI: 96053901 Department of Human Genetics,
                 University of Michigan Medical School, Ann Arbor
                 48109-0618, USA.",
}

@Misc{moore:2000:CAMDA,
  author =       "Jason H. Moore and Joel S. Parker and Lance W. Hahn",
  title =        "Symbolic Discriminant Analysis for Mining Gene
                 Expression Patterns",
  booktitle =    "Critical Assessment of Techniques for Microarray Data
                 Analysis (CAMDA00)",
  year =         "2000",
  address =      "Levine Science Research Building, Duke University,
                 Durham, N.C.",
  month =        "18-19 " # dec,
  note =         "submitted abstract",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://bioinformatics.duke.edu/CAMDA/CAMDA00/posters.asp#11",
  abstract =     "Linear discriminant analysis is a popular multivariate
                 statistical approach for classification of observations
                 into groups because the theory is well described and
                 the method is easy to implement and interpret. However,
                 an important limitation is that linear discriminant
                 functions need to be pre-specified. That is, specific
                 variables need to be selected and added linearly into
                 the model. Only the coefficients are estimated from the
                 data. To address this limitation, we developed symbolic
                 discriminant analysis (SDA) for the automatic selection
                 of gene expression variables and discriminant functions
                 that can take any form.

                 Our SDA approach is inspired by the symbolic regression
                 approach of Koza (1992). We begin by defining the
                 mathematical functions (e.g. +, -, /, *, log, sqrt,
                 etc.) and the list of gene expression variables that
                 could potentially be used as the building blocks for
                 discriminant functions. Symbolic discriminant functions
                 are evaluated by generating discriminant scores for
                 each observation to be classified. The overlap in
                 distributions of discriminant scores between groups is
                 an estimate of the classification error. Class
                 membership for new observations can be predicted from
                 the discriminant score that separates the
                 distributions. To identify optimal symbolic
                 discriminant functions from the near infinite model
                 space, we employed parallel genetic programming for
                 machine learning on a 110 processor Beowulf-style
                 parallel supercomputer.

                 We applied the SDA approach to identifying subsets of
                 gene expression variables and symbolic discriminant
                 functions that can correctly classify and predict types
                 of human acute leukemia. Using a leave-one-out
                 cross-validation strategy, we identified no fewer than
                 15 different combinations of gene expression variables
                 and symbolic discriminant functions that correctly
                 classified 38/38 observations in the first dataset and
                 correctly predicted 31/34 observations in the
                 independent dataset. The most common gene identified
                 across these models was the human synaptonemal complex
                 protein 1 (SCP1) gene that is expressed in solid tumors
                 and haematological malignancies.

                 We conclude that the SDA approach provides a powerful
                 alternative to traditional multivariate statistical
                 methods for identifying gene expression patterns. The
                 advantages of SDA include the ability to identify an
                 important subset of gene expression variables from
                 among thousands of candidates and the ability to
                 identify the most appropriate mathematical functions
                 relating the gene expression variables to a clinical
                 endpoint. We anticipate this will be an important
                 methodology to add to the repertoire of approaches for
                 mining gene expression patterns.",
  notes =        "Program in Human Genetics, Department of Molecular
                 Physiology and Biophysics, Vanderbilt University
                 Medical School, Nashville, TN 37232-0700",
}

@InProceedings{moore:2001:ECML,
  author =       "Jason Moore and Joel Parker and Lance Hahn",
  title =        "Symbolic Discriminant Analysis for Mining Gene
                 Expression Patterns",
  booktitle =    "12th European Conference on Machine Learning
                 (ECML'01)",
  year =         "2001",
  address =      "Freiburg, Germany",
  month =        "3-7 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "New laboratory technologies have made it possible to
                 measure the expression levels of thousands of genes
                 simultaneously in a particular cell or tissue. The
                 challenge for computational biologists will be to
                 develop methods that are able to identify subsets of
                 gene expression variables that classify cells and
                 tissues into meaningful clinical groups. Linear
                 discriminant analysis is a popular multivariate
                 statistical approach for classification of observations
                 into groups. This is because the theory is well
                 described and the method is easy to implement and
                 interpret. However, an important limitation is that
                 linear discriminant functions need to be pre-specified.
                 To address this limitation and the limitation of
                 linearity, we developed symbolic discriminant analysis
                 (SDA) for the automatic selection of gene expression
                 variables and discriminant functions that can take any
                 form. We have implemented the genetic programming
                 machine learning methodology for optimizing SDA in
                 parallel on a Beowulf-style computer cluster.",
  notes =        "http://www.informatik.uni-freiburg.de/~ml/ecmlpkdd/index.html",
}

@InCollection{moraleda:1999:CGOESTP,
  author =       "Jorge Moraleda",
  title =        "Custom Genetic Operators for the Euclidean Steiner
                 Tree Problem",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "174--183",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{Moriarty:1998:henn,
  author =       "David E. Moriarty and Risto Miikkulainen",
  title =        "Hierarchical Evolution of Neural Networks",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "428--433",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c074.pdf",
  size =         "6 pages",
  abstract =     "In most applications of neuro-evolution, each
                 individual in the population represents a complete
                 neural network. Retent work on the SANE system,
                 however, has demonstrated that evolving individual
                 neurons often produces a more efficient genetic search.
                 This paper demonstrates that while SANE can solve easy
                 tasks very quickly, it often stalls in larger problems.
                 A hierarchical approach to neuro-evolution is presented
                 that overcomes SANE' s difficul ties by integrating
                 both a neuron-level exploratory search and a
                 network-level exploitive search. In a robot arm
                 manipulation task, the hierarchical approach
                 outperforms both a neuron-based search and a
                 network-based search.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@InProceedings{moroni:2000:A,
  author =       "Artemis Moroni and Fernando Von Zuben and Jonatas
                 Manzolli",
  title =        "ArTbitration",
  booktitle =    "Genetic Algorithms in Visual Art and Music",
  year =         "2000",
  editor =       "Colin G. Johnson and Juan Jesus Romero Cardalda",
  pages =        "143--145",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@MastersThesis{Morrison:1994:ibs,
  author =       "Dan Morrison",
  title =        "Development of a Prototype Intelligent Browsing
                 System, utilising Boolean Query Generation using
                 Genetic Programming",
  school =       "University College, London",
  year =         "1994",
  address =      "Gower Street, London, WC1E 6BT, UK",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Text
                 Retrival, Automatic Query generation, iBrowse",
  size =         "53 pages plus 40 pages of appendices",
  abstract =     "The need was identified for a generic Information
                 Retrival tool. Genetic Programming was selected as most
                 suitable paradigm for providing the necessary adaptive
                 intelligence. This was combined with conventional
                 Bollean query search techniques. Each Query is treated
                 as a genetic individual and a population of these is
                 eveolved so as to move through the search space of all
                 possible queries efficeiently. The criteria that guide
                 this search is termed relevance feedback. This
                 information is derived from the suer through tne
                 evaluation of a document set and forms the basis of the
                 fitness funtion. The best query produced in this way
                 can then be used to scan other documents, ordering
                 these according to relevance. These processes can be
                 lined to produce an application that can learn by
                 experience, requires no explicit instructions and can
                 be apllied to a wide variety of IR situations.

                 The development work was divided into three stages:
                 design and implementation of an experimental software
                 platform, research into viable configurations using
                 this platform, and construction of working models.
                 Stage one formed the focus of this project.

                 The project specification was thus to produce a
                 software system that can act as a testbed during
                 experimentation in teh second stage and as an early
                 prototype of future applications. This was achieved,
                 the souce code being written in C++ to run on a PC.",
  notes =        "Supervised by Chris Clack",
}

@InProceedings{mota:1999:ISEFSCGA,
  author =       "Cristina Mota and Heitor Ferreira and Agostinho Rosa",
  title =        "Independent and Simultaneous Evolution of Fuzzy Sleep
                 Classifiers by Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1622--1629",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Mousavi:1997:iscdd,
  author =       "A. Mousavi and A. Gunasekaran and P. Adi",
  title =        "Intelligent System for Customer Driven Design",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "150--156",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670

                 ",
}

@InProceedings{moustafa:1999:UGAFAFAMG,
  author =       "Rida E. Moustafa and Kenneth A. De Jong and Edward J.
                 Wegman",
  title =        "Using Genetic Algorithms For Adaptive Function
                 Approximation and Mesh Generation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "798",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{mowery:2002:AGACEPCD,
  author =       "Jared D. Mowery",
  title =        "A Genetic Algorithm using Changing Environments for
                 Pathfinding in Continuous Domains",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "168--176",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InCollection{mowery:2002:SGGSPGA,
  author =       "Ben Mowery",
  title =        "Solving the Generalized Graph Search Problem with
                 Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "158--167",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InCollection{mozes:1995:EBTLCGP,
  author =       "Ari W. Mozes",
  title =        "Emergent Behavior in Traffic Light Controllers using
                 Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "209--218",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{Mullen:1997:GAoacthsp,
  author =       "David S. Mullen and Ralph M. Butler",
  title =        "Genetic Algorithms In Optimization of Adjacency
                 Constrained Timber Harvest Scheduling Problems",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "379",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{mulloy:1996:dGPctsp,
  author =       "Brian S. Mulloy and Rick L. Riolo and Robert S.
                 Savit",
  title =        "Dynamics of Genetic Programming and Chaotic Time
                 Series Prediction",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "166--174",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://pscs.physics.lsa.umich.edu/pub/papers/pscs-96-001.ps.gz",
  size =         "9 pages",
  abstract =     "An investigation into the dynamics of Genetic
                 Programming applied to chaotic time series prediction
                 is reported. An interesting characteristic of adaptive
                 search techniques is their ability to perform well in
                 many problem domains while failing in others. Because
                 of Genetic Programming's flexible tree structure, any
                 particular problem can be represented in myriad forms.
                 These representations have variegated effects on search
                 performance. Therefore, an aspect of fundamental
                 engineering significance is to find a representation
                 which, when acted upon by Genetic Programming
                 operators, optimizes search performance. We discover,
                 in the case of chaotic time series prediction, that the
                 representation commonly used in this domain does not
                 yield optimal solutions. Instead, we find that the
                 population converges onto one ``accurately
                 replicating'' tree before other trees can be explored.
                 To correct for this premature convergence we make a
                 simple modification to the crossover operator. In this
                 paper we review previous work with GP time series
                 prediction, pointing out an anomalous result related to
                 overlearning, and report the improvement effected by
                 our modified crossover operator.",
  notes =        "GP-96",
}

@InProceedings{munetomo:1999:ILGND,
  author =       "Masaharu Munetomo and David E. Goldberg",
  title =        "Identifying Linkage Groups by
                 Nonlinearity/Non-monotonicity Detection",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "433--440",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{murata:1999:SLSDGLSAMOP,
  author =       "Tadahiko Murata and Hisao Ishibuchi and Mitsuo Gen",
  title =        "Specification of Local Search Directions in Genetic
                 Local Search Algorithms for Multi-Objective
                 Optimization Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "441--448",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{murray:2002:OAGASEDP,
  author =       "Mike Murray",
  title =        "On the Application of Genetic Algorithms to Scheduling
                 Engineering Design Projects",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "177--186",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{muruzabal:1999:MSGUCC,
  author =       "Jorge Muruzabal",
  title =        "Mining the Space of Generality with
                 Uncertainty-Concerned Cooperative Classifiers",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "449--457",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{muruzabal:2000:pmbcGP,
  author =       "Jorge Muruzabal and Carlos Cotta-Porras and Amelia
                 Fernandez",
  title =        "Some Probabilistic Modelling Ideas For Boolean
                 Classification In Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "133--148",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "We discuss the problem of boolean classification via
                 Genetic Programming. When predictors are numeric, the
                 standard approach proceeds by classifying according to
                 the sign of the value provided by the evaluated
                 function. We consider an alternative approach whereby
                 the magnitude of such a quantity also plays a role in
                 prediction and evaluation. Specifically, the original,
                 unconstrained value is transformed into a probability
                 value which is then used to elicit the classification.
                 This idea stems from the well-known logistic regression
                 paradigm and can be seen as an attempt to squeeze all
                 the information in each individual function. We
                 investigate the empirical behaviour of these variants
                 and discuss a third evaluation measure equally based on
                 probabilistic ideas. To put these ideas in perspective,
                 we present comparative results obtained by alternative
                 methods, namely recursive splitting and logistic
                 regression.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{muslea:1997:SINERGYlb,
  author =       "Ion Muslea",
  title =        "A General-Purpose {AI} Planning System Based on the
                 Genetic Programming Paradigm",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "157--164",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  URL =          "http://www.isi.edu/~muslea/PS/gp97.ps",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{muslea:1997:SINERGY,
  author =       "Ion Muslea",
  title =        "{SINERGY}: {A} Linear Planner Based on Genetic
                 Programming",
  booktitle =    "Fourth European Conference on Planning",
  year =         "1997",
  editor =       "Sam Steel and Rachid Alami",
  volume =       "1348",
  series =       "Lecture notes in artificial intelligence",
  address =      "Toulouse, France",
  month =        "24--26 " # sep,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-63912-8",
  URL =          "http://www.isi.edu/~muslea/PS/ecp97.ps",
  notes =        "ECP'97 http://lolita.laas.fr:80/ecp97/",
}

@InProceedings{muslea:1998:SINERGY,
  author =       "Ion Muslea",
  title =        "A General-Purpose {AI} planning System Based on the
                 Genetic Programming Paradigm",
  booktitle =    "Proceedings of the World Automation Congress
                 WAC-1998",
  year =         "1998",
  address =      "Anchorage",
  month =        "10-14 " # may,
  keywords =     "genetic algorithms, genetic programming, AI planning,
                 conjunctive goals, SINERGY",
  URL =          "http://www.isi.edu/~muslea/PS/2_WAC.ps",
  URL =          "http://roadrunner.com/~js/products/books.html",
  size =         "6 pages",
  notes =        "Interational Symposium On Intelligent Automation and
                 Control (ISIAC'98) at the third Biannual World
                 Automation Congress (WAC'98)",
}

@InProceedings{Musumbu:1997:esiaialp,
  author =       "Kaninda Musumbu and Kablan Barbar and Maroun Nassif",
  title =        "Evolution Strategies to Improve Abstract
                 Interpretation Algorithms for Logic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "logic programming, Evolution Strategies",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InCollection{Mydlowec:1997:dGPemm,
  author =       "William Mydlowec",
  title =        "Discovery by Genetic Programming of Empirical
                 Macroeconomic Models",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "168--177",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InProceedings{myers:1999:LBNIDEA,
  author =       "James W. Myers and Kathryn B. Laskey and Kenneth A.
                 DeJong",
  title =        "Learning Bayesian Networks from Incomplete Data using
                 Evolutionary Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "458--465",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{mysinger:1998:GDAIPCGB,
  author =       "Michael Mysinger",
  title =        "Genetic Design of an Artificial Intelligence to Play
                 the Classic Game of Battleship",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "101--110",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{nacaskul:1997:pop,
  author =       "Poomjai Nacaskul",
  title =        "Phenotype-Object Programming, Phenotype-Array
                 Datatype, and an Evolutionary Signal-Ensemble {FX}
                 Trading Model",
  booktitle =    "ET'97 Theory and Application of Evolutionary
                 Computation",
  year =         "1997",
  editor =       "Chris Clack and Kanta Vekaria and Nadav Zin",
  pages =        "95--108",
  address =      "University College London, UK",
  month =        "15 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We set out to optimise a financial trading model which
                 uses an ensemble of time-series trend indicating
                 signal-models. The optimisation search is combinatorial
                 over the combination of signal-model classes as well as
                 parametric over the parameterisation of individual
                 signal-model objects. Because the optimisation
                 objective (net gain from simulated transactions against
                 a historical price series) is not differentiable w.r.t.
                 our trading model space, we look to evolutionary
                 optimisation [EO] methodologies [Fogel, 1994], e.g.
                 Genetic Algorithm [GA] [Holland, 1975/92], which rely
                 on direct solution performance evaluation. However,
                 because the multi-stage nature of our solution space
                 prohibits stochastic-evolutionary convergence, we need
                 to engineer a new EO paradigm to implement
                 combinatorial and parametric search processes
                 concurrently. This, we accomplish by exploiting the
                 inherently object-oriented [OO] [Booch, 1994;
                 Stroustrup, 1991] nature of an EO algorithm and of the
                 combinatorial-parametric solutions. We propose
                 Phenotype-Object Programming [POP] as a generalised OO
                 model and implementation of an EO algorithm and
                 Phenotype-Array Datatype [PAD] as a generalised OO
                 model and implementation of a combinatorial-parametric
                 solution [Nacaskul, 1997]. We apply this to our
                 signal-ensemble trading model and discuss experimental
                 results on DEM/JPY and USD/DEM data.",
  notes =        "http://www.cs.ucl.ac.uk/isrg/et97/",
}

@Article{nachbar:1995,
  author =       "Robert B. Nachbar",
  title =        "Genetic Programming",
  journal =      "The Mathematica Journal",
  year =         "1995",
  volume =       "5",
  number =       "3",
  pages =        "44--55",
  keywords =     "genetic algorithms, genetic programming, Mathematica",
  address =      "600 Harrison st., San Francisco, CA 94107, USA",
  publisher =    "Miller Freedman Inc",
  notes =        "Tutorial. Mathemetica can simplify GP S-expressions.
                 Constant pertubation (=fine tuning mutation?) Announced
                 in posting to GP list on Mon, 12 Jun 95 08:39:42 EDT
                 Code available?",
}

@InProceedings{nachbar:1998:me:hrctam,
  author =       "Robert B. Nachbar",
  title =        "Molecular Evolution: {A} Hierarchical Representation
                 for Chemical Topology and Its Automated Manipulation",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "246--253",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98 Grammar for producing organic molecules based on
                 valancy",
}

@Article{nachbar:2000:meamhtaams,
  author =       "Robert B. Nachbar",
  title =        "Molecular Evolution: Automated Manipulation of
                 Hierarchical Chemical Topology and Its Application to
                 Average Molecular Structures",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "1/2",
  pages =        "57--94",
  month =        apr,
  keywords =     "genetic algorithms, chemical topology, hierarchy,
                 Mathematica, genetic program, topological descriptor,
                 average chemical structure",
  ISSN =         "1389-2576",
  abstract =     "A simple hierarchical data structure (tree) and
                 associated set of algorithms (written in Mathematica)
                 have been developed that permit the direct manipulation
                 of the topology of a molecule while simultaneously
                 maintaining valid chemical valence. Coupled with a
                 genetic algorithm optimization engine, these
                 computational tools can be used to optimize chemical
                 structures under the guidance of an appropriate fitness
                 function. A detailed study of the factors that
                 influence the performance of the method revealed that
                 it is strongly dependent on the size and complexity of
                 the evolved chemical structures. The effects of
                 population size and choice of genetic operators are
                 much smaller. The results of an exploration into the
                 discovery of average molecular structures using this
                 methodology is also described.",
}

@InProceedings{naemura:1998:mgGPie,
  author =       "Takayoshi Naemura and Tomonori Hashiyama and Shigeru
                 Okuma",
  title =        "Module Generation for Genetic Programming and Its
                 Incremental Evolution",
  booktitle =    "Second Asia-Pacific Conference on Simulated Evolution
                 and Learning",
  year =         "1998",
  editor =       "Charles Newton",
  address =      "Australian Defence Force Academy, Canberra,
                 Australia",
  month =        "24-27 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.okuma.nuee.nagoya-u.ac.jp/~naemura/research/SEAL98.ps",
  notes =        "SEAL'98 Possible publication in springer-verlag LNAI
                 series SEAL98#064 ADF, MA, COAST, NAND gates, parity
                 problem (incremental Even-9)",
}

@InProceedings{Nagasaka:1997:grsgCS,
  author =       "Ichiro Nagasaka and Toshiharu Taura",
  title =        "Geometic Representation for Shape Generation using
                 Classifier System",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "genetic algorithms, classifier systems",
  pages =        "515--520",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{nakanishi:1996:cp,
  author =       "Yasuto Nakanishi",
  title =        "Capturing Preference into a Function using
                 Interactions with a Manual Evolutionary Design Aid
                 System",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "133--140",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://snoopy.t.u-tokyo.ac.jp/~naka/papers/GP96/GP96.html",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{nakashima:1995:opma,
  author =       "Hideyuki Nakashima and Itsuki Noda and Ichiro Ohsawa",
  title =        "Organic Programming for Multi-Agents",
  booktitle =    "ICMAS-95 Proceedings First International Conference on
                 Multi-Agent Systems",
  year =         "1995",
  editor =       "Victor Lesser",
  pages =        "459",
  address =      "San Francisco, California, USA",
  month =        "12--14 " # jun,
  publisher =    "AAAI Press/MIT Press",
  keywords =     "multi-agent",
  ISBN =         "0-262-62102-9",
  size =         "1 page",
  abstract =     "An organic program consists of processes executing the
                 same genetically encoded program but in possibly
                 different environments. Enviroments are formed by
                 collection of cells. Processes control agents?
                 Processes are arranged in a heirarchy with higher
                 levels (leafs) potentially overrriding lower ones.

                 No discussion of how programs are inherited or evole,",
}

@InProceedings{nansgue:1997:ibGPp,
  author =       "Phaderm Nangsue and Susan E. Conry",
  title =        "Internet-Based Genetic Programming Platform",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "174--179",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming, java",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{nangsue:1998:aomdpmEA,
  author =       "Phaderm Nangsue and Susan E. Conry",
  title =        "An Agent-Oriented, Massively Distributed
                 Parallelization Model of Evolutionary Algorithms",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{naoki:1999:TEEMTSR,
  author =       "Mori Naoki and Kita Hajime",
  title =        "The Entropy Evaluation Method for the Thermodynamical
                 Selection Rule",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "799",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{narayanan:1998:DNAacsp,
  author =       "Ajit Narayanan and Spiridon Zorbalas",
  title =        "{DNA} algorithms for computing shortest paths",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "718--724",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{nassar:2002:ACDFAC,
  author =       "Karim Nassar",
  title =        "Automatic Creation of Digital Fast Adder Circuits by
                 Means of Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "187--194",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp two-bit and 4-bit fast adder",
}

@Article{Nastran:MPT,
  author =       "M. Nastran and J. Balic",
  title =        "Prediction of metal wire behavior using genetic
                 programming",
  journal =      "Journal of Materials Processing Technology",
  year =         "2002",
  volume =       "122",
  number =       "2-3",
  date =         "28 March",
  pages =        "368--373",
  abstract =     "Dimensional stability of forming processes is becoming
                 very important in the modern manufacture. It is
                 particularly in mass manufacture that technological
                 systems have to be most reliable and accurate. Growing
                 market demands are pushing the manufacturing engineers
                 towards process optimization in order to achieve high
                 machinery efficiency and reduce manufacturing costs.
                 Predicting the process behavior is an important
                 precondition for having it improved. The paper presents
                 the use of genetic programming for forecasting the wire
                 geometry after forming. The obtained results are the
                 basis for later optimization of forming processes.",
  keywords =     "genetic algorithms, genetic programming, Manufacture,
                 Control, Prediction, Accuracy",
  URL =          "http://www.sciencedirect.com/science/article/",
}

@InProceedings{naudts:1999:AMDEE,
  author =       "Bart Naudts and Adriaan Schippers",
  title =        "A Motivated Definition of Exploitation and
                 Exploration",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "800",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{nault:1999:PGSHCT,
  author =       "Georges Nault and Vincent Rialle and Jean-Guy
                 Meunier",
  title =        "{PROGEN}: a Genetic-Based Semi-automatic Hypertext
                 Construction Tool-first steps and experiment",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1630--1635",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{navarro:1999:MIUPGO,
  author =       "Pedro Luis Kantek Garcia Navarro and Pedro P. B. de
                 Oliveira and Fernando M. Ramos and Haroldo F.
                 Campos-Velho",
  title =        "Magnetotelluric Inversion Using Problem-Specific
                 Genetic Operators",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1580--1587",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{nawa:1998:CBMsrri,
  author =       "Norberto Eiji Nawa and Hugo {de Garis} and Felix Gers
                 and Michael Korkin",
  title =        "{ATR}'s {CAM}-Brain Machine ({CBM}) Simulation Results
                 and Representation Issues",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "875--882",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Evolvable Hardware",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{neff:1997:GPposec,
  author =       "A. Darcie Neff and James W. Haefner and Raymond D.
                 Dueser",
  title =        "Using Genetic Programming to Predict the Occurrence of
                 Species in Ecological Communities",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@TechReport{Neil96,
  author =       "Julian Neil and Kevin B. Korb",
  title =        "The {MML} evolution of classification graphs",
  institution =  "Department of Computer Science, Monash University",
  year =         "1996",
  type =         "Technical report",
  number =       "CS 96/252",
  address =      "Melbourne, Australia",
  month =        "15 " # jan,
  keywords =     "genetic algorithms, genetic programming,
                 classification graphs (DAGs), decision graph,
                 classification graph, DGraph, minimum message length,
                 MML, description, MDL, C4.5",
  URL =          "http://www.csse.monash.edu.au/publications/1996/tr-cs96-252.ps.gz",
  abstract =     "NB. what is commonly called a decision [tree$|$graph]
                 is more correctly called a classification tree or graph
                 (as here)",
  notes =        "not a classic tree lisp S-expression but variable size
                 graph chromosomes are evolved by a GA. Tested on usual
                 machine learning test set. See also Neil:1999:ECM (and
                 a number of technical reports 1998-99)",
  size =         "20 pages",
}

@InProceedings{Neil:1999:ECM,
  author =       "Julian R. Neil and Kevin B. Korb",
  title =        "The Evolution of Causal Models: {A} Comparison of
                 {Bayesian} Metrics and Structure Priors",
  booktitle =    "Proceedings of the 3rd Pacific-Asia Conference on
                 Methodologies for Knowledge Discovery and Data Mining
                 ({PAKDD}-99)",
  year =         "1999",
  editor =       "Ning Zhong and Lizhu Zhou",
  volume =       "1574",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "432--437",
  address =      "Beijing, China",
  month =        "26-28 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65866-1",
  notes =        "See also Neil96",
}

@InProceedings{neves:1999:ASDEA,
  author =       "Jose Neves and Miguel Rocha and Hugo Rodrigues and
                 Miguel Biscaia and Jose Alves",
  title =        "Adaptive Strategies and the Design of Evolutionary
                 Applications",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "473--479",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{neves:1999:EPPNR,
  author =       "Ana Neves and Arlindo Silva and Ernesto Costa",
  title =        "Evolutionary Path Planning for Nonholonomic Robots",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "466--472",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ngan:1998:ugbGPdmmk,
  author =       "Po Shun Ngan and Man Leung Wong and Kwong Sak Leung
                 and Jack C. Y. Cheng",
  title =        "Using Grammar Based Genetic Programming for Data
                 Mining of Medical Knowledge",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "254--259",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{Nguyen:1997:icsGAaocrb,
  author =       "Khanh V. Nguyen",
  title =        "Improving the Crossover Operator in Genetic Algorithms
                 and Applications in Optimal Conference Room Booking",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "178--186",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@InCollection{kinnear:nguyen,
  title =        "Evolvable 3{D} Modeling for Model-Based Object
                 Recognition Systems",
  author =       "Thang Nguyen and Thomas Huang",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "22",
  pages =        "459--475",
  size =         "17 pages",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP on very small populations (10). Very powefull,
                 aircraft design, primatives

                 see also
                 ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/.message
                 gp-3D-modeling.ps.Z

                 ",
}

@TechReport{nguyen:emsat3d,
  author =       "Thang C. Nguyen and David S. Goldberg and Thomas S.
                 Huang",
  title =        "Evolvable Modeling: structural adaptation through
                 hierarchical evolution for 3-{D} model-based vision",
  institution =  "Beckman Institute and Coordinated Science Laboratory,
                 University of Illinois",
  year =         "1993",
  address =      "Urbana, IL 61801, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/gp-3D-modeling.ps.Z",
  abstract =     "The paper describes a platform to evolve 3-D models of
                 jet planes, with ideas from both GA and GP. It is meant
                 to help in model-acquisition, generation and
                 refinement, mainly for model-based 3-D object
                 recognition (a computer vision problem), rather than
                 for high-resolution computer graphics. Nevertheless,
                 the main idea is to evolve 3-D structures suitable for
                 graphic rendering and other uses. It has been
                 implemented successfully in Mathematica and the paper
                 is an abstract submitted to ACCV'93.",
  notes =        "See also kinnear:nguyen",
  size =         "5 pages",
}

@InProceedings{aguyen:1997:e3D,
  author =       "Thang C. Nguyen and Thomas Huang",
  title =        "Evolutionary 3{D} Design of Complex Shapes and a
                 Vector Space Genetic Algorithm",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "188--198",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670

                 SR71 aircraft. B-spline model face",
}

@InProceedings{niao:1999:OE,
  author =       "Fernando Niao and German Hernandez and Dipankar
                 Dasgupta",
  title =        "On Evolution of stochastic dynamical neural networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "801",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{nicolau+ryan:2002:gecco:workshop,
  title =        "On the use of gene dependency to avoid deceptive
                 traps",
  author =       "Miguel Nicolau and Conor Ryan",
  pages =        "124--127",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{niehaus:2001:EuroGP,
  author =       "Jens Niehaus and Wolfgang Banzhaf",
  title =        "Adaption of Operator Probabilities in Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "325--336",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Adaptation,
                 Adaption, Structure Optimisation",
  ISBN =         "3-540-41899-7",
  size =         "12 pages",
  abstract =     "In this work we tried to reduce the number of free
                 parameters within Genetic Programming without reducing
                 the quality of the results. We developed three new
                 methods to adapt the probabilities, different genetic
                 operators are applied with. Using two problems from the
                 areas of symbolic regression and classification we
                 showed that the results in these cases were better than
                 randomly chosen parameter sets and could compete with
                 parameter sets chosen with empirical knowledge.

                 ",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{Nikolaev:1997:iGPdt,
  author =       "Nikolay I. Nikolaev and Vanio Slavov",
  title =        "Inductive Genetic Programming with Decision Trees",
  booktitle =    "9th European Conference on Machine Learning",
  year =         "1997",
  address =      "Prague, Czech Republic",
  month =        "3-26 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.aubg.bg/faculty/cs/nikolaev/papers/ecml97.ps.gz",
  abstract =     "This paper proposes an empirical study of inductive
                 Genetic Programming with Decision Trees. An approach to
                 development of fitness functions for efficient
                 navigation of the search process is presented. It
                 relies on analysis of the fitness landscape structure
                 and suggests measuring its characteristics with
                 statistical correlations. We demonstrate that this
                 approach increases the global landscape correlation,
                 and thus leads to mitigation of the search
                 difficulties. Another claim is that the elaborated
                 fitness functions help to produce decision trees with
                 low syntactic complexity and high predictive
                 accuracy.",
  notes =        "ECML-97",
}

@TechReport{nikolaev:1997:t2tdp,
  author =       "N. I. Nikolaev and V. Slavov",
  title =        "The Tree-to-Tree Distance Problem in Inductive Genetic
                 Programming",
  institution =  "Computer Science, American University in Bulgaria",
  year =         "1997",
  type =         "Technical Report",
  number =       "TR 3-6-97",
  address =      "Bulgaria",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.aubg.bg/faculty/cs/nikolaev/pbl.html",
}

@InProceedings{nikolaev:1998:ciGP,
  author =       "N. Nikolaev and S. Slavov",
  title =        "Concepts of Inductive Genetic Programming",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "49--60",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "This paper presents the fundamental concepts of
                 inductive Genetic Programming, an evolutionary search
                 method especially suitable for inductive learning
                 tasks. We review the components of the method, and
                 propose new approaches to some open issues such as: the
                 sensitivity of the operators to the topology of the
                 genetic program trees, the coordination of the
                 operators, and the investigation of their performance.
                 The genetic operators are examined by correlation and
                 information analysis of the fitness landscapes. The
                 performance of inductive Genetic Programming is studied
                 with population diversity and evolutionary dynamics
                 measures using hard instances for induction of regular
                 expressions.",
  notes =        "EuroGP'98",
}

@InProceedings{nikolaev:1998:dbiGP,
  author =       "Nikolay I. Nikolaev and Vanio Slavov",
  title =        "The Dynamics of Biased Inductive Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "260--268",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{Nikolaev98,
  author =       "Nikolay I. Nikolaev and Vanio Slavov",
  title =        "Inductive Genetic Programming with Decision Trees",
  journal =      "Intelligent Data Analysis",
  volume =       "2",
  pages =        "31--44",
  year =         "1998",
  number =       "1-4",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6VSY-40T9N4T-W/1/691d2fb30e5913396fa5ab5af6772f5c",
  abstract =     "This article proposes a study of inductive Genetic
                 Programming with Decision Trees (GPDT). The theoretical
                 underpinning is an approach to the development of
                 fitness functions for improving the search guidance.
                 The approach relies on analysis of the global fitness
                 landscape structure with a statistical correlation
                 measure. The basic idea is that the fitness landscape
                 could be made informative enough to enable efficient
                 search navigation. We demonstrate that by a careful
                 design of the fitness function the global landscape
                 becomes smoother, its correlation increases, and
                 facilitates the search. Another claim is that the
                 fitness function has not only to mitigate navigation
                 difficulties, but also to guarantee maintenance of
                 decision trees with low syntactic complexity and high
                 predictive accuracy.",
}

@InCollection{nikolaev:1999:aigp3,
  author =       "Nikolay I. Nikolaev and Hitoshi Iba and Vanio Slavov",
  title =        "Inductive Genetic Programming with Immune Network
                 Dynamics",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "15",
  pages =        "355--376",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@Article{nikolaev:2001:TEC,
  author =       "Nikolay Y. Nikolaev and Hitoshi Iba",
  title =        "Regularization Approach to Inductive Genetic
                 Programming",
  journal =      "IEEE Transactions on Evolutionary Computing",
  year =         "2001",
  volume =       "54",
  number =       "4",
  pages =        "359--375",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, learning
                 (artificial intelligence), tree data structures, tree
                 searching, data mining, Kolmogorov-Gabor polynomials,
                 inductive genetic programming, learning polynomials,
                 multivariate polynomials, tree structures, statistical
                 bias, tree nodes, data mining, regularization, time
                 series prediction, STROGANOFF,local search",
  URL =          "http://ieeexplore.ieee.org/iel5/4235/20398/00942530.pdf?isNumber=20398",
  abstract =     "This paper presents an approach to regularization of
                 inductive genetic programming tuned for learning
                 polynomials. The objective is to achieve optimal
                 evolutionary performance when searching high-order
                 multivariate polynomials represented as tree
                 structures. We show how to improve the genetic
                 programming of polynomials by balancing its statistical
                 bias with its variance. Bias reduction is achieved by
                 employing a set of basis polynomials in the tree nodes
                 for better agreement with the examples. Since this
                 often leads to over-fitting, such tendencies are
                 counteracted by decreasing the variance through
                 regularization of the fitness function. We demonstrate
                 that this balance facilitates the search as well as
                 enables discovery of parsimonious, accurate, and
                 predictive polynomials. The experimental results given
                 show that this regularization approach outperforms
                 traditional genetic programming on benchmark data
                 mining and practical time-series prediction tasks.",
}

@InProceedings{nikolaev:2001:CEC,
  author =       "Nikolay Nikolaev and Hitoshi Iba",
  title =        "Genetic Programming of Polynomial Harmonic Models
                 using the Discrete Fourier Transform",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "267--274",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 regularization, time series prediction, STROGANOFF,
                 GMDH network",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 overfitting avoidance. polynomial harmonic GP. Tested
                 on Mackey-Glass, Sunspots, Yen-Dollar exchange rate
                 prediction, time lags Complexity penalty, Akaike
                 fitness multipled by (N+M)/(N-M)

                 ",
}

@InProceedings{nikolaev:2001:GECCO,
  title =        "Genetic Programming using Chebishev Polynomials",
  author =       "Nikolay Nikolaev and Hitoshi Iba",
  pages =        "89--96",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming,
                 regularization, time series prediction, STROGANOFF,
                 PCA, GMDH network",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO

                 overfitting avoidance. polynomial harmonic GP. Tested
                 on Mackey-Glass, Sunspots, Yen-Dollar exchange rate
                 prediction, time lags Complexity penalty, Akaike, H.
                 (1970). Statistical Predictor Identification. Annals of
                 the Institute of 25 Statistical Mathematics 22, 203 -
                 217. fitness multipled by (N+M)/(N-M)

                 ",
}

@Article{nikolaev:2001:GPEM,
  author =       "Nikolay I. Nikolaev and Hitoshi Iba",
  title =        "Accelerated Genetic Programming of Polynomials",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "3",
  pages =        "231--257",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1389-2576",
  abstract =     "An accelerated polynomial construction technique for
                 genetic programming is proposed. This is a horizontal
                 technique for gradual expansion of a partial polynomial
                 during traversal of its tree-structured representation.
                 The coefficients of the partial polynomial and the
                 coefficient of the new term are calculated by a rapid
                 recurrent least squares (RLS) fitting method. When used
                 for genetic programming (GP) of polynomials this
                 technique enables us not only to achieve fast
                 estimation of the coefficients, but also leads to power
                 series models that differ from those of traditional
                 Koza-style GP and from those of the previous GP with
                 polynomials STROGANOFF. We demonstrate that the
                 accelerated GP is sucessful in that it evolves
                 solutions with greater generalization capacity than
                 STROGANOFF and traditional GP on symbolic regression,
                 pattern recognition, and financial time-series
                 prediction tasks.",
}

@InProceedings{nikolaev:2001:gpphmdft,
  author =       "Nikolay Nikolaev and Hitoshi Iba",
  title =        "Genetic Programming of Polynomial Harmonic Models
                 using the Discrete Fourier Transform",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "267--274",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@InProceedings{nikolaev:2002:oaigpop,
  author =       "Nikolay Nikolaev and Lilian M. {de Menezes} and
                 Hitoshi Iba",
  title =        "Overfitting Avoidance in Genetic Programming of
                 Polynomials",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "1209--1214",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, stroganoff",
}

@InProceedings{nishiguchi:1998:erpmnGP,
  author =       "Masato Nishiguchi and Yoshiji Fujimoto",
  title =        "Evolutions of Recursive Programs with Multi-Niche
                 Genetic Programming (mn{GP})",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "247--252",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  file =         "c043.pdf",
  size =         "6 pages",
  abstract =     "A recursive program is one of the most intelligent and
                 sophisticated programs written by a human programmer.
                 This paper provides the challenging experiments for the
                 evolution of such an intelligent recursive program with
                 Genetic Programming (GP). In this paper, we propose two
                 types of the GP with multi-niches (mnGP) : GP with
                 structure-based multi-niches and GP with fitness
                 case-based multi-niches. We apply them to the Fibonacci
                 series problem and the search problem, respectively. We
                 have obtained outstanding results with mnGP for these
                 problems.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence

                 ",
}

@InProceedings{nishikawa:1999:VDSPDG,
  author =       "Akio Nishikawa and Masami Hagiya and Masayuki
                 Yamamura",
  title =        "Virtual {DNA} Simulator and Protocol Design by {GA}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1810--1816",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "dna and molecular computing",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{niwa:1996:dGPemsa,
  author =       "Tatsuya Niwa and Hitoshi Iba",
  title =        "Distributed Genetic Programming: Empirical Study and
                 Analysis",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "339--344",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@InCollection{nodelman:1998:TERMMEDLSM,
  author =       "Uri Nodelman",
  title =        "The Evolution of Relational Memory Models and the
                 Emergence of Distinct Long-term and Short-term
                 Memories",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "111--117",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InCollection{kinnear:nordin,
  author =       "Peter Nordin",
  title =        "A Compiling Genetic Programming System that Directly
                 Manipulates the Machine Code",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "311--331",
  chapter =      "14",
  size =         "19 pages",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Machine code GP Sun Spark and i868",
}

@InProceedings{Nordin:1995:cce,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Complexity Compression and Evolution",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "310--317",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-370-0",
  URL =          "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/icga95-1.ps.gz",
  size =         "8 pages",
  abstract =     "Compression of information is an important concept in
                 the theory of learning. We argue for the hypothesis
                 that there is an inherent compression pressure towards
                 short, elegant and general solutions in a genetic
                 programming system and other variable length
                 evolutionary algorithms. This pressure becomes visible
                 if the size or complexity of solutions are measured
                 without non-effective code segments called introns. The
                 built in parsimony pressure effects complex fitness
                 functions crossover probability, generality, maximum
                 depth or length of solutions, explicit parsimony,
                 granularity of fitness function, initialization depth
                 or length, and modularization. Some of these effects
                 are positive and some are negative. In this work we
                 provide a basis for an analysis of these effects and
                 suggestions to overcome the negative implications in
                 order to obtain the balance needed for successful
                 evolution. An empirical investigation that supports our
                 hypothesis is also presented.",
}

@InProceedings{Nordin:1995:tcp,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Evolving Turing-Complete Programs for a Register
                 Machine with Self-modifying Code",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "318--325",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-370-0",
  URL =          "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/icga95-2.ps.gz",
  abstract =     "

                 The majority of commercial computers today are register
                 machines of von Neumann type. We have developed a
                 method to evolve Turing-complete programs for a
                 register machine. The described implementation enables
                 the use of most program constructs, such as arithmetic
                 operators, large indexed memory, automatic
                 decomposition into subfunctions and subroutines (ADFs),
                 conditional constructs i.e. if-then-else, jumps, loop
                 structures, recursion, protected functions, string and
                 list functions. Any C-function can be compiled and
                 linked into the function set of the system. The use of
                 register machine language allows us to work at the
                 lowest level of binary machine code without any
                 interpreting steps. In a von Neumann machine, programs
                 and data reside in the same memory and the genetic
                 operators can thus directly manipulate the binary
                 machine code in memory. The genetic operators
                 themselves are written in C-language but they modify
                 individuals in binary representation. The result is an
                 execution speed enhancement of up to 100 times compared
                 to an interpreting C-language implementation, and up to
                 2000 times compared to a LISP implementation. The use
                 of binary machine code demands a very compact coding

                 ",
}

@InProceedings{nordin:1995:introns,
  author =       "Peter Nordin and Frank Francone and Wolfgang Banzhaf",
  title =        "Explicitly Defined Introns and Destructive Crossover
                 in Genetic Programming",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "6--22",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/ML95.ps.gz",
  size =         "13 pages",
  abstract =     "In Genetic Programming, introns play at least two
                 substantial roles: (1) A structural protection role,
                 allowing the population to preserve highly-fit building
                 blocks; and (2) A global protection role, enabling an
                 individual to protect itself almost entirely against
                 the destructive effect of crossover. We introduce
                 Explicitly Defined Introns into Genetic Programming.
                 Our results suggest that the introduction of Explicitly
                 Defined Introns can improve fitness , generalization,
                 and CPU time. Further, Explicitly Defined Introns
                 partially replace the role of Implicit Introns ( that
                 is, introns that emerge from crossover and mutation
                 without being explicitly defined as such). Finally,
                 Explicitly Defined Introns and Implicit Introns appear,
                 in some situations, to work in tandem to produce better
                 training, fitness and generalization than occurs
                 without Explicitly Defined Introns.",
  notes =        "Problem with ftp 26/6/95 part of rosca:1995:ml",
}

@InProceedings{nordin:1995:robot,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Genetic Programming Controlling a Miniature Robot",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "61--67",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/

                 See also nordin:1996:aigp2",
}

@InCollection{nordin:1996:aigp2,
  author =       "Peter Nordin and Frank Francone and Wolfgang Banzhaf",
  title =        "Explicitly Defined Introns and Destructive Crossover
                 in Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "111--134",
  chapter =      "6",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  notes =        "

                 ",
}

@InProceedings{nordin:1996:pcis,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Programmatic Compression of Images and Sound",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "345--350",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/gp96.ps.gz",
  size =         "6 pages",
  abstract =     "The importance of digital data compression in the
                 future media arena cannot be overestimated. A novel
                 approach to data compression is built on Genetic
                 Programming. This technique has been referred to as
                 {"}programmatic compression{"}. In this paper we apply
                 a variant of programmatic compression to the
                 compression of bitmap images and sampled digital sound.
                 The work presented here constitutes the first
                 successful result of genetic programming applied to
                 compression of real full size images and sound. A
                 compiling genetic programming system is used for
                 efficiency reasons. Different related issues are
                 discussed, such as the handling of very large fitness
                 case sets.",
  notes =        "GP-96, notes based on submitted version

                 Fitness tests divided into chunks. Reference to stack,
                 fixed variables and indexed memory (also save/restore
                 and swap). In some cases fitness based upon frequency
                 domain. ADFs did not {"}significantly improve
                 results{"}. Later {"}chunks{"} (fitness cases)
                 population seeded (2 methods used).",
  size =         "9 pages",
}

@Unpublished{nordin:1996:iric-paint,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Image recognition and image encoding using paint
                 primitives and genetic programming",
  note =         "publication details missing",
  year =         "1996",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
  size =         "6 pages",
}

@InProceedings{nordin:1997:grep,
  author =       "Peter Nordin and Wolfgang Banzhaf",
  title =        "Genetic Reasoning Evolving Proofs with Genetic
                 Search",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "255--260",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{nordin:1997:insse,
  author =       "P. Nordin and W. Banzhaf and F. D. Francone",
  title =        "Introns in Nature and in Simulated Structure
                 Evolution",
  booktitle =    "Bio-Computation and Emergent Computation",
  year =         "1997",
  editor =       "Dan Lundh and Bjorn Olsson and Ajit Narayanan",
  address =      "Skovde, Sweden",
  month =        "1-2 " # sep,
  publisher =    "World Scientific Publishing",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "981-02-3262-4",
  URL =          "http://www.wspc.com.sg/books/lifesci/3593.html",
  abstract =     "In this study we measure the compression of
                 information in a simulated evolutionary system. We do
                 the investigation taking introns in the genome into
                 account. We mainly investigate evolution of linear
                 computer code but also present results from evolution
                 of tree structures as well as messy genetic algorithms.
                 The size of solutions is an important property of any
                 system trying to learn or adapt to its environment. The
                 results show significant compression or constant size
                 of exons during evolution - in contrast to the rapid
                 growth of overall size. Our conclusion is that an
                 built-in pressure towards low-complexity solutions is
                 measurable in several simulated evolutionary systems
                 which may account for the robust adaptation shown by
                 these systems.",
  notes =        "BCEC97

                 We change one change each in struction to a NoOperation
                 (NOP) and if this doesn't make any difference for any
                 of the fitness cases then we call it an (first order)
                 intron. For the tree-based-GP we look at what values
                 that come in to each node and what values that
                 propagates upwards from each node. If one value into
                 the node is the same as one value out from the node
                 *for all fitness cases* then the other sub-trees under
                 the node are introns.

                 The tree run was symbolic regression of a 3rd order
                 polynomial",
}

@PhdThesis{nordin:thesis,
  author =       "Peter Nordin",
  title =        "Evolutionary Program Induction of Binary Machine Code
                 and its Applications",
  school =       "der Universitat Dortmund am Fachereich Informatik",
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  size =         "290 pages",
  abstract =     "This thesis presents the Compiling Genetic Programming
                 System (CGPS) a machine learning method for automatic
                 program induction. The objective of the system is to
                 automatically produce computer programs. CGPS is the
                 marriage of two new ideas (1) a special evolutionary
                 program induction algorithm variant and (2) the use of
                 large scale meta manipulation of binary code. Both
                 ideas may have merits on their own but it is by
                 combining the two that the real benefits emerge, making
                 CGPS a powerful machine learning paradigm.",
  notes =        "(1) The evolutionary program induction method is an
                 instance of an evolutionary algorithm (EA) a class of
                 algorithms that borrow metaphors from biology,
                 evolution and natural selection. It uses a linear
                 program representation in contrast to other well used
                 methods such as Koza's genetic programming (GP)
                 approach which has a hierarchical tree-based program
                 structure. One way to view CGPS is as a large alphabet
                 genetic algorithm (GA) where each letter in the
                 alphabet corresponds to a syntactically closed computer
                 program structure. A letter in the GA could for example
                 be a line in a computer program i.e. an assignment a :=
                 a + 1. CGPS uses recombination (crossover) in between
                 letters which guarantees syntactic closure during
                 evolution. However, from a GA point of view, the letter
                 in the linear string normally does not have an internal
                 structure. A program line in a computer language the
                 program or a machine code instruction have plenty of
                 internal structure determining the operation to be
                 performed and the operands used. A better metaphor
                 could therefore be the gene concept in nature. Genes in
                 DNA are syntactically closed sequences providing the
                 recipe of a protein. As in CGPS, crossover normally
                 acts between genes/instructions preserving the
                 syntactic closure of the object. There is a mutation
                 operator, to produce variation within the internal
                 structure of the gene/instruction. In CGPS, the
                 mutation operator changes the syntactically closed
                 program object into another syntactically closed
                 program object it replaces a valid letter with another
                 valid letter in the GA alphabet. CGPS also uses two
                 significant features in the program individual (genome)
                 which is the header and the footer. The header is a
                 prefix part of the individual while the footer is the
                 suffix part. The genetic operators prevent the header
                 and the footer from being changed, and the header and
                 the footer are used to ensure syntactic closure of the
                 whole individual.",
  notes =        "(2) CGPS in this thesis is mostly applied to program
                 induction of binary machine code. Binary machine code
                 is the code in the computer that is directly executed
                 by the processor. The program individual in CGPS is a
                 binary machine code function and the genetic operators
                 operate directly on the binary machine code. This
                 implies that CGPS is a meta-manipulating program. In
                 low level programming, a meta-manipulating program is a
                 program that changes its own binary code or the binary
                 code of another program. Usually, this only means
                 changing a few bytes e.g. dynamic linking of a
                 function. However, CGPS is the first real instance of a
                 large scale meta manipulating program which constantly
                 manipulates, shuffles around and changes large chunks
                 of binary code as a part of the learning process. So,
                 the output from CGPS is a binary machine code program
                 that can be executed directly by the processor. The
                 meta manipulation of the individual (genome) is done
                 with the evolutionary algorithm, mentioned in (1)
                 above. The headers and footers are used to make sure
                 that the individual always preserves syntactic closure
                 during evolution, no matter what the genetic operators
                 do with the code in between.",
  notes =        "The marriage of these two ideas produce a system which
                 can induce turing-complete machine code programs very
                 efficiently. The system also has several other
                 attractive properties, such as constant memory usage,
                 compact representation and uncomplicated memory
                 management. In this thesis the background to CGPS is
                 presented together with other evolutionary algorithms
                 and other evolutionary program induction techniques.
                 The detailed description of CGPS and its implementation
                 is described together with several evaluations or case
                 studies in different feasible domains such as robotic
                 control, image processing and natural language
                 processing. Some of the evaluations make comparisons to
                 other machine learning algorithms; neural networks and
                 hierarchical genetic programming. The formal definition
                 of CGPS is given followed by aninvestigation of
                 generalization in variable length evolutionary
                 algorithms. Finally, several possible directions for
                 the future of CGPS research are presented.",
  notes =        "Tel/Fax +49 231 261550 email: krehl-verlag@t-online.de
                 Price: 68DM

                 * Orders within Germany can easily order in any
                 bookstore (DM 68,-), although firms/institutions or
                 (Master or Visa) credit card owners also can order
                 directly (shipping and credit card service charge
                 included).

                 * International Sales are possible to Master or
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                 - order should arrive via FAX (+49 2501 261550) or
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                 payment data (we are working on secure order via WWW
                 but have no final timeline for this now) The order
                 should mention credit card type, number and expiration
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                 - Beside the cost of the book (w/o VAT), i.e. DM 68,-
                 minus 7% the interested party has to decide about
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                 request for different areas, e.g. USA DM 3.50 for 3-5
                 weeks delivery or DM 24,-- for 1-2 weeks delivery). The
                 total amount is drawn from the credit card in german
                 currency.",
  notes =        "Table of Contents

                 1 Introduction 21

                 1.1 Evolutionary Algorithms 22

                 1.2 Genetic Programming 25

                 1.3 Machine Language Genetic Programming 31

                 1.4 Introduction to CGPS 38

                 I Implementation 47

                 2 Computer Hardware 49

                 2.1 Introduction to Computer Hardware50

                 2.2 Von Neumann Computers 50

                 3 The SPARC Architecture and CGPS 55

                 3.1 Fundamentals of CGPS 56

                 3.2 Implementation 58

                 3.3 Floating Point Instructions 69

                 3.4 Self Modifying Code in C language 69

                 3.5 How to Call a Self|made Function from C 69

                 3.6 Genetic Operators 70

                 3.7 Initialisation 72

                 4 Additional Features of the System 75

                 4.1 Leaf Procedures and Primitives 77

                 4.2 Memory in Tree-Based GP 78

                 4.3 Conditionals 80

                 4.4 Automatically Defined Subroutines in CGPS 80

                 4.5 Leaf Procedure Examples 84

                 4.6 Loops and Recursion in Tree-Based GP 85

                 4.7 Loops and Recursion in CGPS 88

                 4.8 Loop Example in CGPS 88

                 4.9 External Functions 91

                 4.10 Strings and Lists 91

                 4.11 Parameters to the System 92

                 4.12 C-language Output 93

                 4.13 Platforms for CGPS 94

                 4.14 Portability Methods 94

                 4.15 Caveats 94

                 4.16 How to Get Started 95

                 4.17 Using Tree Representation 96

                 4.18 Speed Evaluation 97

                 4.19 Why is Binary Manipulation so Fast? 98

                 4.20 Applications 99

                 5 A Walkthrough of an Example System101

                 5.1 Variables Constants and Parameters 102

                 5.2 Random Number Generation 104

                 5.3 Initialisation 107

                 5.4 Output Functions 107

                 5.5 The Fitness Function 108

                 5.6 Reproduce an Individual 109

                 5.7 Crossover 110

                 5.8 Mutation 110

                 5.9 Tournament Selection 111

                 5.10 Read Training Data 111

                 5.11 The Main Procedure 112

                 II Evaluations 115

                 6 On-line CGPS 117

                 6.1 Background 118

                 6.2 Introduction 119

                 6.3 Methods 122

                 6.4 Results 130

                 6.5 Conclusions of On-line CGPS 135

                 7 Control Using Memory of Past Events 139

                 7.1 Introduction 140

                 7.2 The Evolutionary Algorithm 140

                 7.3 Setup 141

                 7.4 Objectives 141

                 7.5 The Memory Based GP Control Architecture 142

                 7.6 Results 146

                 7.7 Future Directions of Memory Based GP in Control
                 152

                 7.8 Summary 153

                 8 High-performance Applications 155

                 8.1 Historic Remarks on CGPS and Image Coding 156

                 8.2 Introduction 156

                 8.3 Implementation 157

                 8.4 Results 159

                 8.5 Summary and Conclusions 161

                 9 CGPS and Programmatic Compression 163

                 9.1 Introduction 164

                 9.2 Programmatic Compression (PC) 164

                 9.3 Compression of Sound 166

                 9.4 Compression of Pictures 171

                 9.5 Summary and Conclusion 173

                 10 CGPS and Tree-Based GP 175

                 10.1 Introduction 176

                 10.2 Methods 176

                 10.3 The Evolutionary Algorithm 177

                 10.4 Results 177

                 10.5 Discussion and Conclusions 177

                 11 Neural Networks and CGPS 179

                 11.1 The Sample Problem 180

                 11.2 The Neural Network180

                 11.3 Results 182

                 11.4 Summary 183

                 12 Neural Networks and Generalisation 185

                 12.1 The Problems Used In This Study 186

                 12.2 Classication as Symbolic Regression 187

                 12.3 Introns and Explicitly Defined Introns 187

                 12.4 Results 187

                 12.5 Summary 190

                 III Explanation 193

                 13 Formalisation of CGPS 195

                 13.1 Register Machines 196

                 13.2 Evolutionary Algorithms 199

                 13.3 Compiling Genetic Programming 203

                 14 Complexity, Compression and Evolution 205

                 14.1 Introduction 206

                 14.2 Complexity, Effective Fitness and Evolution
                 209

                 14.3 Example 212

                 14.4 Kolmogorov Complexity and Generalisation 217

                 14.5 Empirical Results 220

                 14.6 Other Evolutionary Techniques 223

                 14.7 Summary and Conclusions 223

                 15 Explicitly Defined Introns 229

                 15.1 Introduction 230

                 15.2 Definitions 232

                 15.3 The Experimental Setup 233

                 15.4 Results 235

                 16 Conclusions and Outline of Future Perspectives
                 245

                 16.1 A Computer Language for Meta-manipulation 246

                 16.2 Typed GP, Constrained Crossover, Grammar and
                 Search Bias 247

                 16.3 Other Machine Learning Algorithms 251

                 16.4 Special Processors 252

                 16.5 Large Number of Input Parameters 253

                 16.6 Reasoning about Machine Code Programs with a Meta
                 GP System 254

                 16.7 The Logic of Genetic Reasoning 258

                 16.8 Some Brief Initial Results 259

                 Appendix A: Flow Charts of CGPS 263",
  publisher =    "Krehl Verlag",
  publisher_address = "Postfach 51 01 42, D-48163 Muenster, GERMANY",
  ISBN =         "3-931546-07-1",
}

@InProceedings{nordin:1998:AIMGP:lb,
  author =       "Peter Nordin",
  title =        "{AIMGP}: {A} Formal Description",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Article{Nordin:1998:RAS,
  author =       "Peter Nordin and Wolfgang Banzhaf and Markus
                 Brameier",
  title =        "Evolution of a world model for a miniature robot using
                 genetic programming",
  journal =      "Robotics and Autonomous Systems",
  volume =       "25",
  pages =        "105--116",
  year =         "1998",
  number =       "1-2",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V16-3VSPF6D-7/1/883b0d9e78af0fc4f70e997adb715e89",
  abstract =     "We have used an automatic programming method called
                 genetic programming (GP) for control of a miniature
                 robot. Our earlier work on real-time learning suffered
                 from the drawback of the learning time being limited by
                 the response dynamics of the robot's environment. In
                 order to overcome this problem we have devised a new
                 technique which allows learning from past experiences
                 that are stored in memory. The new method shows its
                 advantage when perfect behavior emerges in experiments
                 quickly and reliably. It is tested on two control
                 tasks, obstacle avoiding and wall following behavior,
                 both in simulation and on the real robot platform
                 Khepera.",
}

@InCollection{nordin:1999:aigp3,
  author =       "Peter Nordin and Wolfgang Banzhaf and Frank D.
                 Francone",
  title =        "Efficient Evolution of Machine Code for {CISC}
                 Architectures using Instruction Blocks and Homologous
                 Crossover",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  pages =        "275--299",
  chapter =      "12",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{nordin:1999:greimp,
  author =       "Peter Nordin and Anders Eriksson and Mats Nordahl",
  title =        "Genetic Reasoning: Evolutionary Induction of
                 Mathematical Proofs",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "221--231",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@InProceedings{nordin:1999:fogp,
  author =       "Peter Nordin and Wolfgang Banzhaf and Frank D.
                 Francone",
  title =        "Compression of Effective Size in Genetic Programming",
  booktitle =    "Foundations of Genetic Programming",
  year =         "1999",
  editor =       "Thomas Haynes and William B. Langdon and Una-May
                 O'Reilly and Riccardo Poli and Justinian Rosca",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/nordin.ps.gz",
  size =         "4 pages",
  notes =        "GECCO'99 WKSHOP, part of haynes:1999:fogp",
}

@InProceedings{nordin:1999:AP,
  author =       "Peter Nordin and Frank Hoffmann and Frank D. Francone
                 and Markus Brameier and Wolfgang Banzhaf",
  title =        "{AIM}-{GP} and Parallelism",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1059--1066",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, parallel and
                 distributed processing",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@Article{nordin:1999:gp3,
  author =       "Peter Nordin",
  title =        "Genetic Programming {III} - Darwinian Invention and
                 Problem Solving",
  journal =      "Evolutionary Computation",
  year =         "1999",
  volume =       "7",
  number =       "4",
  pages =        "451--453",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1063-6560",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=7&issue=4",
  size =         "2.3 pages",
  notes =        "Review of koza:gp3",
}

@InProceedings{nordin:1999:cimfa,
  author =       "Peter Nordin and Mats Nordahl",
  title =        "{ELVIS}: An Evolutionary Architecture For {A} Humanoid
                 Robot",
  booktitle =    "Proceeding of Symposium on Artificial Intelligence
                 (CIMAF99)",
  year =         "1999",
  editor =       "Alberto A. Ochoa Rodriguez and Marta R. Soto Ortiz and
                 Roberto Santana Hermida",
  address =      "Havanna, Cuba",
  email =        "Mrs. Urbia Vila <urbia@cidet.icmf.inf.cu>",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "959-02-0241-1",
  notes =        "CIMAF99
                 http://members.tripod.com/~Adaptive_Systems_99/Procc.html",
}

@InProceedings{nordin:1999:arob,
  author =       "Peter Nordin and Mats Nordahl",
  title =        "An Evolutionary Architecture For {A} Humanoid Robot",
  booktitle =    "Proceedings 4th International Symposium on Artificial
                 Life and Robotics",
  year =         "1999",
  editor =       "M. Sugisaka",
  address =      "B-Con Plaza, Beppu, Oita, Japan",
  month =        "19-22 " # jan,
  organisation = "Oita University",
  email =        "arobsecr@cc.oita-u.ac.jp",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AROBF99
                 http://arob.cc.oita-u.ac.jp/AROB99/AROB99E.html",
}

@InProceedings{numata:1998:tspGP,
  author =       "Makoto Numata and Ken Sugawara and Ikuo Yoshihara and
                 Kenichi Abe",
  title =        "Time Series Prediction by Genetic Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{numata:1999:tspGP,
  author =       "M. Numata and K. Sugawara and S. Yamada and I.
                 Yoshihara and K. Abe",
  title =        "Time series prediction modeling by genetic programming
                 without inheritance of model parameters",
  booktitle =    "Proceedings 4th International Symposium on Artificial
                 Life and Robotics",
  year =         "1999",
  editor =       "M. Sugisaka",
  address =      "B-Con Plaza, Beppu, Oita, Japan",
  month =        "19-22 " # jan,
  organisation = "Oita University",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AROB'99 Details from www site etc",
}

@InProceedings{numata:1999:G,
  author =       "M. Numata and I. Yoshihara and M. Yoshizawa and K.
                 Abe",
  title =        "{GP}-based heart rate prediction for artificial heart
                 control",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "193--197",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, Genetic Programming, back
                 propergation-like",
  notes =        "GECCO-99LB",
}

@Article{Nyongesa:2001:aampfd,
  author =       "H. O. Nyongesa and S. Kent and R. O'Keefe",
  title =        "Genetic programming for anti-air missile proximity
                 fuze delay-time algorithms",
  journal =      "IEEE Aerospace and Electronics Systems Magazine",
  year =         "2001",
  volume =       "16",
  number =       "1",
  pages =        "41--45",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, missile guidance, detonation, timing,
                 aerospace simulation, learning automata, delay-time
                 algorithms, anti-air missiles, proximity fuzes, warhead
                 detonation, evolutionary optimization, automated timing
                 determination, simulation study",
  URL =          "http://ieeexplore.ieee.org/iel5/62/19347/00894177.pdf",
  abstract =     "This paper describes the application of genetic
                 programming to delay-time algorithms for anti-air
                 missiles equipped with proximity fuzes. Current
                 algorithms for determining the delay-time before the
                 detonation of a missile warhead rely on human effort
                 and experience and are, in general, deficient. We show
                 that by applying genetic programming, an evolutionary
                 optimization technique, determination of the timing can
                 be automated and made near-optimal. A simulation study
                 is discussed.",
}

@InProceedings{nyongesa:2001:EvoWorks,
  author =       "Henry O. Nyongesa",
  title =        "Generation of Time-Delay Algorithms for Anti-air
                 Missiles Using Genetic Programming",
  booktitle =    "Applications of Evolutionary Computing",
  year =         "2001",
  editor =       "Egbert J. W. Boers and Stefano Cagnoni and Jens
                 Gottlieb and Emma Hart and Pier Luca Lanzi and Gunther
                 R. Raidl and Robert E. Smith and Harald Tijink",
  volume =       "2037",
  series =       "LNCS",
  pages =        "243--247",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-41920-9",
  notes =        "EvoWorkshops2001",
}

@InCollection{kinnear:oakley,
  author =       "Howard Oakley",
  title =        "Two Scientific Applications of Genetic Programming:
                 Stack Filters and Non-Linear Equation Fitting to
                 Chaotic Data",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "369--389",
  chapter =      "17",
  size =         "~16 pages",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Two Scientific Applications of Genetic Programming:
                 The development of stack filters, the fitting of
                 non-linear equations to chaotic data

                 Mackey-Glass, REAL World examples. Contrasts with other
                 techniques eg GA.

                 ",
}

@TechReport{Oakley:INM,
  author =       "E H N Oakley",
  title =        "Signal Filtering and data Processing for Laser
                 Rheometry",
  institution =  "Institute of Naval Medicine",
  address =      "Portsmouth, UK",
  year =         "1993",
  notes =        "June 1993 Report no R930??? See also Chapter 17 in
                 Kinnear. Graycoding not particularly effective.

                 ",
  keywords =     "genetic algorithms, genetic programming",
  size =         "~30 pages",
}

@InProceedings{oakley:1994:sncds,
  author =       "E. H. N. Oakley",
  title =        "The application of genetic programming to the
                 investigation of short, noisy, chaotic data series",
  booktitle =    "Evolutionary Computing",
  publisher =    "Springer-Verlag",
  year =         "1994",
  editor =       "T. C. Fogarty",
  series =       "Lecture Notes in Computer Science",
  address =      "Leeds, UK",
  month =        "11-13 " # apr,
  organisation = "AISB",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.io.com/~ftp/genetic-programming/papers/OAKPAPRS.TARR.GZ",
  size =         "10 pages",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Proceedings of the Workshop on Artificial Intelligence
                 and Simulation of Behaviour Workshop on Evolutionary
                 Computing. Workshop in Leeds, UK, April 11-13,
                 1994

                 OAKPAPRS.TARR.GZ contains: AISBLNCS.TEX - TeX format,
                 but without the illustrations. If you want to see the
                 pictures, then you must get hold of a copy of the
                 original.",
}

@InProceedings{oakley:1995:chaos,
  author =       "E. Howard N. Oakley",
  title =        "Genetic Programming as a Means of Assessing and
                 Reflecting Chaos",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "68--72",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.io.com/~ftp/genetic-programming/papers/OAKPAPRS.TARR.GZ",
  notes =        "AAAI-95f GP {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/

                 OAKPAPRS.TARR.GZ contains: AAAIFALL.RTF - RTF format.
                 Some of the formatting (and illustration) data is
                 corrupted, but this should be usable.

                 AAAIFALL.TXT - text only format.",
}

@InProceedings{oakley:1996:GPrcb,
  author =       "E. Howard N. Oakley",
  title =        "Genetic Programming, the Reflection of Chaos, and the
                 Bootstrap: Towards a useful Test for Chaos",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "175--181",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.quercus.demon.co.uk/oakgp96.pdf",
  size =         "9 pages",
  notes =        "GP-96",
}

@InCollection{oakley:1997:HECsf,
  author =       "E. H. N. Oakley",
  title =        "Genetic programming for stack filters",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and D. B. Fogel and Z. Michalewicz",
  chapter =      "section G3.2",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  notes =        "http://bookmark.iop.org/bookpge.htm?&book=386h#top",
}

@InCollection{oakley:1997:HECcd,
  author =       "E. H. N. Oakley",
  title =        "Genetic programming for non-linear equation fitting to
                 chaotic data",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and D. B. Fogel and Z. Michalewicz",
  chapter =      "section G4.5",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  notes =        "http://bookmark.iop.org/bookpge.htm?&book=386h#top",
}

@InProceedings{oates:1999:VEPCADDMP,
  author =       "Martin Oates and David Corne and Roger Loader",
  title =        "Variation in {EA} Performance Characteristics on the
                 Adaptive Distributed Database Management Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "480--487",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{oates:1999:T,
  author =       "Martin Oates and David Corne and Brian C. H. Turton",
  title =        "The effects of selection pressure on parameter choice
                 in evolutionary search",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "198--203",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{obayashi:1999:ECSWSO,
  author =       "Shigeru Obayashi and Daisuke Sasaki and Yukihiro
                 Takeguchi",
  title =        "Evolutionary Computation of Supersonic Wing Shape
                 Optimization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1791",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ochoa:1999:OROMR,
  author =       "Gabriela Ochoa and Inman Harvey and Hilary Buxton",
  title =        "On Recombination and Optimal Mutation Rates",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "488--495",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{odriscoll:2002:gecco:workshop,
  title =        "Synthesising Edge Detectors with Grammatical
                 Evolution",
  author =       "Marianne O'Driscoll and Stephen McKenna and J. J.
                 Collins",
  pages =        "137--140",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InCollection{oestreich:1998:FPUAMGP,
  author =       "Nathan Oestreich",
  title =        "Formulating Proofs in Universal Algebra Model with
                 Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "118--127",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{ogihara:1998:DNAspbbc,
  author =       "Mitsunori Ogihara and Animesh Ray",
  title =        "{DNA}-Based Self-Propagating Algorithm for Solving
                 Bounded-Fan-In Boolean Circuits",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "725--730",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{ogihara:1999:RMMDCBC,
  author =       "Mitsunori Ogihara",
  title =        "Relating the Minimum Model for {DNA} Computation and
                 Boolean Circuits",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1817--1821",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "dna and molecular computing",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{oh:1999:EBBOSIPDG,
  author =       "Jae C. Oh",
  title =        "Effects of ``Physical Body'' on Biased Opponent
                 Selection in the Iterated Prisoner's Dilemma Game",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1447",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ok:2001:eblgpapr,
  author =       "Sooyol Ok and Kazuo Miyashita and Kazunor Hase",
  title =        "Evolving Bipedal Locomotion with Genetic Programming
                 --- {A} Preliminary Report ---",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1025--1032",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Neural
                 Oscillators, Bipedal Walking, Central Pattern
                 Generator(CPG), Feedback Network",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 lil-gp. Redhat linux cluster machine",
}

@InProceedings{olague:1998:cndns,
  author =       "Gustavo Olague and Roger Mohr",
  title =        "Camera Network Design by Natural Selection",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "567",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{olague:1999:AGACAMPCV,
  author =       "Gustavo Olague",
  title =        "Analysis of Genetic Algorithms Convergence Applied to
                 Mensuration Problems in Computer Vision",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1792",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{oliveira2:2001:gecco,
  title =        "Using Heuristics Related to Cellular Automata Behavior
                 Forecast to Improve Genetic Search for a Grouping
                 Task",
  author =       "Gina M. B. Oliveira and Pedro P. B. de Oliveira and
                 Nizam Omar",
  pages =        "181",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 Cellular Automata, Cellular Programming, Dynamic
                 Behavior, Forecast, Parameter, Grouping Task, Genetic
                 Algorithms",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{oliver-morales:2002:gecco,
  author =       "Carlos Oliver-Morales and Katya Rodr{\'\i}guez
                 V{\'a}zquez",
  title =        "{MB GP} In Modelling And Prediction",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "892",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, poster paper,
                 encoding, modelling, prediction",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{olmer:1996:ertbm,
  author =       "Olmer and Banzhaf and Nordin",
  title =        "Evolving Real-time Behavior Modules for a real Robot
                 with Genetic Programming",
  booktitle =    "Proceedings of the international symposium on robotics
                 and manufacturing",
  year =         "1996",
  address =      "Montpellier, France",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ISRAM 96, We have done some experiments combining GP
                 and a subsumption architecture for robotic control. The
                 system controlled a Khepera robot learning four
                 different behaviors.",
}

@InProceedings{olsson:1999:UEADPF,
  author =       "Bjorn Olsson",
  title =        "Using Evolutionary Algorithms in the Design of Protein
                 Fingerprints",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1636--1642",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{olsson96,
  title =        "Optimization Using {A} Host-Parasite Model with
                 Variable-Size Distributed Populations",
  author =       "Bj{\"{o}}rn Olsson",
  year =         "1996",
  booktitle =    "Proceedings of the 1996 IEEE 3rd International
                 Conference on Evolutionary Computation",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, coevolution",
  size =         "5 pages",
  notes =        "

                 ",
}

@InProceedings{olsson98c,
  author =       "Bjorn Olsson",
  title =        "Evaluation of a Simple Host-Parasite Genetic
                 Algorithm",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Ann ual Conference on Evolutionary
                 Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, coevolution, SHPGA",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 alife92:hillis sorting networks, 1D cellular automata,
                 tic-tac-toe endgame",
}

@InProceedings{olsson98d,
  author =       "Bjorn Olsson",
  title =        "A Host-Parasite Genetic Algorithm for Asymmetric
                 Tasks",
  booktitle =    "Machine Learning: ECML-98",
  year =         "1998",
  editor =       "C. N\'{e}dellec and C. Rouveirol",
  address =      "Dorint-Parkhotel, Chemnitz, Germany",
  month =        "21-24 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, coevolution, SHPGA",
  notes =        "ECML-98",
}

@Misc{olsson:1999:SEEMGP,
  author =       "Lars Olsson",
  title =        "Strategy Evolution for Electronic Markets using
                 Genetic Programming",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, agents,
                 economics",
  URL =          "http://www.sics.se/~larre/thesis/poster.ps",
}

@PhdThesis{olsson:1994:thesis,
  author =       "J. R. Olsson",
  title =        "Inductive functional programming using incremental
                 program transformation and Execution of logic programs
                 by iterative-deepening {A}* {SLD}-tree search",
  school =       "University of Oslo",
  year =         "1994",
  type =         "Dr scient thesis",
  address =      "Norway",
  URL =          "http://www.ia-stud.hiof.no/~rolando/01-report.ps.Z",
  ISBN =         "82-7368-099-1",
  size =         "156 pages",
  notes =        "Research report 189",
}

@Article{olsson:1995:AI,
  author =       "Roland Olsson",
  title =        "Inductive functional programming using incremental
                 program transformation",
  journal =      "Artificial Intelligence",
  year =         "1995",
  volume =       "74",
  number =       "1",
  pages =        "55--81",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, ADATE, STGP",
  URL =          "http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6TYF-4002FJH-9-1&_cdi=5617&_orig=browse&_coverDate=03%2F31%2F1995&_sk=999259998&wchp=dGLbVlb-lSzBV&_acct=C000010182&_version=1&_userid=125795&md5=ba5db57b3fa83d990440da8dfd8afcd7&ie=f.pdf",
  URL =          "http://www.ia-stud.hiof.no/~rolando/art_int_paper_74.ps",
  size =         "27 pages",
  abstract =     "The paper presents a system, ADATE, for automatic
                 functional programming. ADATE uses specifications that
                 contain few constraints on the programs to be
                 synthesised and that allow a wide range of correct
                 programs. ADATE can generate novel and unexpected
                 recursive programs with automatic invention of
                 recursive auxiliary functions. Successively better
                 programs are developed using incremental program
                 transformations. A key to the success of ADATE is the
                 exact design of these transformations and how to
                 systematically search for appropriate transformation
                 sequences.",
  notes =        "heuristically guided enumeration. call count limit
                 (recursive calls). Example 2.2 simplification of
                 polynomials. Syntactic complexity = sum log_2
                 m.

                 Several references to automatic program invention (most
                 using LISP) from 1976 to 1992.",
}

@InProceedings{olsson:1998:pmadate,
  author =       "Roland Olsson",
  title =        "Population Management for Automatic Design of
                 Algorithms through Evolution",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "592--597",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, ADATE",
  URL =          "http://www.ia-stud.hiof.no/~rolando/popart3.ps",
  file =         "c102.pdf",
  size =         "6 pages",
  abstract =     "The paper describes population based search in the
                 system Automatic Design of Algorithms through Evolution
                 (ADATE) that maintains chains of gradually bigger and
                 better programs. The main challenge is to avoid missing
                 links that lead to entrapment in too bad local optima.
                 To avoid entrapment, the ADATE system employs iterative
                 re-expansion of programs, population maintenance using
                 a syntactic complexity / evaluation value ordering
                 scheme and three different diversification methods that
                 strive to avoid too similar programs. When combined
                 with the general program transformations explained in a
                 previous paper, these techniques enable ADATE to
                 synthesize recursive programs with automatic invention
                 of recursive help functions. We also briefly present
                 experimental results supporting that automatic
                 synthesis of complex programs from {"}first
                 principles{"} is possible indeed, but only if vast
                 computational resources are employed effectively.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence

                 Solutions to, Reversing a list, list delete min,
                 cartesian product, intersecting two lists, string
                 comparison, sorting a list, locating a substring, BST
                 insertion, binary multiplication, simplifying a
                 polynomial, transposing a matrix, permutation
                 generation, BST deletion, Path finding, Binary
                 addition, Rectangle intersection",
}

@InProceedings{olsson:1998:awsADATE,
  author =       "J. Roland Olsson",
  title =        "The Art of Writing Specifications for the {ADATE}
                 Automatic Programming System",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "278--283",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, ADATE",
  ISBN =         "1-55860-548-7",
  URL =          "http://www.ia-stud.hiof.no/~rolando/specart5.ps",
  notes =        "GP-98",
}

@InProceedings{olsson:1999:htif,
  author =       "J. Roland Olsson",
  title =        "How to Invent Functions",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "232--243",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, ADATE",
  ISBN =         "3-540-65899-8",
  URL =          "http://www.ia-stud.hiof.no/~rolando/abstrart1.ps",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Gene duplication. ADFs and other forms of abstraction
                 added to ADATE",
}

@InProceedings{olsson:2002:sigp,
  author =       "Roland Olsson",
  title =        "Self Improving Genetic Programming",
  booktitle =    "Advances in Intelligent Systems, Fuzzy Systems,
                 evolutionary Computation",
  year =         "2002",
  pages =        "262--266",
  month =        feb,
  publisher =    "WSEAS Press",
  keywords =     "genetic algorithms, genetic programming, ADATE",
  ISBN =         "960-8052-61-0",
  notes =        "http://www.wseas.org/8052610.doc 23 June 2002
                 http://www.wseas.org/New_Books.htm

                 http://www.ia-stud.hiof.no/~rolando/sig-adate.ps",
}

@InProceedings{olson:2002:gecco,
  author =       "Ronald Olsson and Brock Wilcox",
  title =        "Self-improvement For The {ADATE} Automatic Programming
                 System",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "893",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, poster paper,
                 automatic programming, mutation method, neutral
                 evolution, self-improvement, ADATE",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@Misc{oltean:2002:MEP,
  author =       "Mihai Oltean and D. Dumitrescu",
  title =        "Multi Expression Programming",
  year =         "2002",
  month =        May,
  note =         "submitted",
  email =        "ddumitr@nessie.cs.ubbcluj.ro",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Computation, Multi Expression Programming, linear
                 representation, Tic-Tac-Toe, symbolic regression, game
                 strategy, heuristics generation.",
  URL =          "http://www.mep.cs.ubbcluj.ro/oltean.pdf",
  size =         "32 pages",
  abstract =     "In this paper a new evolutionary paradigm, called
                 Multi-Expression Programming (MEP), intended for
                 solving computationally difficult problems is proposed.
                 A new encoding method is designed. MEP individuals are
                 linear entities that encode complex computer programs.
                 In this paper MEP is used for solving some
                 computationally difficult problems like symbolic
                 regression, game strategy discovering, and for
                 generating heuristics. Other exciting applications of
                 MEP are suggested. Some of them are currently under
                 development. MEP is compared with Gene Expression
                 Programming (GEP) by using a well-known test problem.
                 For the considered problems MEP performs better than
                 GEP.",
  notes =        "Note critisism on GP-list of {"}MEP better than GEP{"}
                 cf. ferreira:2001:CS 15 May 2002",
}

@InProceedings{oneill:1999:UHGE,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Under the Hood of Grammatical Evolution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1143--1148",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{oneill:1999:em-lcCp,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Evolving Multi-Line Compilable {C} Programs",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "83--92",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Chromosome is binary string. Interpretted as
                 Backus-Naur formal grammar. Rules of grammar are
                 prespecified to yeild very limited C program.
                 Demonstrated on modified Santa Fe trail Ant problem.",
}

@InProceedings{oneill:1999:aghlfea,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Automatic Generation of High Level Functions using
                 Evolutionary Algorithms",
  booktitle =    "Proceedings of the 1st International Workshop on Soft
                 Computing Applied to Software Enineering",
  year =         "1999",
  editor =       "Conor Ryan and Jim Buckley",
  pages =        "21--29",
  address =      "University of Limerick, Ireland",
  month =        "12-14 " # apr,
  organisation = "SCARE",
  publisher =    "Limerick University Press",
  keywords =     "genetic algorithms, genetic programming, gramatical
                 evolution",
  ISBN =         "1-874653-52-6",
  notes =        "http://scare.csis.ul.ie/scase99/ SCASE'99",
}

@InProceedings{oneill:1999:AGCA,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Automatic Generation of Caching Algorithms",
  booktitle =    "Evolutionary Algorithms in Engineering and Computer
                 Science",
  year =         "1999",
  editor =       "Kaisa Miettinen and Marko M. Mäkelä and Pekka
                 Neittaanmäki and Jacques Periaux",
  pages =        "127--134",
  address =      "Jyväskylä, Finland",
  publisher_address = "Chichester, UK",
  month =        "30 " # may # " - 3 " # jun,
  publisher =    "John Wiley \& Sons",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 Evolution",
  URL =          "http://www.mit.jyu.fi/eurogen99/papers/oneill.ps",
  notes =        "EUROGEN'99 Claims performance better than humann coded
                 {"}Least Recently Used{"} LRU algorithm. Provides
                 indexed memory but evolved solutions dont use it. Test
                 data like Paterson:1997:ecacGP",
}

@Misc{oneill:1999:APGE,
  author =       "Michael O'Neill",
  title =        "Automatic Programming with Grammatical Evolution",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithm, genetic programming",
  URL =          "http://shine.csis.ul.ie/papers/gradworkshop99/gradworkshop99.html",
}

@InProceedings{oneill:1999:ECAL,
  author =       "M. O'Neill and C. Ryan",
  title =        "Genetic Code Degeneracy: Implications for Grammatical
                 Evolution and Beyond",
  booktitle =    "Advances in Artificial Life",
  year =         "1999",
  editor =       "D. Floreano and J.-D. Nicoud and F. Mondada",
  volume =       "1674",
  series =       "LNAI",
  pages =        "149",
  address =      "Lausanne",
  month =        "13-17 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-66452-1",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66452-1",
  notes =        "ECAL-99",
}

@InProceedings{oneill:2000:xGEso,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Crossover in Grammatical Evolution: {A} Smooth
                 Operator?",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "149--162",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "Grammatical Evolution is an evolutionary algorithm
                 which can produce code in any language, requiring as
                 inputs a BNF grammar definition describing the output
                 language, and the fitness function. The usefulness of
                 crossover in GP systems has been hotly debated for some
                 time, and this debate has also arisen with respect to
                 Grammatical Evolution. This paper serves to analyse the
                 crossover operator in our algorithm by comparing the
                 performance of a variety of crossover operators.
                 Results show that the standard one point crossover
                 employed by Grammatical Evolution is not as destructive
                 as it might originally appear, and is useful in
                 performing a global search over the course of entire
                 runs. This is attributed to the fact that prior to the
                 crossover event the parent chromosomes undergo
                 alignment which facilitates the swapping of blocks
                 which are more likely to be in context.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{ONeill:2000:GECCO,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Grammar based function definition in Grammatical
                 Evolution",
  pages =        "485--490",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{o'neill:2000:I,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Incorporating gene expression models into evolutionary
                 algorithms",
  booktitle =    "Gene Expression: the Missing Link in Evolutionary",
  year =         "2000",
  pages =        "167--172",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{oneill:2001:EuroGP,
  author =       "Michael O'Neill and Conor Ryan and Maarten Keijzer and
                 Mike Cattolico",
  title =        "Crossover in Grammatical Evolution: The Search
                 Continues",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "337--347",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Crossover, Genotype-Phenotype Mapping,
                 Linear Genome, Grammar",
  ISBN =         "3-540-41899-7",
  size =         "11 pages",
  abstract =     "Grammatical Evolution is an evolutionary automatic
                 programming algorithm that can produce code in any
                 language, requiring as inputs a BNF grammar definition
                 describing the output language, and the fitness
                 function. The usefulness of crossover in GP systems has
                 been hotly debated for some time, and this debate has
                 also arisen with respect to Grammatical Evolution. This
                 paper serves to continue an analysis of the crossover
                 operator in Grammatical Evolution by looking at the
                 result of turning off crossover, and by exchanging
                 randomly generated blocks in a headless chicken-like
                 crossover. Results show that crossover in Grammatical
                 Evolution is essential on the problem domains examined.
                 The mechanism of one-point crossover in Grammatical
                 Evolution is discussed, resulting in the discovery of
                 some interesting properties that could yield an insight
                 into the operator's success.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{oneill:2001:EvoWorkshops,
  author =       "Michael O'Neill and Anthony Brabazon and Conor Ryan
                 and J. J. Collins",
  title =        "Evolving Market Index Trading Rules using Grammatical
                 Evolution",
  booktitle =    "Applications of Evolutionary Computing",
  editor =       "Egbert J. W. Boers and Stefano Cagnoni and Jens
                 Gottlieb and Emma Hart and Pier Luca Lanzi and Gunther
                 R. Raidl and Robert E. Smith and Harald Tijink",
  year =         "2001",
  volume =       "2037",
  series =       "LNCS",
  pages =        "343--352",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-19 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  ISBN =         "3-540-41920-9",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-41920-9",
  notes =        "EvoWorkshops2001. One of the better GA for stock
                 market prediction papers",
}

@Article{oneill:2001:TEC,
  author =       "Michael O'Neill and Conor Ryan",
  title =        "Grammatical Evolution",
  journal =      "IEEE Transaction on Evolutionary Compuation",
  year =         "2001",
  note =         "Forthcomming",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Crossover, Genotype-Phenotype Mapping,
                 Linear Genome, Grammar",
}

@InProceedings{o'neill2:2001:gecco,
  title =        "Grammar Defined Introns: An Investigation Into
                 Grammars, Introns, and Bias in Grammatical Evolution",
  author =       "Michael O'Neill and Conor Ryan and Miguel Nicolau",
  pages =        "97--103",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Grammars, Introns, Bias, Degenerate Genetic
                 Code",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@PhdThesis{oneill:thesis,
  author =       "Michael O'Neill",
  title =        "Automatic Programming in an Arbitrary Language:
                 Evolving Programs with Grammatical Evolution",
  school =       "University Of Limerick",
  year =         "2001",
  address =      "Ireland",
  month =        aug,
  email =        "michael.oneill@ul.ie",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  size =         "163 pages",
  abstract =     "We present a novel Evolutionary Automatic Programming
                 system, Grammatical Evolution that is capable of
                 generating programs in an arbitrary language from a
                 binary string. Grammatical Evolution adopts a genotype
                 to phenotype mapping; the genotype is the raw genetic
                 material, analogous to the DNA of Molecular Biology,
                 and the phenotype the functional program that is
                 generated (the equivalent of proteins in Molecular
                 Biology). Resulting from the genotype-phenotype
                 distinction, and inspired by Molecular Biology, a
                 number of features are introduced that result in
                 benefits for Grammatical Evolution. We demonstrate
                 Grammatical Evolution's viability on a number of proof
                 of concept problems with performance on a par with, and
                 in some cases superior to Genetic Programming. An
                 analysis of the system is conducted in which we focus
                 on a number of features arising directly from the
                 genotype-phenotype distinction, namely the degenerate
                 genetic code, and the novel, wrapping operator. We
                 conclude the investigations with an analysis of the
                 effects of the genetic operator of crossover on
                 Grammatical Evolution, before detailing our conclusions
                 and outlining directions for future research.",
}

@InProceedings{oneill:2002:gecco:workshop,
  title =        "Investigations into Memory in Grammatical Evolution",
  author =       "Michael O'Neill and Conor Ryan",
  pages =        "141--144",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InCollection{ong:emECHO,
  author =       "Stephen Ong",
  title =        "Evolving Metazoans in Echo",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "129--135",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, Holland's echo, evolving
                 reproducing agents",
  ISBN =         "0-18-182105-2",
  notes =        "

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{ono:1999:ARRGAUNDCAUCESCP,
  author =       "Isao Ono and Hajime Kita and Shigenobu Kobayashi",
  title =        "A Robust Real-Coded Genetic Algorithm using Unimodal
                 Normal Distribution Crossover Augmented by Uniform
                 Crossover: Effects of Self-Adaptation of Crossover
                 Probabilities",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "496--503",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{openshaw:1994:bnsim,
  author =       "S. Openshaw and I. Turton",
  title =        "Building New Spatial Interaction Models Using Genetic
                 Programming",
  booktitle =    "Evolutionary Computing",
  publisher =    "Springer-Verlag",
  year =         "1994",
  editor =       "T. C. Fogarty",
  series =       "Lecture Notes in Computer Science",
  address =      "Leeds, UK",
  month =        "11-13 " # apr,
  organisation = "AISB",
  keywords =     "genetic algorithms, genetic programming, FORTRAN77,
                 Cray-YMP",
  URL =          "http://gam.leeds.ac.uk/staff/i.turton/ai/aisb.ps",
  size =         "10 pages",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Proceedings of the Workshop on Artificial Intelligence
                 and Simulation of Behaviour Workshop on Evolutionary
                 Computing. Workshop in Leeds, UK, April 11-13, 1994
                 stack machine GP tried it out the results seemed
                 intresting but harder to intepret discusses some work
                 we did using stack and conventional gp.

                 Uses stack based and conventional GP to fit formulae to
                 describe geographic distance-to-work travel data. cf
                 earlier attempts to do the same using bit string GA.
                 Both GP approaches more successful than simple
                 theoretical model and ``compare well'' with more
                 complex models. Constants agumented by parameters which
                 are optimised outside the GP by nonlinear parameter
                 estimation, high CPU cost but easier for GP. Suggests
                 work needed on good ways to validate GP
                 software.

                 vector (parallel) processor",
}

@InProceedings{opitz:1999:AEAFSS,
  author =       "David W. Opitz",
  title =        "An Evolutionary Approach to Feature Set Selection",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "803",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  abstract =     "{"}improved performance over ... Adaboost and
                 Bagging{"}",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{opitz:1999:HAMAEEA,
  author =       "David W. Opitz and Subhash C. Basak and Brian D.
                 Gute",
  title =        "Hazard Assessment Modeling: An Evolutionary Ensemble
                 Approach",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1643--1650",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{oppacher:1997:GAGP,
  author =       "F. Oppacher and M. Wineberg",
  title =        "A Canonical Genetic Algorithm Based Approach to
                 Genetic Programming",
  booktitle =    "ICANNGA97",
  year =         "1997",
  address =      "University of East Anglia, Norwich, UK",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}

@InProceedings{oppacher:1999:TSBGAIGDE,
  author =       "Franz Oppacher and Mark Wineberg",
  title =        "The Shifting Balance Genetic Algorithm: Improving the
                 {GA} in a Dynamic Environment",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "504--510",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{oprea:1999:HP,
  author =       "Mihaela Oprea and Stephanie Forrest",
  title =        "How the immune system generates diversity: Pathogen
                 space coverage with random and evolved antibody
                 libraries",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1651--1656",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ppsn92:oReilly,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "An Experimental Perspective on Genetic Programming",
  booktitle =    "Parallel Problem Solving from Nature 2",
  year =         "1992",
  editor =       "R Manner and B Manderick",
  pages =        "331--340",
  address =      "Brussels, Belgium",
  month =        sep # " 28 - 30",
  publisher =    "Elsevier Science",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ai.mit.edu/people/unamay/papers//ppsn92.ps",
  size =         "10 pages",
  abstract =     "Genetic Programming (GP) has recently been introduced
                 by John R. Koza as a method for genetically breeding
                 populations of computer programs to solve problems. We
                 believe GP to constitute a significant extension of the
                 Genetic Algorithm (GA) research paradigm primarily
                 because it generalizes the genetic search techniques:
                 instead of looking for a solution to a specific
                 instance of a problem, GP attempts to evolve a program
                 capable of computing the solutions for any instance of
                 the problem. We have implemented a genetic programming
                 environment, GP*, that is capable of duplicating Koza`s
                 experiments. In this paper we describe a specific GP
                 experiment on the evolution of programs to sort
                 vectors, and discuss the issues that must be addressed
                 in any application of GP: the design of fitness
                 functions and test suites, and the selection of program
                 terminals and functions. Our observations point to
                 several previously unnoticed shortcomings of the GP
                 approach. We hypothesize that these shortcomings are
                 due to the fact that GP only uses a hierarchical
                 representation but does not construct its solutions in
                 an explicitly hierarchical manner.",
  notes =        "Critical of Koza's GP (nb non-ADF) {"}We conclude that
                 GP in its current form is heirarchical only with
                 respect to its representation and not with resepect to
                 its process of constructing solutions. This limits the
                 ability of GP to evolve complex programs from simple,
                 general functions, and makes the algorithm stongly
                 dependant on initial human design
                 decisions.{"}

                 Proposes SPECIALISE and DECOMPOSE operators, like
                 encapsulate and expand, but applied infrequently and
                 depending upon how the GP is going. SPECIALISE would
                 look for common code in better programs and convert
                 them to functions which cannot be disrupted by
                 crossover.

                 However: ``Regarding the specialize and decompose
                 operators, we abandoned them after very preliminary
                 work''.

                 References Ken De Jong ICGA-87

                 ",
}

@TechReport{OReilly:1992:tabbGP,
  author =       "U. M. O'Reilly and F. Oppacher",
  title =        "The Troubling Aspects of a Building Block Hypothesis
                 for Genetic Programming",
  institution =  "Santa Fe Institute",
  year =         "1992",
  type =         "Working Paper",
  number =       "94-02-001",
  address =      "1399 Hyde Park Road Santa Fe, New Mexico 87501-8943
                 USA

                 ",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "A version of this paper will be published in
                 Foundations of Genetic Algorithms (FOGA), See
                 OReilly:1995:tabbGP",
}

@TechReport{OReilly:1992:ubbf,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "Using Building Block Functions to Investigate a
                 Building Block Hypothesis for Genetic Programming",
  institution =  "Santa Fe Institute",
  year =         "1994",
  type =         "Working Paper",
  number =       "94-02-029",
  address =      "1399 Hyde Park Road Santa Fe, New Mexico 87501-8943
                 USA

                 ",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper presents building block functions, i.e.,
                 functions in which explicit schemas of high fitness are
                 defined (BB functions, for short) which are useful in
                 investigating the character of Genetic Programming
                 search. One conjecture we believe to be answerable
                 through experimentation with these functions is whether
                 GP exploits building blocks. That is, is one
                 explanation for GP's power that, when primary partial
                 solutions are discovered, their numbers increase and GP
                 crossover is able to combine them into increasingly
                 larger sub-solutions and eventually find the solution?
                 The functions should also provide insight into more
                 details aspects of the roles of GP crossover and GP
                 genotype growth",
  notes =        "Working report

                 ",
}

@InProceedings{OReilly:1995:tabbGP,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "The Troubling Aspects of a Building Block Hypothesis
                 for Genetic Programming",
  booktitle =    "Foundations of Genetic Algorithms 3",
  year =         "1995",
  editor =       "L. Darrell Whitley and Michael D. Vose",
  pages =        "73--88",
  address =      "Estes Park, Colorado, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "31 " # jul # "--2 " # aug # " 1994",
  organisation = "International Society for Genetic Algorithms",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-356-5",
  size =         "16 pages",
  abstract =     "In this paper we carefully formulate a Schema Theorem
                 for Genetic Programming (GP) using a schema definition
                 that accounts for the variable length and the
                 non-homologous nature of GP's representation. In a
                 manner similar to early GA research, we use
                 interpretations of our GP Schema Theorem to obtain a GP
                 Building Block definition and to state a ``classical''
                 Building Block Hypothesis (BBH): that GP searches by
                 hierarchically combining building blocks. We report
                 that this approach is not convincing for several
                 reasons: it is difficult to find support for the
                 promotion and combination of building blocks solely by
                 rigourous interpretation of a GP Schema Theorem; even
                 if there were such support for a BBH, it is empirically
                 questionable whether building blocks always exist
                 because partial solutions of consistently above average
                 fitness and resilience to disruption are not assured;
                 also, a BBH constitutes a narrow and imprecise account
                 of GP search behaviour.

                 ",
  notes =        "FOGA-3 An early version of this paper is an SFI
                 Technical Report (see OReilly:1992:tabbGP). The paper
                 was accepted to a conference called ``Foundations of
                 Genetic Algorithms III''. It was presented at the
                 conference in Estes Park, Co, in July of 1993 (1994?)
                 and the latest version will appear in proceedings
                 forthcoming this year.

                 Presents a schema theorem for genetic programming based
                 on analogy with Holland's ST. Definition of schema more
                 general than Koza's (in Koza:book). Also delvelops
                 building block hyposthesis (again using analogy with
                 linear GA's). Then comprehensively trashes them both!
                 Some arguments against them apply equally well to
                 linear (bit string) GAs.

                 Chapter 4 of U.M. O'Reilly's PhD thesis OReilly:thesis
                 is similar to this paper. Pages 192-196 of Chapter 7
                 summarize the results.",
}

@TechReport{OReilly:1994:GPSAHCsfi,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "Program Search with a Hierarchical Variable Length
                 Representation: Genetic Programming, Simulated
                 Annealing and Hill Climbing",
  institution =  "Santa Fe Institute",
  year =         "1994",
  number =       "94-04-021",
  address =      "1399 Hyde Park Road Santa Fe, New Mexico 87501-8943
                 USA

                 ",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper presents a comparison of Genetic
                 Programming(GP) with Simulated Annealing (SA) and
                 Stochastic Iterated Hill Climbing (SIHC) based on a
                 suite of program discovery problems which have been
                 previously tackled only with GP. All three search
                 algorithms employ the hierarchical variable length
                 representation for programs brought into recent
                 prominence with the GP paradigm. We feel it is not
                 intuitively obvious that mutation-based adaptive search
                 can handle program discovery yet, to date, for each GP
                 problem we have tried, SA or SIHC also work.",
  notes =        "Hard copy sent from SFI by pdb@santafe.edu (Patricia
                 Brunello) Second revision dated 6/6/94

                 This paper was co-authored with Franz Oppacher of
                 Carleton University. An abridged version appears in the
                 Proceedings of the Third Conference on Parallel Problem
                 Solving from Nature, Springer Verlag, 1994. A longer
                 version is SFI Technical Report 94-04-021",
  size =         "13 pages",
}

@InProceedings{OReilly:1994:GPSAHC,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "Program Search with a Hierarchical Variable Length
                 Representation: Genetic Programming, Simulated
                 Annealing and Hill Climbing",
  booktitle =    "Parallel Problem Solving from Nature -- PPSN III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and Reinhard
                 Manner",
  series =       "Lecture Notes in Computer Science",
  number =       "866",
  pages =        "397--406",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58484-6",
  URL =          "http://www.ai.mit.edu/people/unamay/papers/ppsn-94.ps",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
  abstract =     "This paper presents a comparison of Genetic
                 Programming(GP) with Simulated Annealing (SA) and
                 Stochastic Iterated Hill Climbing (SIHC) based on a
                 suite of program discovery problems which have been
                 previously tackled only with GP. All three search
                 algorithms employ the hierarchical variable length
                 representation for programs brought into recent
                 prominence with the GP paradigm. We feel it is not
                 intuitively obvious that mutation-based adaptive search
                 can handle program discovery yet, to date, for each GP
                 problem we have tried, SA or SIHC also work.",
  notes =        "PPSN3

                 A longer version is SFI Technical Report 94-04-021.",
  size =         "10 pages",
}

@TechReport{OReilly:1995:hybridsfi,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "Hybridized Crossover-Based Search Techniques for
                 Program Discovery",
  institution =  "Santa Fe Institute",
  year =         "1995",
  number =       "95-02-007",
  address =      "1399 Hyde Park Road Santa Fe, New Mexico 87501-8943
                 USA

                 ",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ai.mit.edu/people/unamay/papers/xo-hybrid.ps",
  abstract =     "In this paper we address the problem of program
                 discovery as defined by Genetic Programming. We have
                 two major results: First, by combining a standard
                 crossover operator with two traditional single point
                 search algorithms (simulated annealing and stochastic
                 iterated hill climbing), we have solved some problems
                 with fewer fitness evaluations and a greater
                 probability of a success than Genetic Programming.
                 Second, we have managed to enhance Genetic Programming
                 by hybridizing it with the simple scheme of hill
                 climbing from a few individuals, at a fixed interval of
                 generations. The new hillclimbing component has two
                 options for generating candidate solutions: mutation or
                 crossover. When it uses crossover, mates are either
                 randomly selected or are individually drawn from the
                 population at large, or are drawn from a pool of
                 fittest individuals. The population pool option has
                 proved superior thus indicating that a combination of
                 population-based evolution and greedy exploitation of a
                 single individual has merit.

                 ",
  notes =        "If you want the paper version contact SFI
                 (mat@santafe.edu) or contact una-may for a postscript
                 uuencoded version. All comments are welcome. Contact me
                 with unamay@santafe.edu.

                 The unabridged version of this paper is Santa Fe
                 Institute Working Paper: 95-02-007. An abridged (6
                 page) version is to appear in the proceedings of the
                 1995 World Conference on Evolutionary Computation held
                 in Perth, Australia, December 1-3, 1995.",
  size =         "11 pages",
}

@PhdThesis{OReilly:thesis,
  author =       "Una-May O'Reilly",
  title =        "An Analysis of Genetic Programming",
  school =       "Carleton University",
  year =         "1995",
  address =      "Ottawa-Carleton Institute for Computer Science,
                 Ottawa, Ontario, Canada",
  month =        "22 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.santafe.edu/pub/unamay/",
  url2 =         "ftp://cs.ucl.ac.uk/genetic/papers/oreilly/",
  url3 =         "http://www.ai.mit.edu/people/unamay/thesis.html",
  size =         "224 pages",
  notes =        "http://www.santafe.edu/~unamay",
}

@InProceedings{OReilly:1995:hybrid,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "Hybridized Crossover-Based Search Techniques for
                 Program Discovery",
  booktitle =    "Proceedings of the 1995 World Conference on
                 Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "573--578",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, simulated
                 annealing, iterative methods, search problems,
                 programming theory, hybridized crossover-based search
                 techniques, program discovery, hierarchical crossover
                 operator, single-point search algorithms, simulated
                 annealing, stochastic iterated hill climbing; candidate
                 solutions, success probability, inter-generation
                 interval",
  ISBN =         "0-7803-2759-4",
  URL =          "http://www.ai.mit.edu/people/unamay/papers/ec95.ps",
  URL =          "http://ieeexplore.ieee.org/iel2/3507/10438/00487447.pdf",
  size =         "6 pages",
  abstract =     "In this paper we address the problem of program
                 discovery as defined by Genetic Programming. We have
                 two major results: First, by combining a hierarchical
                 crossover operator with two traditional single point
                 search algorithms: Simulated Annealing and Stochastic
                 Iterated Hill Climbing, we have solved some problems
                 with fewer fitness evaluations and a greater
                 probability of a success than Genetic Programming.
                 Second, we have managed to enhance Genetic Programming
                 by hybridizing it with the simple scheme of hill
                 climbing from a few individuals, at a fixed interval of
                 generations. The new hill climbing component has two
                 options for generating candidate solutions: mutation or
                 crossover. When it uses crossover, mates are either
                 randomly created, randomly drawn from the population at
                 large, or drawn from a pool of fittest individuals.",
  notes =        "ICEC-95

                 This paper is an abridged version of SFI Tech Report:
                 95-02-007

                 ",
}

@InCollection{OReilly:1996:aigp2,
  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "A Comparative Analysis of {GP}",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "23--44",
  chapter =      "2",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "In order to analyze Genetic Programming (GP), this
                 chapter compares it with two alternative adaptive
                 search algorithms, Simulated Annealing (SA) and
                 Stochastic Iterated Hill Climbing (SIHC). SIHC and SA
                 are used to solve program discovery problems posed in
                 the style of GP. In separate versions they employ
                 either GP's crossover operator or a mutation operator.
                 The comparisons in terms of likelihood of success and
                 efficiency show them to be effective. Based upon their
                 success, hybrid versions of GP and hill climbing are
                 designed that improve upon a canonical version of GP.
                 Program discovery practitioners may find it useful to
                 coherently view all the algorithms this chapter
                 considers by using the perspective of evolution.",
}

@InProceedings{oreilly:1996:igADF,
  author =       "Una-May O'Reilly",
  title =        "Investigating the Generality of Automatically Defined
                 Functions",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "351--356",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.ai.mit.edu/people/unamay/papers/submission.ps",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{oreilly:1997:dnGPugo,
  author =       "Una-May O'Reilly",
  title =        "Using a Distance Metric on Genetic Programs to
                 Understand Genetic Operators",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "199--206",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670. See
                 oreilly:1997:dnGPugo2",
}

@Unpublished{oreilly:1997:dmGPugo,
  author =       "Una-May O'Reilly",
  title =        "Using a Distance Metric on Genetic Programs to
                 Understand Genetic Operators",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, variable size
                 representation",
  abstract =     "I describe a distance metric called ''edit'' distance
                 which quantifies the syntactic difference between two
                 genetic programs. In the context of one specific
                 problem, the 6 bit multiplexor, I use the metric to
                 analyze the amount of new material introduced by
                 different crossover operators, the difference among the
                 best individuals of a population and the difference
                 among the best individuals and the rest of the
                 population. The relationships between these data and
                 run performance are imprecise but they are sufficiently
                 interesting to encourage encourage further
                 investigation into the use of edit distance.",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "4 pages. See oreilly:1997:dnGPugo2",
}

@InProceedings{oreilly:1997:dnGPugo2,
  author =       "Una-May O'Reilly",
  title =        "Using a Distance Metric on Genetic Programs to
                 Understand Genetic Operators",
  booktitle =    "IEEE International Conference on Systems, Man, and
                 Cybernetics, Computational Cybernetics and Simulation",
  year =         "1997",
  pages =        "4092--4097",
  volume =       "5",
  address =      "Orlando, Florida, USA",
  month =        "12-15 " # oct,
  keywords =     "genetic algorithms, genetic programming, distance
                 metric, genetic programs, genetic operators, edit
                 distance, syntactic difference, multiplexor, crossover
                 operators, population, best individuals, run
                 performance, search, trees",
  ISBN =         "0-7803-4053-1",
  URL =          "http://ieeexplore.ieee.org/iel4/4942/13793/00637337.pdf",
  abstract =     "I describe a distance metric called {"}edit{"}
                 distance which quantifies the syntactic difference
                 between two genetic programs. In the context of one
                 specific problem, the 6 bit multiplexor, I use the
                 metric to analyze the amount of new material introduced
                 by different crossover operators, the difference among
                 the best individuals of a population and the difference
                 among the best individuals and the rest of the
                 population. The relationships between these data and
                 run performance are imprecise but they are sufficiently
                 interesting to encourage further investigation into the
                 use of edit distance.",
  notes =        "{"}fair crossover{"} (no 90/10 bias), {"}Height fair
                 crossover{"} and normal subtree crossover",
}

@InProceedings{oreilly:1997:edGPp,
  author =       "Una-May O'Reilly",
  title =        "The Impact of External Dependency in Genetic
                 Programming Primitives",
  booktitle =    "ET'97 Theory and Application of Evolutionary
                 Computation",
  year =         "1997",
  editor =       "Chris Clack and Kanta Vekaria and Nadav Zin",
  pages =        "45--58",
  address =      "University College London, UK",
  month =        "15 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Both control and data dependencies among primitives
                 impact the behavioural consistency of subprograms in
                 genetic programming solutions. Behavioural consistency
                 in turn impacts the ability of genetic programming to
                 identify and promote appropriate subprograms. We
                 present the results of modelling dependency through a
                 parameterized problem in which a subprogram exhibits
                 internal and external dependency levels that change as
                 the subprogram is successively incorporated into larger
                 subsolutions. We find that the key difference between
                 non-existent and {"}full'' external dependency is a
                 longer time to solution identification and a lower
                 likelihood of success as shown by increased difficulty
                 in identifying and promoting correct subprograms.",
  notes =        "http://www.cs.ucl.ac.uk/isrg/et97/ see also
                 oreilly:1998:edGPp",
}

@InProceedings{oreilly:1998:edGPp,
  author =       "Una-May O'Reilly",
  title =        "The Impact of External Dependency in Genetic
                 Programming Primitives",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "306--311",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, external
                 dependency, primitives, control dependencies, data
                 dependencies, subprogram behavioural consistency,
                 parameterized problem, solution identification, success
                 likelihood",
  ISBN =         "0-7803-4869-9",
  file =         "c053.pdf",
  URL =          "http://ieeexplore.ieee.org/iel4/5621/15048/00699750.pdf",
  size =         "6 pages",
  abstract =     "Both control and data dependencies among primitives
                 impact the behavioural consistency of subprograms in
                 genetic programming solutions. Behavioural consistency
                 in turn impacts the ability of genetic programming to
                 identify and promote appropriate subprograms. We
                 present the results of modelling dependency through a
                 parameterized problem in which a subprogram exhibits
                 internal and external dependency levels that change as
                 the subprogram is successively combined into larger
                 subsolutions; We And that the key difference between
                 nonexistent and {"}full{"} external dependency is a
                 longer time to solution identification and a lower
                 likelihood of success as shown by increased difficulty
                 in identifying and promoting correct subprograms.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence. See also
                 oreilly:1997:edGPp",
}

@Unpublished{oreilly:1998:piefdsDRAFT,
  author =       "Una-May O'Reilly and Girish Ramachandran",
  title =        "Evolution as a design strategy for non-linear
                 architecture: Generative modeling of 3{D} surfaces",
  note =         "Early version of oreilly:1998:piefds",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming,
                 architecture",
  URL =          "http://www.ai.mit.edu/people/unamay/papers/alive-v1.ps",
  size =         "10 pages",
}

@InProceedings{oreilly:1998:piefds,
  author =       "Una-May O'Reilly and Girish Ramachandran",
  title =        "A Preliminary Investigation of Evolution as a Form
                 Design Strategy",
  booktitle =    "Proceedings of the Sixth International Conference on
                 Artificial Life",
  year =         "1998",
  editor =       "Christoph Adami and Richard K. Belew and Hiroaki
                 Kitano and Charles E. Taylor",
  address =      "University of California, Los Angeles",
  month =        "26-29 " # jun,
  publisher =    "MIT Press",
  note =         "Forthcoming",
  keywords =     "genetic algorithms, genetic programming,
                 architecture",
  URL =          "http://www.ai.mit.edu/people/unamay/papers/ev80.ps",
  size =         "5 pages",
  abstract =     "As it moves towards the end of the 20th century,
                 architecture is being challenged by our digital,
                 high-technology, information-based age. Architecture
                 like other forms of cultural expression is looking at
                 ways to engage and interpret the paradigm shifts in
                 science. This challenge demands creative exploration at
                 the levels of architectural theory, design strategy or
                 concepts, methods and realization. Our paper describes
                 an exploration in 3-D form generation as a strategy for
                 architectural design conceptualization process that
                 parallels the nonlinear paradigm. We have built a
                 prototype system that integrates evolution, both as a
                 metaphor and an active generative modeling tool, with
                 the interpretive aspects of the design process. The
                 metaphor - replete with concepts such as genetic
                 engineering, inheritance, sexual reproduction and
                 random variation has proved valuable in providing a
                 coherent, exploratory process. The architect is firmly
                 in control but the evolution module aids him or her by
                 providing the unexpected 3-D spatial and volumetric
                 configurations which would be impossible to conceive
                 otherwise and suggesting novel combinations or
                 adaptations of forms currently under consideration.
                 This evolutionary modeling tool keeps the clay wet for
                 the architect to interact, interpret and translate
                 these complex 3-D forms and spaces to architecture at
                 any given time. The evolution module, while essentially
                 an evolutionary algorithm, does not fit within the
                 strict specifications of evolutionary programming,
                 genetic programming, genetic algorithms or evolution
                 strategies. One of its unique elements is a language of
                 adaptation at the architectural level (e.g. fold,
                 twist, stretch, hole) which maps to manipulation of
                 vertex profiles and faces defined by vertices. It also
                 yields control of its random variability to the
                 architect via parameterizations. This gives the
                 architect the role of ``genetic engineer''.",
  notes =        "ALife VI. See also oreilly:1998:piefdsDRAFT",
}

@InProceedings{oreilly:1998:fssaGP,
  author =       "Una-May O'Reilly and David E. Goldberg",
  title =        "How Fitness Structure Affects Subsolution Acquisition
                 in Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "269--277",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www.ai.mit.edu/people/unamay/papers/timing-final.ps",
  size =         "9 pages",
  abstract =     "We define fitness structure in genetic programming to
                 be the mapping between the subprograms of a program and
                 their respective fitness values. This paper shows how
                 various fitness structures of a problem with
                 independent subsolutions relate to the acquisition of
                 subsolutions. The rate of subsolution acquisition is
                 found to be directly correlated with fitness structure
                 whether that structure is uniform, linear or
                 exponential. An understanding of fitness structure
                 provides partial insight into the complicated
                 relationship between fitness function and the outcome
                 of genetic programming's search.",
  notes =        "GP-98",
}

@Unpublished{oreilly:1999:fogpepslm,
  author =       "Una-May O'Reilly",
  title =        "Foundations of Genetic Programming: Effective
                 Population Size, Linking and Mixing",
  note =         "GECCO'99 workshop",
  month =        apr,
  year =         "1999",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ai.mit.edu/people/unamay/popsize.ps",
  notes =        "Summary of recent work: goldberg:1998:good
                 oreilly:1998:edGPp oreilly:1998:fssaGP",
  size =         "2 pages",
}

@InProceedings{o'reilly:2001:aagpd,
  author =       "Una-May O'Reilly and Peter Testa and Simon Greenwold
                 and Martin Hemberg",
  title =        "{Agency-GP:} Agent-Based Genetic Programming for
                 Design",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "303--309",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001LB",
}

@Article{Ortega-Sanchez:2000:ebcaftp,
  author =       "Cesar Ortega-Sanchez and Daniel Mange and Steve Smith
                 and Andy Tyrrell",
  title =        "Embryonics: {A} Bio-Inspired Cellular Architecture
                 with Fault-Tolerant Properties",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "3",
  pages =        "187--215",
  month =        jul,
  keywords =     "genetic algorithms, embryonics, bio-inspired systems,
                 FPGAs, fault-tolerant systems, reliability models,
                 evolvable hardware",
  ISSN =         "1389-2576",
  abstract =     "This paper details and expands the work on Embryonics,
                 a recently proposed fault-tolerant cellular
                 architecture with reconfiguration properties inspired
                 by the ontogenetic development of multicellular
                 systems. The design of a selector-based embryonic cell,
                 its applications and the reliability models associated
                 to different embryonic reconfiguration strategies are
                 presented. It is noted that embryonic distributed
                 systems possess, in the majority of cases, better
                 reliability characteristics than equivalent centralised
                 systems.",
}

@InCollection{orthlieb:1995:THP,
  author =       "Carl Orthlieb",
  title =        "The Hannibal Project",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "219--228",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{osborn:1995:glp,
  author =       "Thomas R. Osborn and Adib Charif and Ricardo Lamas and
                 Eugene Dubossarsky",
  title =        "Genetic Logic Programming",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "728",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, prolog",
  URL =          "ftp://ftp.socs.uts.edu.au/pub/users/osborn/glp.uu",
  url_2 =        "ftp://cs.ucl.ac.uk/genetic/papers/glpfinal.ps.gz",
  size =         "5 pages",
  abstract =     "

                 Genetic Logic Programming (GLP) is a new method which
                 applies the Genetic Algorithms paradigm to Declarative
                 Programming - specifically to evolve populations of
                 Prolog programs. This talk examines GLP applied to
                 Natural Language Understanding to illustrate the power,
                 issues and limitations of GLP. Populations of Prolog
                 query interpreters evolve to respond more correctly to
                 queries about the Aesop fable: {"}The Fox and the
                 Crow{"}. The interpreters process parsed text and
                 consult a general knowledge-base. The gene pool
                 consists of a large set of Prolog rules and facts which
                 are tentatively proposed as being 'useful' for
                 interpretation. Essentially, interpreters act as an
                 interface between queries, knowledge bases and the
                 text. Closure and termination are addressed at the
                 level of design of the gene pool, and various Prolog
                 options.

                 Fitness amounts to a score on a high school like
                 {"}comprehension test{"}, with special care to deal
                 with redundant and dependent answers, and with an eye
                 to rewarding correct higher-level
                 abstractions.

                 glpfinal.ps.gz 3 July 2002 Here is the final version of
                 the paper. The corrections are minor but the formating
                 is much better.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html

                 Posting by Tom to GP list Thu, 01 Feb 1996 13:50:40
                 +1100

                 The gist was applying evolutionary methods to (Prolog)
                 Logic Programs, which were applied to NLU. There are
                 several subtlties which are covered in the paper -
                 notions of fitness in terms of measured program
                 correctness, the evolving thing was an interface
                 between (parsed) text and lexicons and
                 knowledge-base(s), genes were 'hooks' into the KB
                 (either a rule or several, ...), and termination and
                 closure were tricky.

                 ",
}

@InProceedings{ostermeier:1999:AESCSIAANMDWCMSPC,
  author =       "Andreas Ostermeier and Nikolaus Hansen",
  title =        "An Evolution Strategy with Coordinate System Invariant
                 Adaptation of Arbitrary Normal Mutation Distributions
                 Within the Concept of Mutative Strategy Parameter
                 Control",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "902--909",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ostrowski:1998:ismcaoalsesd,
  author =       "David A. Ostrowski and Robert G. Reynolds",
  title =        "Integration of Slicing Methods into a Cultural
                 Algorithm in Order to Assist in Large-Scale
                 Engineerging Systems Design",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "191--198",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 Ford, Dearborn",
}

@InProceedings{ostrowski:2002:ucatesiam,
  author =       "David A. Ostrowski and Troy Tassier and Mark P.
                 Everson and Robert G. Reynolds",
  title =        "Using Cultural Algorithms to Evolve Strategies in
                 Agent-Based Models",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "741--746",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Cultural Algorithms are self-adaptive models that
                 support the collective evolution process through the
                 employment of a population and a belief space. Here,
                 the Cultural approach is applied to derive a
                 generalized set of beliefs from successive populations
                 of parameter configurations from an agent-based
                 simulation of transactions within a durable goods
                 market. The maintenance of this information allows for
                 the guided evolution of the agent-based system over
                 successive simulations. In order to more effectively
                 evaluate parameter configurations, Software Engineering
                 techniques of white and black box testing are applied.
                 In this paper, a methodology for the use of Cultural
                 Algorithms to optimize strategies in agent-based models
                 is presented. This approach is demonstrated in an
                 application used to model pricing strategies in the
                 context of an agent-based model under a simulated
                 real-world market scenario and a heterogeneous
                 population.",
}

@InProceedings{osullivan:2002:EuroGP,
  title =        "An investigation into the use of different search
                 strategies with Grammatical Evolution",
  author =       "John O'Sullivan and Conor Ryan",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "268--277",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "We present an investigation into the performance of
                 Grammatical Evolution using a number of different
                 search strategies, Simulated Annealing, Hill Climbing,
                 Random Search and Genetic Algorithms. Comparative
                 results on three different problems are examined. We
                 analyse the nature of the search spaces presented by
                 these problems and offer an explanation for the
                 contrasting performance of each of the search
                 strategies. Our results show that Genetic Algorithms
                 provide a consistent level of performance across all
                 three problems successfully coping with sensitivity of
                 the system to discrete changes in the selection of
                 productions from the associated grammar.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{otero:2002:gecco,
  author =       "Fernando E. B. Otero and Monique M. S. Silvia and Alex
                 A. Freitas",
  title =        "Genetic Programming For Attribute Construction In Data
                 Mining",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1270",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, poster paper",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{otero:2002:gecco:lbp,
  title =        "Genetic Programming for Attribute Construction in Data
                 Mining",
  author =       "Fernando E. B. Otero and Monique M. S. Silva and Alex
                 A. Freitas",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "370--376",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 Fitness function uses information gain per attribute",
}

@InProceedings{BC-sipar-m:94,
  author =       "M. Oussaid\`ene and B. Chopard",
  title =        "{Optimisation g\'en\'etique massivement parall\`ele}",
  booktitle =    "SIPAR-Workshop on Parallel and Distributed Computing",
  year =         "1994",
  organization = "SIPAR/Universit\'e de Fribourg",
  notes =        "

                 ",
}

@InProceedings{renpar:95,
  author =       "M. Oussaid\`ene and B. Chopard and M. Tomassini",
  title =        "{Programmation \'evolutionniste parall\`ele}",
  booktitle =    "RenPar '95 workshop",
  year =         "1995",
  editor =       "Belgium University of Mons",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{oussaidene:1996:pGPtme,
  author =       "Mouloud Oussaidene and Bastien Chopard and Olivier V.
                 Pictet and Marco Tomassini",
  title =        "Parallel Genetic Programming: An Application to
                 Trading Models Evolution",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "357--380",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://cuiwww.unige.ch/~chopard/Genetics/GP96.ps",
  size =         "6 pages",
  notes =        "GP-96 PVM3 PGPS master-slave slaves given an
                 individual program to evaluate. IBM SP-2. Considers
                 static v. dynamic scheduling of tasks to slaves (little
                 difference). Near linear speed up.

                 Simulated foreign exchange trading, 7 years of data on
                 7 currency pairs. Best S-expression 7 nodes. Olsen and
                 associates research institute",
}

@PhdThesis{mouloud-phd:96,
  author =       "M. Oussaid{\`e}ne",
  title =        "Genetic Programming: Methodology, Parallelization and
                 Applications",
  school =       "Computer Science Departement, University of Geneva",
  year =         "1996",
  address =      "24 rue General-Dufour 1211 Geneva 4, Switzerland",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://cuiwww.unige.ch/~chopard/Genetics/mouloud.ps",
  size =         "152 pages",
  notes =        "Abstract in French, main body of text in English

                 ",
}

@Article{par-comp:96,
  author =       "Mouloud Oussaid\`ene and Bastien Chopard and Olivier
                 V. Pictet and Marco Tomassini",
  title =        "Parallel Genetic Programming and its application to
                 trading model induction",
  journal =      "Parallel Computing",
  year =         "1997",
  volume =       "23",
  pages =        "1183--1198",
  keywords =     "genetic algorithms, genetic programming, Parallel
                 genetic programming, Performance analysis, Financial
                 trading models",
  abstract =     "This paper presents a scalable parallel implementation
                 of genetic programming on distributed memory machines.
                 The system runs multiple master-slave instances each
                 mapped on all the allocated nodes and multithreading is
                 used to overlap message latencies with useful
                 computation. Load balancing is achieved using a dynamic
                 scheduling algorithm and comparison with a static
                 algorithm is reported. To alleviate premature
                 convergence, asynchronous migration of individuals is
                 performed among processes. We show that nearly linear
                 speedups can be obtained for problems of large enough
                 size. The system has been applied to infer robust
                 trading strategies which is a compute-intensive
                 financial application. © Copyright 2001, Elsevier
                 Science, All rights reserved.",
  URL =          "http://www.elsevier.nl/gej-ng/10/35/21/30/22/26/abstract.html",
  notes =        "

                 ",
}

@InProceedings{Ozkul:1997:ecs,
  author =       "Umur Ozkul",
  title =        "Evolution of Complex Systems",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "295",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@TechReport{padman:1995:GPhscps,
  author =       "Rema Padman and Stephen Roehrig",
  title =        "A Genetic Programming Approach for Heuristic Selection
                 in Constrained Project Scheduling",
  institution =  "H. John Heinz III School of Public Policy and
                 Management, Carniege-Mellon University",
  year =         "1995",
  number =       "95-30",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The resource-constrained project scheduling problem
                 (RCPSP) with cash flows investigates the scheduling of
                 activities that are linked by precedence constraints
                 and multiple resource restrictions. Given the presence
                 of cash flows which represent expenses for initiating
                 activities and payments for completed work, maximizing
                 the Net Present Value of the project is a practical
                 objective. This is a complex combinatorial optimization
                 problem which precludes the development of optimal
                 schedules for large projects. Many heuristics exists
                 for the RCPSP, but it has proven difficult to decide in
                 advance which heuristic will provide the best result,
                 given a problem characterization in terms of parameters
                 such as size and complexity. In this paper we discuss
                 the use of genetic programming (GP) for heuristic
                 selection, and compare it directly to alternative
                 methods such as OLS regression and neural networks. The
                 study indicates that the GP approach yields results
                 that are an improvement on earlier methods. The GP
                 solution also gives valualbe information about project
                 environments where a given heuristic is inappropriate.
                 In addition, this approach has no problem evolving
                 complex nonlinear functions to capture the relationship
                 between problem parameters and heuristic performance.
                 Thus the results given in this paper shed light on the
                 logical domains of applicability of the various
                 heuristics, while at the same time provide an improved
                 heuristic selection process.",
  notes =        "http://www.heinz.cmu.edu/heinz/wpapers/active/wp00019.html

                 {"}On the Use of Genetic Programming for Selecting
                 Heuristics for Resource-Constrained Project Scheduling
                 - An Extended Abstract{"} (with Stephen Roehrig);
                 Proceedings of the Fourth International Workshop on
                 Project Management and Scheduling, Leuven, Belgium, pp.
                 129-132, 1994.

                 {"}A Genetic Programming Approach for Heuristic
                 Selection in Constrained Project Scheduling{"} (with
                 Rema Padman); Computer Science and Operations Research:
                 Recent Advances in the Interface (R. Helgason, ed.),
                 Kluwer, 1995.

                 ",
}

@InCollection{padman:1997:GPhscps,
  author =       "Rema Padman and Stephen F. Roehrig",
  title =        "A Genetic Programming Approach for Heuristic Selection
                 in Constrained Project Scheduling",
  booktitle =    "Interfaces in Computer Science and Operations
                 Research: Advances in Metaheuristics, Optimization, and
                 Stochastic Modeling Technologies",
  publisher =    "Kluwer Academic Publishers",
  year =         "1997",
  editor =       "Richard S. Barr and Richard V. Helgason and Jeffrey L.
                 Kennington",
  chapter =      "18",
  pages =        "405--421",
  address =      "Norwell, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP used to select which of 16 predefined schduling
                 heuristic to use. Test case 1440 randomly generated
                 project networks of 144 types chosen to span a domain.
                 GP better than ANN approach. cf padman:1995:GPhscps

                 ",
}

@InProceedings{page:1999:smuxspmGP,
  author =       "J. Page and R. Poli and W. B. Langdon",
  title =        "Smooth Uniform Crossover with Smooth Point Mutation in
                 Genetic Programming: {A} Preliminary Study",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "39--49",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Evolve function node within trees as well as trees.
                 Demonstrated on big partity problems.",
}

@PhdThesis{page:thesis,
  author =       "Jonathan Page",
  title =        "Sub-Symbolic Representation and Search Operators for
                 Genetic Programming",
  school =       "School of Computer Science, The University of
                 Birmingham",
  year =         "1999",
  address =      "B29 15TT, UK",
  month =        dec,
  email =        "jpage@praxis-cs.co.uk, jonny99@btinternet.com",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/page/thesis.ps.gz",
  size =         "226 pages",
  abstract =     "Genetic Programming (GP) is one of the more recent
                 additions to a diverse body of biologically-inspired
                 search techniques known as evolutionary algorithms
                 (EAs). GP differs from most other EAs in that candidate
                 solutions are executable programs of arbitrary size
                 which are evaluated according to how well they perform
                 on a specified task. Whilst GP has been successfully
                 applied to a number of problem domains, there remain
                 many tasks on which performance is notably poor.

                 This thesis argues that poor performance is, in many
                 cases, due to the nature of the search that GP's
                 operators perform. GP's primary operator, crossover,
                 has been shown to perform a predominantly local search
                 of the program space, and this can make global optima
                 difficult to locate if the fitness landscape is
                 characterised by long stretches of very weak or neutral
                 fitness gradients.

                 Recently, a GP search operator that performs global
                 search has been proposed. This operator is known as GP
                 uniform crossover, and in this thesis we test it
                 extensively on a number of benchmark problems. We also
                 propose a novel sub-symbolic representation for
                 function which can be used in conjunction with GP
                 uniform crossover, and which accords GP the additional
                 ability to perform a very local, directed search of the
                 program space.

                 The experiments reported in this thesis raise a number
                 of issues that are discussed in detail. The
                 sub-symbolic representation, as used here, constrains
                 the size of the function set and in many cases this is
                 significantly larger than those used in most GP
                 implementations. Despite of this, we report enhanced
                 performance on a number of problems and this leads us
                 to question the received wisdom which states that the
                 recommends a minimalist approach to function set
                 design. The use of different operators and
                 representations transforms the search space and
                 highlights a number of properties of GP program spaces
                 that have so far been overlooked. We discuss these and,
                 in the light of our findings, reevaluate some of the
                 hypotheses that have been put forward concerning the
                 program spaces of some well-known functions. The search
                 operators used here also constrain the complexity of
                 the programs that may be evolved. We examine the
                 advantages of this, particularly with respect to
                 controlling program size and resultant run-time, and
                 the disadvantages.

                 The issues arising from these studies are diverse and
                 many have received little attention in the literature.
                 We believe that the findings reported here pose
                 numerous questions, and we suggest several directions
                 for future research.",
}

@InCollection{pak:1994:aaslife,
  author =       "Shan-Ng Pak",
  title =        "Another Approach to the Synthesis of Life",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "136--146",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-182105-2",
  notes =        "Generic Instruction Pattern - program is variable
                 length collections od symbols which form cells. Asexual
                 reproduction and mutation.

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{palazzari:1999:EPCGA,
  author =       "Paolo Palazzari and Moreno Coli",
  title =        "Evolving Probabilistic Chromosomes in Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "511--518",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{pandyaworayan:2002:gecco:workshop,
  title =        "Time Series Prediction Using a Recursive Algorithm of
                 a Combination of Genetic Programming and Constant
                 Optimization",
  author =       "W. Panyaworayan and G. Wuetschner",
  pages =        "101--107",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop

                 Prevention of overfitting by early stoping based on
                 training and validation sets",
}

@InProceedings{parent:2002:gecco,
  author =       "Johan Parent and Ann Nowe",
  title =        "Evolving Compression Preprocessors With Genetic
                 Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "861--867",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{paris:2001:EA,
  author =       "Gregory Paris and Denis Robilliard and Cyril Fonlupt",
  title =        "Applying Boosting Techniques to Genetic Programming",
  booktitle =    "Artificial Evolution 5th International Conference,
                 Evolution Artificielle, EA 2001",
  year =         "2001",
  editor =       "P. Collet and C. Fonlupt and J.-K. Hao and E. Lutton
                 and M. Schoenauer",
  volume =       "2310",
  series =       "LNCS",
  pages =        "267--278",
  address =      "Creusot, France",
  month =        oct # " 29-31",
  publisher =    "Springer Verlag",
  ISBN =         "3-540-43544-1",
  URL =          "http://link.springer.de/link/service/series/0558/papers/2310/23100267.pdf",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This article deals with an improvement for genetic
                 programming based on a technique originating from the
                 machine learning field: boosting. In a first part of
                 this paper, we test the improvements offered by
                 boosting on binary problems. Then we propose to deal
                 with regression problems, and propose an algorithm,
                 called GPboost, that keeps closer to the original idea
                 of distribution in Adaboost than what has been done in
                 previous implementation of boosting for genetic
                 programming.",
  notes =        "EA'01",
}

@InProceedings{park:1997:GPscnlp,
  author =       "YoungJa Park and ManSuk Song",
  title =        "Genetic Programming Approach to Sense Clustering in
                 Natural Language Processing",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "261",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{park:1998:GAcp,
  author =       "YoungJa Park and ManSuk Song",
  title =        "A Genetic Algorithm for Clustering Problems",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "568--575",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{parker:1998:gargcGA,
  author =       "Gary B. Parker",
  title =        "Generating Arachnid Robot Gaits with Cyclic Genetic
                 Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "576--583",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{parker:1999:AHGCUALFB,
  author =       "Gary B. Parker and Jonathan W. Mills",
  title =        "Adaptive Hexapod Gait Control Using Anytime Learning
                 with Fitness Biasing",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "519--524",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{parmee:1999:PADUCMGA,
  author =       "Ian C. Parmee and Andrew H. Watson",
  title =        "Preliminary Airframe Design Using Co-Evolutionary
                 Multiobjective Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1657--1665",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{paterson:1996:dgp,
  author =       "Norman R. Paterson and Mike Livesey",
  title =        "Distinguishing Genotype and Phenotype in Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "141--150",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming, GADS",
  URL =          "ftp://ftp.dcs.st-and.ac.uk/pub/norman/GADS.ps.gz",
  abstract =     "This paper introduces GADS, a technique for genetic
                 programming where the genotype is distinct from the
                 phenotype. The GADS genotype is a list of integers
                 representing productions in a syntax. This is used to
                 generate the phenotype, which is a program in the
                 language defined by the syntax. Syntactically invalid
                 phenotypes cannot be generated, though there may be
                 phenotypes with residual nonterminals. GADS can be
                 implemented on a traditional genetic algorithm. The
                 paper describes an experiment to decide whether GADS is
                 feasible and to explore the effect of some variables on
                 its performance. The results show that GADS can be more
                 efficient than traditional tree-based genetic
                 programming.

                 http://www.dcs.st-and.ac.uk/~norman/",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{Paterson:1997:ecacGP,
  author =       "Norman Paterson and Mike Livesey",
  title =        "Evolving caching algorithms in {C} by genetic
                 programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "262--267",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www.dcs.st-and.ac.uk/~norman/Pubs/cache.ps.gz",
  notes =        "GP-97. Test data also used in oneill:1999:AGCA",
}

@InProceedings{Paterson:2000:GECCOlb,
  author =       "Norman Paterson and Michael Livesey",
  title =        "Performance Comparison in Genetic Programming",
  pages =        "253--260",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dcs.st-and.ac.uk/~norman/Pubs/PerfCompInGP.ps.gz",
  notes =        "Part of whitley:2000:GECCOlb",
}

@InProceedings{pazos:1996:dprANNdtGA,
  author =       "Alejandro Pazos and Julian Dorado and Antonino
                 Santos",
  title =        "Detection of Patterns in Radiographs using {ANN}
                 Designed and Trained with the Genetic Algorithm",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "432",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@InProceedings{pazos:1999:AARCGM,
  author =       "Alejandro Pazos and A. Santos del Riego and Julian
                 Dorado and J. J. Romero Cardalda",
  title =        "Adaptive Aspects of Rhythmic Composition: Genetic
                 Music",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1794",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{pazos:1999:DARHPBW,
  author =       "A. Pazos and J. Pazos and Alfonso R. Paton",
  title =        "{DNA} Assembly and Recombination for Hamiltonian Paths
                 and Binary Words",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1822--1824",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "dna and molecular computing",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{pazos:1999:OGPTRAWASAF,
  author =       "Alejandro Pazos and Julian Dorado and Antonino Santos
                 and Juan Ramon Rabunal",
  title =        "Optimization of {GA} Parameters to Train Recurrent
                 {ANN} through Weight Adjustment and Selection of
                 Activation Functions",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1793",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{Peakin:2001:Sunday-Times,
  author =       "William Peakin",
  title =        "Creative computer can invent to order",
  journal =      "The Sunday Times",
  year =         "2001",
  month =        "12 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sunday-times.co.uk/news/pages/sti/2001/08/12/stinwenws02005.html",
  notes =        "Positive mention of Koza GP to infringe patents.
                 Picture of Koza. Hmm picture in online version replaced
                 by 14 October 2001.",
}

@InProceedings{pedrycz:2001:gecco,
  title =        "Evolutionary Optimization of Logic-Oriented Systems",
  author =       "Witold Pedrycz and Marek Reformat",
  pages =        "1389--1396",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "real world applications, fuzzy models, fuzzy neurons,
                 logic architectures, rule-based computing, genetic
                 programming",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{Pelikan:1997:rlcGP,
  author =       "Martin Pelikan and Vladimir Kvasnicka and Jiri
                 Pospichal",
  title =        "Read's linear codes and genetic programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "268",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{pelikan:1999:BTBOA,
  author =       "Martin Pelikan and David E. Goldberg and Erick
                 Cantu-Paz",
  title =        "{BOA}: The Bayesian Optimization Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "525--532",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{pereira:1999:GBCCSBBP,
  author =       "Francisco B. Pereira and Penousal Machado and Ernesto
                 Costa and Amilcar Cardoso",
  title =        "Graph Based Crossover-{A} Case Study with the Busy
                 Beaver Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1149--1155",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{pereira:1999:RDEMR,
  author =       "Angela Guimaraes Pereira",
  title =        "Road Design by Evolutionary Modelling of Routes",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1778",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Perkins:1997:iavbge,
  author =       "Simon Perkins",
  title =        "Incremental Acquisition of Visual Behaviour using
                 Guided Evolution",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "296",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@InProceedings{perkins:1999:EEVTS,
  author =       "Simon Perkins",
  title =        "Evolving Effective Visual Tracking through Shaping",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1156--1161",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ieee94:perkis,
  author =       "Tim Perkis",
  title =        "Stack-Based Genetic Programming",
  year =         "1994",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  publisher =    "IEEE Press",
  volume =       "1",
  pages =        "148--153",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Report 93-11-A Antelope Engineering, Albany,
                 California, See also
                 ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/stack-gp.ps.gz",
}

@InProceedings{perrenoud:1999:EHRNG,
  author =       "Mathieu Perrenoud and Marco Tomassini and Moshe Sipper
                 and Mose Zolla",
  title =        "Evolving High-Quality Random Number Generators",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "804",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Perry:popGP,
  author =       "J. E. Perry",
  title =        "The effect of population enrichment in genetic
                 programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "456--461",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  notes =        "GP betting on horse races! 40 runs with pop of 2000
                 but only run for 3 generations. Best of each of these
                 loaded into consolidated run with other 1960 members of
                 population generated at random. Whole run for 50
                 generations. Works better than 3 traditional runs.",
}

@Article{petit:tbn:1998,
  author =       "Charles W. Petit",
  title =        "Touched by nature: Putting evolution to work on the
                 assembly line",
  journal =      "U. S. News and World Report",
  year =         "1999",
  month =        "27 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://pueo.mhpcc.edu/~altenber/IN_THE_MEDIA/US_NEWS.7-27-98/index.html",
  URL =          "http://www.genetic-programming.com/published/usnwr072798.html",
  size =         "13827 bytes",
  notes =        "Many aspects of EC touched upon",
}

@InProceedings{petrowski:1999:ALMOSCOP,
  author =       "A. Petrowski and S. Ben Hamida",
  title =        "A Logarithmic Mutation Operator to Solve Constrained
                 Optimization Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "805",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{petry:1995:appm,
  author =       "Frederick E. Petry and Bertrand Daniel Dunay",
  title =        "Automatic programming and program maintenance with
                 genetic programming",
  journal =      "International Journal of Software Engineering and
                 Knowledge Engineering",
  year =         "1995",
  volume =       "5",
  number =       "2",
  pages =        "165--177",
  keywords =     "genetic algorithms, genetic programming machine
                 learning, encapsulation, software reuse, software
                 maintenance",
  abstract =     "

                 ",
  notes =        "

                 Simple demonstration (NB series of regular languages)
                 Turing machines are synthesised, encapsulated, reused.
                 Library of evolved TM is created. Library programs can
                 be used by other problems. The library is maintained by
                 replacing programs if new and better versions of TM it
                 contains are evolved so its contents are kept valid.",
}

@InProceedings{peysakhov:2000:RL,
  author =       "Maxim Peysakhov",
  title =        "Representation and evolution of Lego-based
                 assemblies",
  booktitle =    "Graduate Student Workshop",
  year =         "2000",
  editor =       "Conor Ryan and Una-May O'Reilly and William B.
                 Langdon",
  pages =        "297--300",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InCollection{phelps:2000:VRDMESGP,
  author =       "Adam Phelps",
  title =        "Virtual Rodents: Discovery of Maze Exploration
                 Solutions using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "296--305",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{phelps:2002:gecco:workshop,
  title =        "Co-Evolution of Auction Mechanisms and Trading
                 Strategies: Towards a Novel Approach to Microeconomic
                 Design",
  author =       "Steve Phelps and Simon Parsons and Peter McBurney and
                 Elizabeth Sklar",
  pages =        "65--72",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InCollection{philips:2000:GAAC,
  author =       "Webb Philips",
  title =        "Genetic Algorithms in Audio Compression",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "306--311",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InCollection{pietrasik:1995:OUGPECD,
  author =       "Dan Pietrasik",
  title =        "On the Use of Genetic Programming in Elevator Control
                 Design",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "229--238",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{pindor:1999:UGA,
  author =       "Andrzej J. Pindor",
  title =        "Using Genetic Algorithm to manipulate polynomial
                 expressions",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1666--1671",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{pingel:1998:CGA,
  author =       "Adam Pingel",
  title =        "Compression by Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "128--136",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{plagianakos:1999:TNNIW,
  author =       "V. P. Plagianakos and M. N. Vrahatis",
  title =        "Training Neural Networks with 3-bit Integer Weights",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "910--915",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Misc{podgorelec:1999:MDPGP,
  author =       "Vili Podgorelec",
  title =        "Medical Diagnosis Prediction using Genetic
                 Programming",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming, decision
                 support systems, medical diagnosis prediction, software
                 metrics",
  URL =          "http://mario.uni-mb.si/~vili/gecco99/",
  notes =        "context free grammar. proGenesys. Fractal metrics
                 alpha used to define and control program complexity via
                 the fitness function. {"}Because the evaluation
                 function could not evaluate those parts [ie introns or
                 bloated code], they remain random and have therefore no
                 real meaning. Our metric alpha cold discover those
                 parts and the results afterwards were much more
                 promising{"}",
}

@InProceedings{podgorelec:2000:fpbfcm,
  author =       "Vili Podgorelec and Kokol",
  title =        "Fighting Program Bloat with the Fractal Complexity
                 Measure",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "326--337",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "The problem of evolving decision programs to be used
                 for medical diagnosis prediction brought us to the
                 problem, well know to the genetic programming (GP)
                 community: the tendency of programs to grow in length
                 too fast. While searching for a solution we found out
                 that an appropriately defined fractal complexity
                 measure can differentiate between random and non-random
                 computer programs by measuring the fractal structure of
                 the computer programs. Knowing this fact, we introduced
                 the fractal measure alpha in the evaluation and
                 selection phase of the evolutionary process of decision
                 program induction, which resulted in a significant
                 program bloat reduction.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InCollection{pohlheim:1996:gspmuGA,
  author =       "Hartmut Pohlheim and Peter Marenbach",
  title =        "Generation of structured process models using genetic
                 algorithms",
  booktitle =    "Evolutionary Computing",
  publisher =    "Springer-Verlag",
  year =         "1996",
  editor =       "T. C. Fogarty",
  number =       "1143",
  series =       "Lecture Notes in Computer Science",
  pages =        "102--109",
  address =      "University of Sussex, UK",
  month =        "1-2 " # apr,
  keywords =     "genetic algorithms, genetic programming, modelling,
                 SMOG",
  ISBN =         "3-540-61749-3",
  URL =          "http://www.rt.e-technik.tu-darmstadt.de/~mali/GP/publications.html",
  notes =        "The post-workshop proceedings of the 1996 AISB
                 workshop on evolutionary computing.

                 In this article describes further results taken form an
                 application of our approach on to artifical example
                 process which was designed and simulated using Matlab
                 and Simulink. from peter home page",
  size =         "8 pages",
}

@InProceedings{pohlheim:1999:OCGCRWDEA,
  author =       "Hartmut Pohlheim and Adolf Heissner",
  title =        "Optimal Control of Greenhouse Climate using Real-World
                 Weather Data and Evolutionary Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1672--1677",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{pohlheim:1999:TTBRSMEEA,
  author =       "Hartmut Pohlheim and Joachim Wegener",
  title =        "Testing the Temporal Behavior of Real-Time Software
                 Modules using Extended Evolutionary Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1795",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{pohlheim:1999:VEASSTMV,
  author =       "Hartmut Pohlheim",
  title =        "Visualization of Evolutionary Algorithms - Set of
                 Standard Techniques and Multidimensional
                 Visualization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "533--540",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 Genetic and Evolutionary Algorithm Toolbox (GEATbx) for
                 use with Matlab. http://www.geatbx.com/",
}

@InProceedings{poil:2001:egsthccsm,
  author =       "Riccardo Poli and Nicholas F. McPhee",
  title =        "Exact {GP} Schema Theory for Headless Chicken
                 Crossover and Subtree Mutation",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1062--1069",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, schema
                 theory, headless chicken crossover, subtree mutation,
                 operator biases",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@InCollection{Poland:1997:wspaGP,
  author =       "Douglas N. Poland",
  title =        "Evolution of a Sailboat Piloting Algorithm using
                 Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "186--196",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  notes =        "part of koza:1997:GAGPs",
}

@TechReport{poli:1996:GPiaTR,
  author =       "Riccardo Poli",
  title =        "Genetic Programming for Image Analysis",
  institution =  "University of Birmingham, UK",
  year =         "1996",
  type =         "Technical Report",
  number =       "CSRP-96-1",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk//pub/tech-reports/1996/CSRP-96-01.ps.gz",
  abstract =     "This paper describes an approach to using GP for image
                 analysis based on the idea that image enhancement,
                 feature detection and image segmentation can be
                 re-framed as image filtering problems. GP can be used
                 to discover efficient optimal filters which solve such
                 problems. However, in order to make the search feasible
                 and effective, terminal sets, function sets and fitness
                 functions have to meet some requirements. In the paper
                 these requirements are described and terminals,
                 functions and fitness functions that satisfy them are
                 proposed. Some preliminary experiments are also
                 reported in which GP (with the mentioned
                 characteristics) is applied to the segmentation of the
                 brain in magnetic resonance images (an extremely
                 difficult problem for which no simple solution is
                 known) and compared with artificial neural nets.",
  notes =        "

                 http://www.cs.bham.ac.uk/system/auto-gen/staff/rmp.html",
  size =         "10 pages",
}

@InCollection{poli:1996:GPfdis,
  author =       "Riccardo Poli",
  title =        "Genetic Programming for Feature Detection and Image
                 Segmentation",
  booktitle =    "Evolutionary Computing",
  publisher =    "Springer-Verlag",
  year =         "1996",
  editor =       "T. C. Fogarty",
  number =       "1143",
  series =       "Lecture Notes in Computer Science",
  pages =        "110--125",
  address =      "University of Sussex, UK",
  month =        "1-2 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61749-3",
  notes =        "The post-workshop proceedings of the 1996 AISB
                 workshop on evolutionary computing.",
  size =         "16 pages",
}

@InProceedings{poli:1996:GPia,
  author =       "Riccardo Poli",
  title =        "Genetic Programming for Image Analysis",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "363--368",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{poli:1996:WSC,
  author =       "R. Poli",
  title =        "Some Steps Towards a Form of Parallel Distributed
                 Genetic Programming",
  booktitle =    "The 1st Online Workshop on Soft Computing (WSC1)",
  year =         "1996",
  address =      "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/",
  month =        "19--30 " # aug,
  organisation = "Research Group on ECOmp of the Society of Fuzzy Theory
                 and Systems (SOFT)",
  publisher =    "Nagoya University, Japan",
  keywords =     "genetic algorithms, genetic programming, parallel
                 programs, graphs, evolutionary computation",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/R.Poli/wsc96.ps.gz",
  url_2 =        "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/files/poli.ps",
  URL =          "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/papers/files/poli.ps.gz",
  abstract =     "Genetic Programming is a method of program discovery
                 consisting of a special kind of genetic algorithm
                 capable of operating on non-linear chromosomes (parse
                 trees) representing programs and an interpreter which
                 can run the programs being optimised. This paper
                 describes PDGP (Parallel Distributed Genetic
                 Programming), a new form of genetic programming which
                 is suitable for the development of fine-grained
                 parallel programs. PDGP is based on a graph-like
                 representation for parallel programs which is
                 manipulated by crossover and mutation operators which
                 guarantee the syntactic correctness of the offspring.
                 The paper describes these operators and reports some
                 preliminary results obtained with this paradigm.",
  size =         "6 pages",
  notes =        "email WSC1 organisers
                 wsc@bioele.nuee.nagoya-u.ac.jp

                 Here PDGP was used to solve the even-3 parity problem.
                 Results were better than Koza's with a minimum effort
                 of 25,000 evals with one particular grid configuration,
                 and many grid configurations giving E<50,000",
}

@TechReport{poli:1996:sn-snnTR,
  author =       "R. Poli",
  title =        "Discovery of Symbolic, Neuro-Symbolic and Neural
                 Networks with Parallel Distributed Genetic
                 Programming",
  institution =  "University of Birmingham, UK",
  year =         "1996",
  month =        aug,
  note =         "Presented at 3rd International Conference on
                 Artificial Neural Networks and Genetic Algorithms,
                 ICANNGA'97",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/authors/R.Poli/icannga97.ps.gz",
  notes =        "See also poli:1997:sn-snn
                 http://www.cs.bham.ac.uk/system/auto-gen/staff/rmp.html",
}

@InProceedings{poli:1997:sn-snn,
  author =       "Riccardo Poli",
  title =        "Discovery of Symbolic, Neuro-Symbolic and Neural
                 Networks with Parallel Distributed Genetic
                 Programming",
  booktitle =    "3rd International Conference on Artificial Neural
                 Networks and Genetic Algorithms, ICANNGA'97",
  year =         "1997",
  address =      "University of East Anglia, Norwich, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~rmp/papers/Poli-ICANNGA1997.ps.gz",
  size =         "4 pages",
  notes =        "see also poli:1996:sn-snnTR
                 http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html

                 Here PDGP was applied to the XOR problem with the only
                 objective of exploring the possibilities offered by
                 this representation. No comparison with GP was made.
                 Weights were added to the links between nodes, to allow
                 the evolution of all sorts of things (logic networks,
                 neural nets, neuro-algebraic nets, etc.). Efforts with
                 logic nets were in the region of 2,000-3,000",
}

@TechReport{Poli:1996:nnPDGP,
  author =       "Riccardo Poli",
  title =        "Discovery of Symbolic, Neuro-Symbolic and Neural
                 Networks with Parallel Distributed Genetic
                 Programming",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-96-14",
  month =        aug,
  year =         "1996",
  email =        "R.Poli@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSRP-96-14.ps.gz",
  abstract =     "Genetic Programming is a method of program discovery
                 consisting of a special kind of genetic algorithm
                 capable of operating on parse trees representing
                 programs and an interpreter which can run the programs
                 being optimised. This paper describes Parallel
                 Distributed Genetic Programming (PDGP), a new form of
                 genetic programming which is suitable for the
                 development of parallel programs in which symbolic and
                 neural processing elements can be combined a in free
                 and natural way. PDGP is based on a graph-like
                 representation for parallel programs which is
                 manipulated by crossover and mutation operators which
                 guarantee the syntactic correctness of the offspring.
                 The paper describes these operators and reports some
                 results obtained with the exclusive-or problem.",
}

@TechReport{poli:1996:PDGPtr,
  author =       "Riccardo Poli",
  title =        "Parallel Distributed Genetic Programming",
  institution =  "School of Computer Science",
  year =         "1996",
  type =         "Technical Report",
  number =       "CSRP-96-15",
  address =      "University of Birmingham, B15 2TT, UK",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSRP-96-15.ps.gz",
  notes =        "PDGP",
  size =         "33 pages",
}

@TechReport{poli:1997:schemaTR,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "A New Schema Theory for Genetic Programming with
                 One-Point Crossover and Point Mutation",
  institution =  "School of Computer Science",
  year =         "1997",
  type =         "Technical Report",
  number =       "CSRP-97-3",
  address =      "The University of Birmingham, B15 2TT, UK",
  month =        jan,
  note =         "Presented at GP-97",
  keywords =     "genetic algorithms, genetic programming, POP-11",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-03.ps.gz",
  abstract =     "In this paper we first review the main results
                 obtained in the theory of schemata in Genetic
                 Programming (GP) emphasising their strengths and
                 weaknesses. Then we propose a new, simpler definition
                 of the concept of schema for GP which is quite close to
                 the original concept of schema in genetic algorithms
                 (GAs). Along with a new form of crossover, one-point
                 crossover, and point mutation this concept of schema
                 has been used to derive an improved schema theorem for
                 GP which describes the propagation of schemata from one
                 generation to the next. In the paper we discuss this
                 result and show that our schema theorem is the natural
                 counterpart for GP of the schema theorem for GAs, to
                 which it asymptotically converges.",
  notes =        "Originally 9 pages (see poli:1998:schema,
                 poli:1997:schema), Revisised August 1997",
  size =         "25 pages",
}

@TechReport{poli:1997:userTR,
  author =       "Riccardo Poli and Stefano Cagnoni",
  title =        "Evolution of Psuedo-colouring Algorithms for Image
                 Enhancement with Interactive Genetic Programming",
  institution =  "School of Computer Science",
  year =         "1997",
  type =         "Technical Report",
  number =       "CSRP-97-5",
  address =      "The University of Birmingham, B15 2TT, UK",
  month =        jan,
  note =         "Presented at GP-97",
  keywords =     "genetic algorithms, genetic programming, POP-11,
                 multiple-sclerosis",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-5.ps.gz",
  abstract =     "In this paper we present an approach to the
                 interactive development of programs for image
                 enhancement with Genetic Programming (GP) based on
                 pseudo-colour transformations. In our approach the user
                 drives GP by deciding which individual should be the
                 winner in tournament selection. The presence of the
                 user does not only allow running GP without a fitness
                 function but it also transforms GP into a very
                 efficient search procedure capable of producing
                 effective solutions to real-life problems in only
                 hundreds of evaluations. In the paper we also propose a
                 strategy to further reduce user interaction: we record
                 the choices made by the user in interactive runs and we
                 later use them to build a model which can replace
                 him/her in longer runs. Experimental results with
                 interactive GP and with our user-modelling strategy are
                 also reported.",
  notes =        "User driven evolution of programs to create a colour
                 image from two (grey level) Magnetic Resonance images
                 of a section of a human brain.

                 Also tries modelling user using GP.

                 see also poli:1997:user",
  size =         "9 pages",
}

@InProceedings{poli:1999:STE,
  author =       "Riccardo Poli",
  title =        "Schema Theorems without Expectations",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "806",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  abstract =     "See TR CSRP-99-3",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{Poli97,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "An Experimental Analysis of Schema Creation,
                 Propagation and Disruption in Genetic Programming",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-97-8",
  month =        feb,
  year =         "1997",
  type =         "Technical Report",
  note =         "Presented at ICGA-97",
  email =        "R.Poli@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/1997/CSRP-97-08.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-08.ps.gz",
  abstract =     "In this paper we first review the main results in the
                 theory of schemata in Genetic Programming (GP)
                 emphasising their strengths and weaknesses. In
                 particular we summarise a new schema theory we have
                 recently developed for GP with one-point crossover and
                 point mutation which is based on a definition of schema
                 quite close to the one used in genetic algorithms. Then
                 we study the creation, propagation and disruption of
                 this new form of schemata in real runs, for standard
                 crossover, one-point crossover and selection only.
                 Finally, we discuss these results in the light our GP
                 schema theorem.",
  size =         "16 pages",
  notes =        "Revised 17 May 1997. See also poli:1997:eascpd",
}

@TechReport{poli:1996:RTNtr,
  author =       "Riccardo Poli",
  title =        "Evolution of Recursive Transistion Networks for
                 Natural Language Recognition with Parallel Distributed
                 Genetic Programming",
  institution =  "School of Computer Science",
  year =         "1996",
  type =         "Technical Report",
  number =       "CSRP-96-19",
  address =      "University of Birmingham, B15 2TT, UK",
  month =        dec,
  note =         "Presented at AISB-97 workshop on Evolutionary
                 Computation",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSRP-96-19.ps.gz",
  size =         "10 pages",
}

@TechReport{poli:1997:1pxoWSC2,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "Genetic Programming with One-Point Crossover and Point
                 Mutation",
  institution =  "University of Birmingham, School of Computer Science",
  address =      "Birmingham, B15 2TT, UK",
  number =       "CSRP-97-13",
  month =        "15 " # apr,
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-13.ps.gz",
  size =         "10 pages, appears in WSC2 see poli:1997:1pxoWSC2c",
}

@InProceedings{poli:1997:1pxoWSC2c,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "Genetic Programming with One-Point Crossover",
  booktitle =    "Soft Computing in Engineering Design and
                 Manufacturing",
  year =         "1997",
  editor =       "P. K. Chawdhry and R. Roy and R. K. Pant",
  pages =        "180--189",
  publisher_address = "Godalming, GU7 3DJ, UK",
  month =        "23-27 " # jun,
  publisher =    "Springer-Verlag London",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-76214-0",
  URL =          "http://www.bath.ac.uk/Departments/Eng/wsc2/ind_paper/p_poli.html",
  abstract =     "In recent theoretical and experimental work on
                 schemata in genetic programming we have proposed a new
                 simpler form of crossover in which the same crossover
                 point is selected in both parent programs. We call this
                 operator one-point crossover because of its similarity
                 with the corresponding operator in genetic algorithms.
                 One-point crossover presents very interesting
                 properties from the theory point of view. In this paper
                 we describe this form of crossover as well as a new
                 variant called strict one-point crossover highlighting
                 their useful theoretical and practical features. We
                 also present experimental evidence which shows that
                 one-point crossover compares favourably with standard
                 crossover.",
  notes =        "WSC2 Second On-line World Conference on Soft Computing
                 in Engineering Design and Manufacturing. also available
                 as poli:1997:1pxoWSC2",
}

@InProceedings{poli:1997:user,
  author =       "Riccardo Poli and Stefano Cagnoni",
  title =        "Genetic Programming with User-Driven Selection:
                 Experiments on the Evolution of Algorithms for Image
                 Enhancement",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "269--277",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97 see also poli:1997:userTR",
}

@InProceedings{poli:1997:schema,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "A New Schema Theory for Genetic Programming with
                 One-point Crossover and Point Mutation",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "278--285",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97, see also poli:1997:schemaTR, poli:1998:schema",
}

@InProceedings{poli:1997:eascpd,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "An Experimental Analysis of Schema Creation,
                 Propagation and Disruption in Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Seventh
                 International Conference",
  year =         "1997",
  editor =       "Thomas Back",
  pages =        "18--25",
  address =      "Michigan State University, East Lansing, MI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "19-23 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-487-1",
  size =         "8 pages",
  abstract =     "In this paper we first review the main results in the
                 theory of schemata in Genetic Programming (GP) and
                 summarise a new GP schema theory which is based on a
                 new definition of schema. Then we study the creation,
                 propagation and disruption of this new form of schemata
                 in real runs, for standard crossover, one-point
                 crossover and selection only. Finally, we discuss these
                 results in the light our GP schema theorem.",
  notes =        "ICGA-97.

                 See also Poli97",
}

@InProceedings{poli:1997:eglpPDGP,
  author =       "Riccardo Poli",
  title =        "Evolution of Graph-like Programs with Parallel
                 Distributed Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Seventh
                 International Conference",
  year =         "1997",
  editor =       "Thomas Back",
  pages =        "346--353",
  address =      "Michigan State University, East Lansing, MI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "19-23 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-487-1",
  URL =          "http://www.cs.bham.ac.uk/~rmp/papers/Poli-ICGA1997-PDGP.ps.gz",
  size =         "8 pages",
  abstract =     "Parallel Distributed Genetic Programming (PDGP) is a
                 new form of Genetic Programming (GP) suitable for the
                 development of programs with a high degree of
                 parallelism. Programs are represented in PDGP as graphs
                 with nodes representing functions and terminals, and
                 links representing the flow of control and results. The
                 paper presents the representations, the operators and
                 the interpreters used in PDGP, and describes
                 experiments in which PDGP has been compared to standard
                 GP.",
  notes =        "ICGA-97

                 Here PDGP was firstly applied to the lawnmower problem.
                 On this problem the effort scaled up (as the size of
                 the lawn was increased) 2300 times better than Std GP
                 and it scaled up linearly rather than exponentially.
                 Also the solutions found were between 10 and 30 times
                 smaller. Then PDGP was applied to the MAX problem.
                 Again the effort scaled up linearly (and 170 times
                 better than GP) rather than exponentially.",
}

@Unpublished{poli:1997:gpxls,
  author =       "Riccardo Poli",
  title =        "Is Crossover a Local Search Operator?",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, variable size
                 representation",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "3 pages",
}

@TechReport{poli:1997:tesTR,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "A Review of Theoretical and Experimental Results on
                 Schemata in Genetic Programming",
  institution =  "University of Birmingham",
  year =         "1997",
  type =         "Technical Report",
  number =       "CSRP-97-27",
  address =      "B15 2TT, UK",
  month =        nov,
  note =         "Presented at ET-97",
  email =        "R.Poli@cs.bham.ac.uk W.B.Langdon@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSRP-97-27.ps.gz",
  abstract =     "Schemata and the schema theorem, although criticised,
                 are often used to explain why genetic algorithms (GAs)
                 work. A considerable research effort has been produced
                 recently to extend the GA schema theory to Genetic
                 Programming (GP). In this paper we review the main
                 results available to date in the theory of schemata for
                 GP and some recent experimental work on schemata.",
  size =         "16 pages",
}

@InProceedings{poli:1997:RTN,
  author =       "Riccardo Poli",
  title =        "Evolution of Recursive Transistion Networks for
                 Natural Language Recognition with Parallel Distributed
                 Genetic Programming",
  booktitle =    "Evolutionary Computing",
  year =         "1997",
  editor =       "David Corne and Jonathan L. Shapiro",
  volume =       "1305",
  series =       "Lecture Notes in Computer Science",
  pages =        "163--177",
  address =      "Manchester, UK",
  month =        "11-13 " # apr,
  organisation = "AISB",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  ISBN =         "3-540-63476-2",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-63476-2",
  URL =          "http://www.cs.bham.ac.uk/~rmp/papers/Poli-AISB-1997.ps.gz",
  notes =        "see also poli:1996:RTNtr

                 Proceedings of the Workshop on Artificial Intelligence
                 and Simulation of Behaviour (AISB) International
                 Workshop on Evolutionary Computing. Workshop in
                 Manchester, UK, April 7-8, 1997

                 Here PDGP was used to evolve recursive transition
                 networks used to recognise whether natural language
                 sentences are grammatical. No comparison with GP was
                 possibile.",
}

@InProceedings{poli:1997:tes,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "A Review of Theoretical and Experimental Results on
                 Schemata in Genetic Programming",
  booktitle =    "ET'97 Theory and Application of Evolutionary
                 Computation",
  year =         "1997",
  editor =       "Chris Clack and Kanta Vekaria and Nadav Zin",
  pages =        "29--43",
  address =      "University College London, UK",
  month =        "15 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Schemata and the schema theorem, although criticised,
                 are often used to explain why genetic algorithms (GAs)
                 work. A considerable research effort has been produced
                 recently to extend the GA schema theory to Genetic
                 Programming (GP). In this paper we review the main
                 results available to date in the theory of schemata for
                 GP and some recent experimental work on schemata.",
  notes =        "http://www.cs.ucl.ac.uk/isrg/et97/ see also
                 poli:1997:tesTR",
}

@InProceedings{poli:1998:rtesGP,
  author =       "Riccardo Poli and W. B. Langdon",
  title =        "A Review of Theoretical and Experimental Results on
                 Schemata in Genetic Programming",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "1--15",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "Schemata and the schema theorem, although criticised,
                 are often used to explain why genetic algorithms (GAs)
                 work. A considerable research effort has been produced
                 recently to extend the GA schema theory to Genetic
                 Programming (GP). In this paper we review the main
                 results available to date in the theory of schemata for
                 GP and some recent experimental work on schemata",
  notes =        "EuroGP'98",
}

@TechReport{poli:1998:evsnTR,
  author =       "Riccardo Poli and William B. Langdon and Una-May
                 O'Reilly",
  title =        "Short Term Extinction Probability of Newly Created
                 Schemata, and Schema Variance and Signal-to-Noise-Ratio
                 Theorems in the Presence of Schema Creation",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-98-6",
  month =        jan,
  year =         "1998",
  email =        "R.Poli@cs.bham.ac.uk, W.B.Langdon@cs.bham.ac.uk",
  file =         "/1998/CSRP-98-06.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-06.ps.gz",
  abstract =     "This paper first analyses the impact of variance on
                 schema transmission. Working from an exact derivation
                 of the expected variance in schema transmission, it
                 derives and analyses the signal-to-noise ratio for
                 schemata. The paper then presents short term schema
                 transmission probability results that focus on newly
                 created schemata in the population. The analysis
                 reveals the relative dependencies between schema
                 transmission, population size, schema measured fitness,
                 schema fragility and schema creation.",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Presented at GP-98",
  notes =        "see poli:1998:evsn",
}

@InProceedings{poli:1998:evsn,
  author =       "Riccardo Poli and William B. Langdon and Una-May
                 O'Reilly",
  title =        "Analysis of Schema Variance and Short Term Extinction
                 Likelihoods",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "284--292",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  email =        "R.Poli@cs.bham.ac.uk, W.B.Langdon@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://cswww.essex.ac.uk/staff/poli/papers/Poli-GP1998-Schema.pdf",
  size =         "9 pages",
  abstract =     "This paper first analyses the impact of variance on
                 schema transmission. Working from an exact derivation
                 of the expected variance in schema transmission, it
                 derives and analyses the signal-to-noise ratio for
                 schemata. The paper then presents short term schema
                 transmission probability results that focus on newly
                 created schemata in the population. The analysis
                 reveals the relative dependencies between schema
                 transmission, population size, schema measured fitness,
                 schema fragility and schema creation.",
  notes =        "GP-98. Based on poli:1998:evsnTR",
}

@TechReport{poli:1998:localTR,
  author =       "Riccardo Poli and William B. Langdon",
  title =        "On the Ability to Search the Space of Programs of
                 Standard, One-point and Uniform Crossover in Genetic
                 Programming",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-98-7",
  month =        jan,
  year =         "1998",
  email =        "R.Poli@cs.bham.ac.uk, W.B.Langdon@cs.bham.ac.uk",
  file =         "/1998/CSRP-98-07.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-07.ps.gz",
  abstract =     "In this paper we study and compare the search
                 properties of different crossover operators in genetic
                 programming (GP) using probabilistic models and
                 experiments to assess the amount of genetic material
                 exchanged between the parents to generate the
                 offspring. These operators are: standard crossover,
                 one-point crossover and a new operator, uniform
                 crossover. Our analysis suggests that standard
                 crossover is a local and biased search operator not
                 ideal to explore the search space of programs
                 effectively. One-point crossover is better in some
                 cases as it is able to perform a global search at the
                 beginning of a run, but it suffers from the same
                 problems as standard crossover later on. Uniform
                 crossover largely overcomes these limitations as it is
                 global and less biased.",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Presented at GP-98",
  notes =        "see poli:1998:local",
}

@InProceedings{poli:1998:local,
  author =       "Riccardo Poli and William B. Langdon",
  title =        "On the Search Properties of Different Crossover
                 Operators in Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "293--301",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  email =        "R.Poli@cs.bham.ac.uk, W.B.Langdon@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www.cs.bham.ac.uk/~rmp/papers/Poli-GP1998.ps.gz",
  abstract =     "In this paper we study and compare the search
                 properties of different crossover operators in genetic
                 programming (GP) using probabilistic models and
                 experiments to assess the amount of genetic material
                 exchanged between the parents to generate the
                 offspring. These operators are: standard crossover,
                 one-point crossover and a new operator, uniform
                 crossover. Our analysis suggests that standard
                 crossover is a local and biased search operator not
                 ideal to explore the search space of programs
                 effectively. One-point crossover is better in some
                 cases as it is able to perform a global search at the
                 beginning of a run, but it suffers from the same
                 problems as standard crossover later on. Uniform
                 crossover largely overcomes these limitations as it is
                 global and less biased.",
  notes =        "GP-98. Based on poli:1998:localTR",
}

@Proceedings{Poli:1998:egplb,
  title =        "Late Breaking Papers at Euro{GP}'98: the First
                 European Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and Marc Schoenauer
                 and Terry Fogarty and Wolfgang Banzhaf",
  address =      "Paris, France",
  month =        "14-15 " # apr,
  organisation = "EvoGP",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~wbl/eurogp98_lbp.html",
  size =         "30 pages",
  abstract =     "This booklet contains the late-breaking papers of the
                 First European Workshop on Genetic Programming
                 (EuroGP'98) held in Paris on April 14-15 1998. The
                 purpose of the late-breaking papers was to provide
                 attendees with information about research that was
                 initiated, enhanced, improved, or completed after the
                 original paper submission deadline in December 1997.

                 To ensure coverage of the most up-to-date research, the
                 deadline for submission was set only a month before the
                 workshop. Late-breaking papers were examined for
                 relevance and quality by the organisers of the
                 EuroGP'98 and one of the other members of the programme
                 committee (Bill Langdon), but no formal review process
                 took place.

                 The 7 late-breaking papers in this booklet (which was
                 distributed at the workshop) were presented during a
                 poster session held on the evening of Wednesday 15
                 April 1998 during EuroGP'98.

                 Authors individually retain copyright (and all other
                 rights) to their late-breaking papers. This booklet is
                 available as technical report CSRP-98-10 from Mrs.
                 Ceinwen Cushway, the School of Computer Science, The
                 University of Birmingham, Edgbaston, Birmingham, B15
                 2TT, UK. Email: <C.Cushway@cs.bham.ac.uk> Tel:
                 +44-121-414-3735
                 http://www.cs.bham.ac.uk/system/tech-reports/tr.html",
  notes =        "EuroGP'98LB",
}

@Article{poli:1998:schema,
  author =       "Riccardo Poli and William B. Langdon",
  title =        "Schema Theory for Genetic Programming with One-point
                 Crossover and Point Mutation",
  journal =      "Evolutionary Computation",
  year =         "1998",
  volume =       "6",
  number =       "3",
  pages =        "231--252",
  keywords =     "Genetic Programming, Genetic Algorithms, Schema
                 Theorem, One-point Crossover",
  URL =          "http://cswww.essex.ac.uk/staff/poli/papers/Poli-ECJ1998.pdf",
  size =         "30 pages",
  notes =        "see also poli:1997:schema",
}

@TechReport{poli:1998:smcGPtr,
  author =       "Riccardo Poli and William B Langdon",
  title =        "Sub-machine-code Genetic Programming",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-98-18",
  month =        aug,
  year =         "1998",
  email =        "R.Poli@cs.bham.ac.uk, W.B.Langdon@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/1998/CSRP-98-18.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-18.ps.gz",
  abstract =     "CPUs are often seen as sequential, however they have a
                 high degree of internal parallelism, typically
                 operating on 32 or 64 bits simultaneously. This paper
                 explores the idea of exploiting this internal
                 parallelism to extend the scope of genetic programming
                 (GP) and improve its efficiency. We call the resulting
                 form of GP sub-machine-code GP. The differences between
                 sub-machine-code GP and the usual form of GP are purely
                 semantic and largely language independent, i.e. any GP
                 system can potentially be used to do sub-machine code
                 GP. In this chapter this form of GP and some of its
                 applications are presented. The speed up obtained with
                 this technique on Boolean classification problems is
                 nearly 2 orders of magnitude.",
}

@TechReport{poli:1999:22parTR,
  author =       "Riccardo Poli and Jonathan Page and William B.
                 Langdon",
  title =        "Solving Even-12, -13, -15, -17, -20 and -22 Boolean
                 Parity Problems using Sub-machine Code {GP} with Smooth
                 Uniform Crossover, Smooth Point Mutation and Demes",
  institution =  "University of Birmingham, School of Computer Science",
  year =         "1999",
  number =       "CSRP-99-2",
  month =        jan,
  email =        "R.Poli@cs.bham.ac.uk, J.Page@cs.bham.ac.uk,
                 bill@cwi.nl",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1999/CSRP-99-02.ps.gz",
  abstract =     "In this paper we describe a recipe to solve very large
                 parity problems, using GP without automatically defined
                 functions. The recipe includes three main ingredients:
                 smooth uniform crossover (a crossover operator inspired
                 by theoretical research), sub-machine-code GP (a
                 technique which allows speeding up fitness evaluation
                 in Boolean classification problems by nearly 2 orders
                 of magnitude), and distributed demes (weakly
                 interacting sub-populations running on separate
                 workstations). We tested this recipe on parity problems
                 with up to 22 input variables (i.e. where the fitness
                 function includes 2^22=4,194,304 fitness cases),
                 solving them with a very high success probability.",
  notes =        "presented at GECCO-99 see poli:1999:22par",
}

@TechReport{Poli:1999:pstwe,
  author =       "Riccardo Poli",
  title =        "Probabilistic Schema Theorems without Expectation,
                 Left-to-Right Convergence and Population Sizing in
                 Genetic Algorithms",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-99-3",
  month =        jan,
  year =         "1999",
  keywords =     "genetic algorithms",
  email =        "R.Poli@cs.bham.ac.uk",
  file =         "/1999/CSRP-99-03.ps.gz",
  abstract =     "In this paper we first develop a new form of schema
                 theorem in which expectations are not present. This
                 theorem allows one to predict with a known probability
                 whether the number of instances of a schema at the next
                 generation will be above a given threshold. Then we use
                 this version of the schema theorem backwards, i.e. to
                 predict the past from the future. Assuming that at
                 least one solution is found at one generation, this
                 allows us to find the conditions (at the previous
                 generation) under which such a solution will indeed be
                 found. By using the notion of left-to-right convergence
                 (i.e. the idea of discovering one new bit of the
                 solution per generation) and by recursively applying
                 the schema theorem to such conditions we can find under
                 which conditions on the initial generation the GA will
                 converge in a constant time. These results allow us to
                 write very simple population sizing equations for
                 different initialisation strategies. The results do not
                 represent a full schema-theorem-based proof of
                 convergence for GAs, because they assume the knowledge
                 of the fitness of the population and of the building
                 blocks being assembled by the GA at each generation.",
  notes =        "mistake in one stage of proof corrected and whole
                 report very substantially revised in Poli:1999:pstwe2",
}

@TechReport{Poli:1999:pstwe2,
  author =       "Riccardo Poli",
  title =        "Probabilistic Schema Theorems without Expectation,
                 Recursive Conditional Schema Theorem, Convergence and
                 Population Sizing in Genetic Algorithms",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-99-3",
  month =        jan,
  year =         "1999",
  email =        "R.Poli@cs.bham.ac.uk",
  file =         "/1999/CSRP-99-03.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1999/CSRP-99-03.ps.gz",
  abstract =     "In this paper we first develop a new form of schema
                 theorem in which expectations are not present. This
                 theorem allows one to predict with a known probability
                 whether the number of instances of a schema at the next
                 generation will be above a given threshold. Then we use
                 this version of the schema theorem backwards, i.e. to
                 predict the past from the future. Assuming that at
                 least one solution is found at one generation, this
                 allows us to find the conditions (at the previous
                 generation) under which such a solution will indeed be
                 found with a given probability. This allows us to
                 obtain a recursive version of the schema theorem. This
                 schema theorem allows one to find under which
                 conditions on the initial generation the GA will
                 converge to a solution on the hypothesis that building
                 block and population fitnesses are known. These results
                 are important because for the first time they make
                 explicit the relation between population size, schema
                 fitness and probability of convergence over multiple
                 generations.",
  notes =        "enetic-Programming@lists.Stanford.EDU Date: Thu, 16
                 Dec 1999 09:35:01 +0000 (GMT)

                 Please note that the old version had a slightly
                 different title: {"}Probabilistic Schema Theorems
                 without Expectation, Left-to-Right Convergence and
                 Population Sizing in Genetic Algorithms{"}.

                 I am glad to say that the material in the technical
                 report is unpublished (except for a small part
                 published as a GECCO'99 poster, which was not affected
                 by the mistake).",
}

@InCollection{poli:1999:aigp3,
  author =       "Riccardo Poli and William B. Langdon",
  title =        "Sub-machine-code Genetic Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "13",
  pages =        "301--323",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3. Machine code level parallelism",
}

@Proceedings{poli:1999:GP,
  title =        "Genetic Programming, Proceedings of Euro{GP}'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-65899-8",
  size =         "282 pages approx",
  notes =        "EuroGP'99",
}

@InProceedings{poli:1999:smcGP:nre,
  author =       "Riccardo Poli",
  title =        "Sub­Machine­Code {GP}: New Results and Extensions",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "65--82",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 SMC allows multiple fitness cases to be processed in
                 one go. New demo on evolving multiply and on continious
                 regression problems.",
}

@InProceedings{poli:1999:22par,
  author =       "Riccardo Poli and Jonathan Page and W. B. Langdon",
  title =        "Smooth Uniform Crossover, Sub-Machine Code {GP} and
                 Demes: {A} Recipe For Solving High-Order Boolean Parity
                 Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1162--1169",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  email =        "R.Poli@cs.bham.ac.uk, J.Page@cs.bham.ac.uk,
                 bill@cwi.nl",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99, part of banzhaf:1999:gecco99, see also
                 poli:1999:22parTR A joint meeting of the eighth
                 international conference on genetic algorithms
                 (ICGA-99) and the fourth annual genetic programming
                 conference (GP-99)",
}

@InProceedings{poli:1999:fogp,
  author =       "Riccardo Poli",
  title =        "Schema Theory without Expectations for {GP} and {GA}s
                 with One-Point Crossover in the presence of Schema
                 Creation",
  booktitle =    "Foundations of Genetic Programming",
  year =         "1999",
  editor =       "Thomas Haynes and William B. Langdon and Una-May
                 O'Reilly and Riccardo Poli and Justinian Rosca",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/poli.ps.gz",
  size =         "3 pages",
  notes =        "GECCO'99 WKSHOP, part of haynes:1999:fogp",
}

@InCollection{Poli:1999:nio,
  author =       "Riccardo Poli",
  title =        "Parallel Distributed Genetic Programming",
  booktitle =    "New Ideas in Optimization",
  publisher =    "McGraw-Hill",
  year =         "1999",
  editor =       "David Corne and Marco Dorigo and Fred Glover",
  chapter =      "27",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  ISBN =         "0-07-709506-5",
  URL =          "http://www.cs.bham.ac.uk/~rmp/papers/Poli-NIO-1999-PDGP.ps.gz",
  notes =        "

                 E.g. a symbolic regression problem x^6-2*x^4+x^2 in
                 which PDGP does 16 times better than std GP and 13
                 times better than GP with ADFs.",
  size =         "pages",
}

@TechReport{Poli00-2,
  author =       "Riccardo Poli",
  title =        "On Fitness Proportionate Selection and the Schema
                 Theorem in the Presence of Stochastic Effects",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-00-2",
  month =        feb,
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  email =        "R.Poli@cs.bham.ac.uk",
  file =         "/2000/CSRP-00-02.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-02.ps.gz",
  abstract =     "Holland's schema theorem has been criticised in (Fogel
                 and Ghozeil 1997, Fogel and Ghozeil 1998, Fogel and
                 Ghozeil 1999) for not being able to estimate correctly
                 the expected proportion of a schema in the population
                 when fitness proportionate selection is used in the
                 presence of noise or other stochastic effects. This is
                 incorrect for two reasons. Firstly, the theorem in its
                 original form is not applicable to this case. As
                 clarified in the paper, if the quantities involved in
                 schema theorems are random variables, the theorems must
                 be interpreted as conditional statements. Secondly, the
                 conditional versions of Holland and other researchers'
                 schema theorems are indeed very useful to model the
                 sampling of schemata in the presence of stochasticity.
                 In the paper I show how one can calculate the correct
                 expected proportion of a schema in the presence of
                 stochastic effects when selection only is present,
                 using a conditional interpretation of Holland's schema
                 theorem. In addition, I generalise this result (again
                 using schema theorems) to the case in which crossover,
                 mutation, and selection with replacement are used. This
                 can be considered as an exact schema theorem applicable
                 both in the presence and in the absence of stochastic
                 effects.",
}

@Proceedings{poli:2000:GP,
  title =        "Genetic Programming, Proceedings of Euro{GP}'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67339-3",
  size =         "361 pages",
  notes =        "EuroGP'2000",
}

@InProceedings{poli:2000:htGP1xbb,
  author =       "R. Poli",
  title =        "Hyperschema Theory for {GP} with One-Point Crossover,
                 Building Blocks, and Some New Results in {GA} Theory",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "163--180",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "Two main weaknesses of GA and GP schema theorems are
                 that they provide only information on the expected
                 value of the number of instances of a given schema at
                 the next generation <i>E[m(H,t+1)]</i>, and they can
                 only give a lower bound for such a quantity. This paper
                 presents new theoretical results on GP and GA schemata
                 which largely overcome these weaknesses. Firstly,
                 unlike previous results which concentrated on schema
                 survival and disruption, our results extend to GP
                 recent work on GA theory by Stephens and Waelbroeck,
                 and make the effects and the mechanisms of schema
                 creation explicit. This allows us to give an exact
                 formulation (rather than a lower bound) for the
                 expected number of instances of a schema at the next
                 generation. Thanks to this formulation we are then able
                 to provide in improved version for an earlier GP schema
                 theorem in which some schema creation events are
                 accounted for, thus obtaining a tighter bound for
                 <i>E[m(H,t+1)]</i>. This bound is a function of the
                 selection probabilities of the schema itself and of a
                 set of lower-order schemata which one-point crossover
                 uses to build instances of the schema. This result
                 supports the existence of building blocks in GP which,
                 however, are not necessarily all short, low-order or
                 highly fit. Building on earlier work, we show how
                 Stephens and Waelbroeck's GA results and the new GP
                 results described in the paper can be used to evaluate
                 schema variance, signal-to-noise ratio and, in general,
                 the probability distribution of <i>m(H,t+1)</i>. In
                 addition, we show how the expectation operator can be
                 removed from the schema theorem so as to predict with a
                 known probability whether <i>m(H,t+1)</i> (rather than
                 <i>E[m(H,t+1)]</i>) is going to be above a given
                 threshold.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@Article{poli:2000:22par,
  author =       "Riccardo Poli and Jonathan Page",
  title =        "Solving High-Order Boolean Parity Problems with Smooth
                 Uniform Crossover, Sub-Machine Code {GP} and Demes",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "1/2",
  pages =        "37--56",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, uniform
                 crossover, subsymbolic representation, sub-machine-code
                 gp, demes, parity problems",
  ISSN =         "1389-2576",
  abstract =     "We propose and study new search operators and a novel
                 node representation that can make GP fitness landscapes
                 smoother. Together with a tree evaluation method known
                 as sub-machine-code GP and the use of demes, these make
                 up a recipe for solving very large parity problems
                 using GP. We tested this recipe on parity problems with
                 up to 22 input variables, solving them with a very high
                 success probability.",
}

@InProceedings{Poli:2000:GECCO,
  author =       "Riccardo Poli",
  title =        "Exact Schema Theorem and Effective Fitness for {GP}
                 with One-Point Crossover",
  pages =        "469--476",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@TechReport{Poli00,
  author =       "Riccardo Poli and Nicholas Freitag McPhee",
  title =        "Exact Schema Theorems for {GP} with One-Point and
                 Standard Crossover Operating on Linear Structures and
                 their Application to the Study of the Evolution of
                 Size",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-00-14",
  month =        oct,
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  email =        "R.Poli@cs.bham.ac.uk, N.F.McPhee@cs.bham.ac.uk",
  file =         "/2000/CSRP-00-14.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-14.ps.gz",
  abstract =     "In this paper, firstly we specialise the exact GP
                 schema theorem for one-point crossover to the case of
                 linear structures of variable length, for example
                 binary strings or programs with arity-1 primitives
                 only. Secondly, we extend this to an exact schema
                 theorem for GP with standard crossover applicable to
                 the case of linear structures. Then we study, both
                 mathematically and numerically, the schema equations
                 and their fixed points for infinite populations for
                 both a constant and a length-related fitness function.
                 This allows us to characterise the bias induced by
                 standard crossover. This is very peculiar. In the case
                 of a constant fitness function, at the fixed-point,
                 structures of any length are present with non-zero
                 probability. However, shorter structures are sampled
                 exponentially much more frequently than longer ones.",
}

@TechReport{Poli00b,
  author =       "Riccardo Poli",
  title =        "Microscopic and Macroscopic Schema Theories for
                 Genetic Programming and Variable-length Genetic
                 Algorithms with One-Point Crossover, their Use and
                 their Relations with Earlier {GP} and {GA} Schema
                 Theories",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-00-15",
  month =        oct,
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  email =        "R.Poli@cs.bham.ac.uk",
  file =         "/2000/CSRP-00-15.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-15.ps.gz",
  abstract =     "A few schema theorems for GP have been proposed in the
                 literature in the last few years. One of their main
                 weaknesses is that they provide only a lower bound for
                 the expected value of the number of instances of a
                 given schema H at the next generation, E[m(H,t+1)],
                 rather than an exact value. This paper presents new
                 theoretical results for GP with one-point crossover
                 which overcome this problem. Firstly, we give an exact
                 formulation (rather than a lower bound) for the
                 expected number of instances of a schema at the next
                 generation in terms of microscopic quantities. Thanks
                 to this formulation we are then able to provide in
                 improved version for an earlier GP schema theorem in
                 which some (but not all) schema creation events are
                 accounted for, thus obtaining a tighter bound for
                 E[m(H,t+1)]. Then, we extend the microscopic schema
                 theorem to obtain an exact formulation of E[m(H,t+1)]
                 in terms of macroscopic quantities. In this formulation
                 E[m(H,t+1)] is a function of the selection
                 probabilities of the schema itself and of a set of
                 lower-order schemata which one-point crossover uses to
                 build instances of the schema. This result makes the
                 effects and the mechanisms of schema creation explicit,
                 unlike previous work which concentrated on schema
                 survival and disruption. This supports the existence of
                 building blocks in GP which, however, are not
                 necessarily all short, low-order or highly fit. Also,
                 the macroscopic schema theorem allows the exact
                 formulation of the notion of effective fitness in GP
                 and opens the way to future work on GP convergence,
                 population sizing, operator biases, and bloat, only to
                 mention some possibilities.",
}

@TechReport{Poli00-16,
  author =       "Riccardo Poli",
  title =        "General Schema Theory for Genetic Programming with
                 Subtree-Swapping Crossover",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-00-16",
  month =        nov,
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  email =        "R.Poli@cs.bham.ac.uk",
  file =         "/2000/CSRP-00-16.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-16.ps.gz",
  abstract =     "In this paper a new general schema theory for genetic
                 programming is presented. Like other recent GP schema
                 theory results, the theory gives an exact formulation
                 (rather than a lower bound) for the expected number of
                 instances of a schema at the next generation. The
                 theory is based on a Cartesian node reference system
                 which makes it possible to describe programs as
                 functions over the space N^2 and allows one to model
                 the process of selection of the crossover points of
                 subtree-swapping crossovers as a probability
                 distribution over N^4. The theory is also based on the
                 notion of variable-arity hyperschema, which generalises
                 previous definitions of schema or hyperschema
                 introduced in GP. The theory includes two main theorems
                 describing the propagation of GP schemata: a
                 microscopic schema theorem and a macroscopic one. The
                 microscopic version is applicable to crossover
                 operators which replace a subtree in one parent with a
                 subtree from the other parent to produce the offspring.
                 Therefore, this theorem is equally applicable to
                 standard GP crossover with and without uniform
                 selection of the crossover points, as it is to
                 one-point crossover, size-fair crossover,
                 strongly-typed GP crossover, context-preserving
                 crossover and many others. The macroscopic version is
                 applicable to crossover operators in which the
                 probability of selecting any two crossover points in
                 the parents depends only on their size and shape. In
                 the paper we provide examples which show how the theory
                 can be specialised to specific crossover operators and
                 how it can be used to derive other general results such
                 as an exact definition of effective fitness and a
                 size-evolution equation for GP with subtree-swapping
                 crossover.",
}

@TechReport{Poli00-23,
  author =       "Riccardo Poli and Nicholas Freitag McPhee",
  title =        "Exact {GP} Schema Theory for Headless Chicken
                 Crossover and Subtree Mutation",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-00-23",
  month =        dec,
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  email =        "R.Poli@cs.bham.ac.uk, N.F.McPhee@cs.bham.ac.uk",
  file =         "/2000/CSRP-00-23.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-23.ps.gz",
  abstract =     "In this paper a new general GP schema theory for
                 headless chicken crossover and subtree mutation is
                 presented. The theory gives an exact formulation for
                 the expected number of instances of a schema at the
                 next generation. The theory includes four main results:
                 microscopic schema theorems for both headless chicken
                 crossover and subtree mutation, and two corresponding
                 macroscopic theorems. The microscopic versions are
                 applicable to headless chicken crossovers and subtree
                 mutation operators. The macroscopic versions are valid
                 for slightly more restricted sets of headless chicken
                 and mutation operators in which the probability of
                 selecting the crossover/mutation point(s) depends only
                 on the size and shape of the parent program(s). In the
                 paper we provide examples which show how the theory can
                 be specialised to specific operators.",
}

@TechReport{Poli01,
  author =       "Riccardo Poli and Jon E Rowe and Nicholas F McPhee",
  title =        "Markov Models for {GP} and Variable-length {GA}s with
                 Homologous Crossover",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-01-6",
  month =        jan,
  year =         "2001",
  email =        "R.Poli@cs.bham.ac.uk, J.E.Rowe@cs.bham.ac.uk,
                 N.F.McPhee@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/2001/CSRP-01-06.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2001/CSRP-01-06.ps.gz",
  abstract =     "In this paper we present a Markov model for GP and
                 variable-length GAs with homologous crossover: a set of
                 operators where the offspring are created preserving
                 the position of the genetic material taken from the
                 parents. We obtain this result by using the core of
                 Vose's model for GAs in conjunction with a
                 specialisation of recent schema theory for such
                 operators. The model is then specialised for the case
                 of GAs operating on variable-length strings, where
                 symmetries can be exploited to obtain further
                 simplifications. In the absence of mutation, the theory
                 presented here generalises Vose's GA model to GP and
                 variable-length~GAs.",
}

@InProceedings{poli:2001:EuroGP_exact,
  author =       "Riccardo Poli and Nicholas Freitag McPhee",
  title =        "Exact Schema Theorems for {GP} with One-Point and
                 Standard Crossover Operating on Linear Structures and
                 their Application to the Study of the Evolution of
                 Size",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "126--142",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Schema
                 theory, Crossover, Crossover bias, Standard Crossover,
                 Fixed points, Variable-length Genetic Algorithms,",
  ISBN =         "3-540-41899-7",
  size =         "17 pages",
  abstract =     "In this paper, firstly we specialise the exact GP
                 schema theorem for one-point crossover to the case of
                 linear structures of variable length, for example
                 binary strings or programs with arity-1 primitives
                 only. Secondly, we extend this to an exact schema
                 theorem for GP with standard crossover applicable to
                 the case of linear structures. Then we study, both
                 mathematically and numerically, the schema equations
                 and their fixed points for infinite populations for
                 both a constant and a length-related fitness function.
                 This allows us to characterise the bias induced by
                 standard crossover. This is very peculiar. In the case
                 of a constant fitness function, at the fixed-point,
                 structures of any length are present with non-zero
                 probability. However, shorter structures are sampled
                 exponentially much more frequently than longer ones.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{poli:2001:EuroGP_general,
  author =       "Riccardo Poli",
  title =        "General Schema Theory for Genetic Programming with
                 Subtree-Swapping Crossover",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "143--159",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Schema
                 theory, Crossover, Subtree-swapping Crossover, Standard
                 Crossover, Evolution of size, Bloat, Variable-length
                 Genetic Algorithms",
  ISBN =         "3-540-41899-7",
  size =         "17 pages",
  abstract =     "In this paper a new, general and exact schema theory
                 for genetic programming is presented. The theory
                 includes a microscopic schema theorem applicable to
                 crossover operators which replace a subtree in one
                 parent with a subtree from the other parent to produce
                 the offspring. A more macroscopic schema theorem is
                 also provided which is valid for crossover operators in
                 which the probability of selecting any two crossover
                 points in the parents depends only on their size and
                 shape. The theory is based on the notions of Cartesian
                 node reference systems and variable-arity hyperschemata
                 both introduced here for the first time. In the paper
                 we provide examples which show how the theory can be
                 specialised to specific crossover operators and how it
                 can be used to derive an exact definition of effective
                 fitness and a size-evolution equation for GP.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@Article{poli:2001:GPEM,
  author =       "Riccardo Poli",
  title =        "Exact Schema Theory for Genetic Programming and
                 Variable-Length Genetic Algorithms with One-Point
                 Crossover",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "2",
  pages =        "123--163",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, schema
                 theory, one-point crossover, variable-length genetic
                 algorithms",
  ISSN =         "1389-2576",
  URL =          "http://ipsapp009.lwwonline.com/content/getfile/4723/5/4/fulltext.pdf",
  abstract =     "A few schema theorems for genetic programming (GP)
                 have been proposed in the literature in the last few
                 years. Since they consider schema survival and
                 disruption only, they can only provide a lower bound
                 for the expected value of the number of instances of a
                 given schema at the next generation rather than an
                 exact value. This paper presents theoretical results
                 for GP with one-point crossover which overcome this
                 problem. First, we give an exact formulation for the
                 expected number of instances of a schema at the next
                 generation in terms of microscopic quantities. Due to
                 this formulation we are then able to provide an
                 improved version of an earlier GP schema theorem in
                 which some (but not all) schema creation events are
                 accounted for. Then, we extend this result to obtain an
                 exact formulation in terms of macroscopic quantities
                 which makes all the mechanisms of schema creation
                 explicit. This theorem allows the exact formulation of
                 the notion of effective fitness in GP and opens the way
                 to future work on GP convergence, population sizing,
                 operator biases, and bloat, to mention only some of the
                 possibilities.",
}

@InProceedings{poli2:2001:gecco,
  title =        "Markov Chain Models for {GP} and Variable-length {GA}s
                 with Homologous Crossover",
  author =       "Riccardo Poli and Jonathan E. Rowe and Nicholas
                 Freitag McPhee",
  pages =        "112--119",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, Markov chain
                 models, variable-length genetic algorithms, homologous
                 crossover, schema theory, mixing matrices",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{poli:2001:gecco,
  title =        "Exact Schema Theory for {GP} and Variable-length {GA}s
                 with Homologous Crossover",
  author =       "Riccardo Poli and Nicholas Freitag McPhee",
  pages =        "104--111",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, schema
                 theory, homologous crossover, variable-length genetic
                 algorithm, recombination distributions, crossover
                 masks",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{poli:2001:deaapd,
  author =       "Riccardo Poli and Chris Stephens",
  title =        "Dynamics of Evolutionary Algorithms: {A} Panel
                 Discussion",
  booktitle =    "Dynamics of Evolutionary Algorithms",
  year =         "2001",
  editor =       "Chris Stephens and Riccardo Poli",
  pages =        "334",
  address =      "San Francisco, California, USA",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}

@TechReport{poli:2002:dagstuhl,
  title =        "Exact Schema Theorems and Markov Chain Models for
                 Genetic Programming and Variable Length Genetic
                 Algorithms",
  author =       "Riccardo Poli",
  editor =       "Hans-Georg Beyer and Ken {De Jong} and Colin Reeves
                 and Ingo Wegener",
  booktitle =    "Theory of Evolutionary Algorithms",
  institution =  "Dagstuhl",
  year =         "2002",
  type =         "Report",
  number =       "330",
  address =      "Germany",
  month =        "13-18 " # jan,
  pages =        "14",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.dagstuhl.de/pub/Reports/02/02031.ps.gz",
  URL =          "ftp://ftp.dagstuhl.de/pub/Reports/02/02031.pdf.gz",
  abstract =     "In my talk I have given an overview of the recent
                 advances in the schema theory for genetic programming
                 (GP) and variable length genetic algorithms (GAs). I
                 have also indicated how GP theory is a generalization
                 of the corresponding theory for GAs operating on xed
                 length strings. In the talk I have also shown how one
                 can extend the Nix and Vose Markov chain model for GAs
                 to GP and variable length GAs. Finally I have brie y
                 indicated some applications of the schema theory,
                 including extensions of Geiringer's theorem to variable
                 length strings under homologous and subtree
                 crossover.",
  notes =        "Seminar No. 02031",
}

@InProceedings{poli:2002:EuroGP,
  title =        "Allele Diffusion in Linear Genetic Programming and
                 Variable-Length Genetic Algorithms with Subtree
                 Crossover",
  author =       "Riccardo Poli and Jonathan E. Rowe and Christopher R.
                 Stephens and Alden H. Wright",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "212--227",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "In this paper we study, theoretically, the search
                 biases produced by GP subtree crossover when applied to
                 linear representations, such as those used in linear GP
                 or in variable length GAs. The study naturally leads to
                 generalisations of Geiringer s theorem and of the
                 notion of linkage equilibrium, which, until now, were
                 applicable only to fixed-length representations. This
                 indicates the presence of a diffusion process by which,
                 even in the absence of selective pressure and mutation,
                 the alleles in a particular individual tend not just to
                 be swapped with those of other individuals in the
                 population, but also to diffuse within the
                 representation of each individual. More precisely,
                 crossover attempts to push the population towards
                 distributions of primitives where each primitive is
                 equally likely to be found in any position in any
                 individual.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{poli:2002:gecco,
  author =       "Riccardo Poli and Christopher R. Stephens and Alden H.
                 Wright and Jonathan E. Rowe",
  title =        "On The Search Biases Of Homologuous Crossover In
                 Linear Genetic Programming And Variable-length Genetic
                 Algorithms",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "868--876",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, Geiringer's
                 theorem, homologous crossover, linkage equilibrium,
                 schema theory, variable length genetic algorithms",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)

                 Nominated for best at GECCO award",
}

@InProceedings{poli:2002:foga,
  author =       "Poli and Stephens and Wright and Rowe",
  title =        "A schema theory based extension of Geiringer's theorem
                 for linear {GP} and variable length {GA}s under
                 homologous crossover",
  booktitle =    "Foundations of Genetic Algorithms {VII}",
  year =         "2002",
  editor =       "Jonathan Rowe and Riccardo Poli and Kenneth A. {De
                 Jong}",
  address =      "Torremolinos, Spain",
  publisher_address = "San Francisco, CA, USA",
  month =        "4-6 " # sep,
  publisher =    "Morgan Kaufmann",
  note =         "Accepted",
  keywords =     "genetic algorithms, genetic programming",
}

@Article{poli:2002:bookshelf,
  author =       "Riccardo Poli",
  title =        "Bookshelf Foundations of Generic Programing",
  journal =      "Wyvern",
  year =         "2002",
  pages =        "8",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.essex.ac.uk/wyvern/2002-03/research.htm#bookshelf",
  URL =          "http://www.essex.ac.uk/wyvern/2002-03/Wyvern_Mar02.pdf",
  notes =        "Newsletter University of Essex, review of
                 langdon:fogp",
}

@InProceedings{Polito:1997:mxm,
  author =       "J. Polito and J. Daida and T. F. Bersano-Begey",
  title =        "Musica ex Machina: Composing 16th-Century Counterpoint
                 with Genetic Programming and Symbiosis",
  booktitle =    "Evolutionary Programming VI: Proceedings of the Sixth
                 Annual Conference on Evolutionary Programming",
  year =         "1997",
  editor =       "Peter J. Angeline and Robert G. Reynolds and John R.
                 McDonnell and Russ Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  address =      "Indianapolis, Indiana, USA",
  publisher_address = "Berlin",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/EP97muse.pdf",
  notes =        "EP-97, see also
                 http://www-personal.engin.umich.edu/~daida/muse/EP97muse.html
                 which includes an auralization of the paper's score.",
}

@InProceedings{poon:1999:PSSEC,
  author =       "Josiah Poon",
  title =        "Problem Solving: Search, Exploration and
                 Co-evolution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "541--548",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{porter:1996:capd,
  author =       "Mark Porter and Mark Willis and Hugo Hiden",
  title =        "Computer-aided Polymer Design using Genetic
                 Programming",
  institution =  "Chemical Engineering, Newcastle University",
  year =         "1996",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper8a.ps",
  abstract =     "Developing new polymeric materials that satisfy bulk
                 property design objectives specified by an engineer is
                 of great importance. Industrial organisations, as well
                 as social and economic factors, have increased the
                 demand for new polymer technology which already
                 encompass; adhesives, fibres, paints, plastics,
                 rubbers, drugs, solvents, pesticides, refrigerants etc.
                 The conventional design procedure is to hypothesise a
                 candidate polymer, using ‘expert’ knowledge. Next, the
                 polymer is synthesised and the material tested for
                 desired bulk properties and redesigned if it is not
                 acceptable. Since this iterative process may involve a
                 large set of candidate molecules, it may take
                 considerable periods of time and consequently large
                 capital expenditure. An alternative approach that
                 significantly reduces the time and money required to
                 design new polymers is highly desirable. Here, Computer
                 Aided Molecular Design (CAMD) using Genetic Programming
                 (GP) may provide an viable option, as the search
                 operation is automated, thus greatly increasing
                 efficiency.",
  notes =        "MSword postscript not camptible with unix",
  size =         "4 pages",
}

@InProceedings{porter:1998:csaGPdm,
  author =       "Mark A. Porter and Mark J. Willis and Gary A.
                 Montague",
  title =        "A Comparison of Symbolic Annealing and Genetic
                 Programming for Data-based Modelling",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "302--307",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{porter:1999:MAPSSHSA,
  author =       "Mark A. Porter and Mark J. Willis and Gary A.
                 Montague",
  title =        "Modelling Antibiotic Production using Standard and
                 Sequential Hybridised Symbolic Annealing",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1796",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, poster papers, simulated annealing,
                 SYMBA, fermentation",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{powers:1994:trade,
  author =       "Rob Powers",
  title =        "A Study on the Emergence of Trade in Artificial
                 Organisms",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "146--155",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-182105-2",
  notes =        "{"}The development and use of currancy has also been
                 ovserved in many cases{"}

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InProceedings{prado:2002:gecco:lbp,
  title =        "A Step-by-Step Description of a Multi-Purpose
                 Evolutionary Algorithm for Phylogenetic Tree
                 Reconstruction",
  author =       "Oclair G. Prado and Fernando J. {Von Zuben}",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "377--383",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

@InProceedings{pratihar:1999:DGSPOPGSSR,
  author =       "Dilip Kumar Pratihar and Kalyanmoy Deb and Amitabha
                 Ghosh",
  title =        "Design of a Genetic-Fuzzy System for Planning Optimal
                 Path and Gait Simultaneously of a Six-legged Robot",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1678--1684",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{pringle:1995:esp,
  author =       "William R. Pringle",
  title =        "{ESP}: Evolutionary Structured Programming",
  editor =       "David Russell",
  institution =  "Penn State University",
  year =         "1995",
  address =      "Great Valley Campus, PA, USA",
  keywords =     "genetic algorithms, genetic programming, structured
                 programming",
  URL =          "http://www.gv.psu.edu/personal/wrp103/wpr/esp.ps",
  notes =        "Artificial Intelligence Research/CMPEN 597A, Summer
                 1995",
  size =         "8 pages",
}

@TechReport{pryor:1998:drbGPm,
  author =       "R. J. Pryor",
  title =        "Developing Robotic Behavior Using a Genetic
                 Programming Model",
  institution =  "Sandia National Laboratories",
  year =         "1998",
  type =         "SANDIA report",
  number =       "SAND98-0074",
  address =      "P.O. Box 5800, Albuquerque, NM 87185-1109, USA",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://www.cs.sandia.gov/pub/rjpryor/Robug_Paper.pdf",
  abstract =     "This report describes the methodology for using a
                 genetic programming model to develop tracking behaviors
                 for autonomous, microscale robotic vehicles. The use of
                 such vehicles for surveillance and detection operations
                 has become increasingly important in defense and
                 humanitarian applications. Through an evolutionary
                 process similar to that found in nature, the genetic
                 programming model generates a computer program that
                 when downloaded onto a robotic vehicleÕs on-board
                 computer will guide the robot to successfully
                 accomplish its task. Simulations of multiple robots
                 engaged in problem- solving tasks have demonstrated
                 cooperative behaviors. This report also discusses the
                 behavior model produced by genetic programming and
                 presents some results achieved during the study",
  size =         "25 pages",
}

@TechReport{Pujol:1997:etwNNGP,
  author =       "Joao Carlos Figueira Pujol and Riccardo Poli",
  title =        "Evolution of the Topology and the Weights of Neural
                 Networks using Genetic Programming with a Dual
                 Representation",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-97-7",
  month =        feb,
  year =         "1997",
  email =        "R.Poli@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1997/CSRP-97-07.ps.gz",
  abstract =     "Genetic programming is a methodology for program
                 development, consisting of a special form of genetic
                 algorithm capable of handling parse trees representing
                 programs, that has been successfully applied to a
                 variety of problems. In this paper a new approach to
                 the construction of neural networks based on genetic
                 programming is presented. A linear chromosome is
                 combined to a graph representation of the network and
                 new operators are introduced, which allow the evolution
                 of the architecture and the weights simultaneously
                 without the need of local weight optimization. This
                 paper describes the approach, the operators and reports
                 results of the application of the model to several
                 binary classification problems.",
  notes =        "See also Pujol:1998:etwNNGP",
}

@InProceedings{pujol:1998:eearNN,
  author =       "Joao Carlos Figueira Pujol and Riccardo Poli",
  title =        "Efficient Evolution of Asymetric Recurrent Neural
                 Networks Using a {PDGP}-inspired Two-dimensional
                 Representation",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "130--141",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  URL =          "http://www.urano.cdtn.br/~pujol/euro986.ps",
  abstract =     "Recurrent neural networks are particularly useful for
                 processing time sequences and simulating dynamical
                 systems. However, methods for building recurrent
                 architectures have been hindered by the fact that
                 available tra ining algorithms are considerably more
                 complex than those for feedforward netwo rks. In this
                 paper, we present a new method to build recurrent
                 neural networks based on evolutionary computation,
                 which combines a linear chromosome wit h a
                 two-dimensional representation inspired by Parallel
                 Distributed Genetic Programming (a form of genetic
                 programming for the evolution of graph-lik e programs)
                 to evolve the architecture and the weights
                 simultaneously. Our method can evolve general
                 asymmetric recurrent architectures as well as
                 specialized recurrent architectures. This paper
                 describes the method and reports on results of its
                 application.",
  notes =        "EuroGP'98. Artificial Ant John Muir Trail",
}

@InProceedings{pujol:1998:dnraenc,
  author =       "Joao Carlos Figueira Pujol and Riccardo Poli",
  title =        "Dual Network Representation Applied to the Evolution
                 of Neural Controllers",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "637--646",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  ISBN =         "3-540-64891-7",
  URL =          "http://www.urano.cdtn.br/~pujol/ep9813.ps",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7",
}

@Article{Pujol:1998:etwNNGP,
  author =       "Joao Carlos Figueira Pujol and Riccardo Poli",
  title =        "Evolution of the Topology and the Weights of Neural
                 Networks using Genetic Programming with a Dual
                 Representation",
  journal =      "Applied Intelligence",
  year =         "1998",
  volume =       "8",
  pages =        "73--84",
  email =        "J.Pujol@cs.bham.ac.uk R.Poli@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, neural networks, PDGP",
  URL =          "http://www.urano.cdtn.br/~pujol/ai98.ps",
  size =         "12 pages",
  notes =        "see also Pujol:1997:etwNNGP XOR, 3,4,5 odd parity, T
                 an C character (TC) recognition problem",
}

@InProceedings{pujol:1998:enndr,
  author =       "Joao Carlos Figueira Pujol and Riccardo Poli",
  title =        "Evolving Neural Networks Using a Dual Representation
                 with a Combined Crossover Operator",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "416--421",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  URL =          "http://www.urano.cdtn.br/~pujol/ice989.ps",
  file =         "c072.pdf",
  size =         "6 pages",
  abstract =     "In this paper a new approach to the evolution of
                 neural networks is presented. A linear chromosome
                 combined with a grid-based representation of the
                 network, and a new crossover operator, allow the
                 evolution of the architecture and the weights
                 simultaneously. In our approach there is no need for a
                 separate weight optimization procedure and networks
                 with more than one type of activation function can be
                 evolved. A pruning strategy is also introduced, which
                 leads to the generation of solutions with varying
                 degrees of complexity. Results of the application of
                 the method to several binary classification problems
                 are reported.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@Article{pujol:1998:eurogp98,
  author =       "Joao C. F. Pujol and Riccardo Poli",
  title =        "Euro{GP}'98: Conference Report",
  journal =      "EvoNews",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ls11-www.informatik.uni-dortmund.de/evonet/Coordinator/html/eurogp__98.html",
  size =         "1 page",
  notes =        "report on banzhaf:1998:GP",
}

@InProceedings{pujol:1999:ENNUWM,
  author =       "Joao Carlos Figueira Pujol and Riccardo Poli",
  title =        "Evolution of Neural Networks Using Weight Mapping",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1170--1177",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  URL =          "http://www.urano.cdtn.br/~pujol/gecco-430.ps",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@PhdThesis{pujol:thesis,
  author =       "Joao Carlos Figueira Pujol",
  title =        "Evolution of Artificial Neural Networks Using a
                 Two-dimensional Representation",
  school =       "School of Computer Science, University of Birmingham",
  year =         "1999",
  address =      "UK",
  month =        apr,
  email =        "pujol@urano.cdtn.br",
  keywords =     "genetic algorithms, genetic programming, PDGP",
  URL =          "http://www.urano.cdtn.br/~pujol/tese19.ps",
  size =         "178 pages",
  abstract =     "The design of artificial neural networks is still
                 largely performed by an expert, with only a few
                 heuristics to guide a trial-and-error search. Recently,
                 new methods based on evolutionary computation (EC) have
                 been applied to the synthesis of artificial neural
                 networks with modest results. The basic limitation of
                 EC-based methods is that they do not take into account
                 the fact that artificial neural networks are
                 two-dimensional structures, and do not use specialized
                 evolutionary operators. In this work, a new method
                 based on a special form of evolutionary computation
                 called genetic algorithms is proposed for the evolution
                 of artificial neural networks. The method is a general
                 purpose procedure able to evolve feedforward and
                 recurrent architectures. It is based on a
                 two-dimensional representation, and includes operators
                 to evolve the architecture and the connection weights
                 simultaneously. The new approach has shown promising
                 results, and has fared better than previous methods in
                 a number of applications, including: binary
                 classification problems, design of neural controllers
                 and a complex navigation task of traversing a trail. An
                 extension of the two-dimensional representation is also
                 presented, which can be combined with other methods,
                 providing them with an alternative procedure to evolve
                 the weights of the connections.",
}

@InCollection{pujol:1999:eNN2da,
  author =       "J. C. F. Pujol and R. Poli",
  title =        "Evolution of Neural Networks using a Two-Dimensional
                 Aproach",
  booktitle =    "Evolution of Engineering and Information Systems and
                 Their Applications",
  publisher =    "CRC Press",
  year =         "1999",
  editor =       "Lakhmi C. Jain",
  series =       "CSC Press international series on computational
                 intelligence",
  address =      "Boca Raton, Florida, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-8493-1965-X",
  notes =        "http://www.crcpress.com:80/us/product2.asp?dept_id=1&sku=1965&mscssid=",
}

@InCollection{punch:1996:aigp2,
  author =       "William F. Punch and Douglas Zongker and Erik D.
                 Goodman",
  title =        "The Royal Tree Problem, a Benchmark for Single and
                 Multiple Population Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "299--316",
  chapter =      "15",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "We have previously shown how a genetic algorithm (GA)
                 can be used to perform {"}data mining,{"} the discovery
                 of particular/important data within large datasets, by
                 finding optimal data classifications using known
                 examples. However, these approaches, while successful,
                 limited data relationships to those that were
                 {"}fixed{"} before the GA run. We report here on an
                 extension of our previous work, substituting a genetic
                 program (GP) for a GA. The GP could optimize data
                 classification, as did the GA, but could also determine
                 the functional relationships among the features. This
                 gave improved performance and new information on
                 important relation ships among features. We discuss the
                 overall approach, and compare the effectiveness of the
                 GA vs. GP on a biochemistry problem, the determination
                 of the involvement of bound water molecules in protein
                 interactions.",
  notes =        "

                 Also available as GARAGe96-01-01",
  size =         "18 pages",
}

@InProceedings{punch:1998:empGP,
  author =       "William F. Punch",
  title =        "How Effective are Multiple Populations in Genetic
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "308--313",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{punch:1999:GP98review,
  author =       "William Punch",
  title =        "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  journal =      "IEEE Transations on Evolutionary Computation",
  year =         "1999",
  volume =       "3",
  number =       "2",
  pages =        "159--161",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Review of koza:gp98",
}

@InProceedings{Punch:2000:GECCO,
  author =       "W. F. Punch and W. M. Rand",
  title =        "{GP}+Echo+Subsumption = Improved Problem Solving",
  pages =        "411--418",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@Article{punch:2001:GPEM,
  author =       "William Punch",
  title =        "Book Review: Genetic Programming--An Introduction: On
                 the Automatic Evolution of Computer Programs and Its
                 Applications",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "2",
  pages =        "193--195",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISSN =         "1389-2576",
  URL =          "http://ipsapp009.lwwonline.com/content/getfile/4723/5/6/fulltext.pdf",
  notes =        "Favourable review of banzhaf:1997:book",
}

@InProceedings{punya:1999:PSCHMUFR,
  author =       "Sheel Punya and Brahma Deo",
  title =        "Prediction of Silicon Content of Hot Metal Using
                 Fuzzy-{GA} Regression",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1685--1690",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{puppala:1998:smbcc,
  author =       "Narendra Puppala and Sandip Sen and Maria Gordin",
  title =        "Shared memory based Cooperative Coevolution",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "570--574",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Applications
                 of Evolutionary Computation, Representation and
                 Operators, Comparing Algorithms",
  file =         "c098.pdf",
  size =         "5 pages",
  abstract =     "Autonomous agents that possess distinct expertise but
                 lack proper coordination skills can suffer from poor
                 performance in a cooperative setting. The success of
                 agents in multiagent systems is based on their ability
                 to adapt effectively with other agents in completing
                 their tasks. We present here a co-evolutionary approach
                 to generating behavioral strategies for autonomous
                 agents cooperating with each other to achieve a common
                 goal. We co-evolve agent behaviors with genetic
                 algorithms (GAS) where one GA population is evolved per
                 individual in the cooperative group. Groups are formed
                 by pairing strategies from each population and the best
                 pairs are stored in shared memory. Population members
                 are evaluated by pairing them with representatives of
                 other populations in the shared memory. Experimental
                 results obtained by conducting experiments in a room
                 painting domain are presented, showing the success of
                 the shared memory approach in consistently generating
                 optimal behavior patterns. Performance comparisons with
                 a random pairing approach and a single population
                 approach demonstrate the utility of the shared memory
                 approach.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence. Presented
                 at WCCI-98 by Dale A. Schoenefeld. Painter and
                 Whitewasher problem",
}

@Unpublished{putnan:1994:gpm,
  author =       "Jeffrey B. Putnam",
  title =        "Genetic Programming of Music",
  month =        "30 " # aug,
  year =         "1994",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.nmt.edu/~jefu/notes/ep.ps",
  notes =        "

                 ",
  size =         "13 pages",
}

@InProceedings{putnam:1996:EP,
  author =       "Jeffrey B. Putnam",
  title =        "A Grammar-Based Genetic Programming Technique Applied
                 to Music Generation",
  booktitle =    "Evolutionary Programming V: Proceedings of the Fifth
                 Annual Conference on Evolutionary Programming",
  year =         "1996",
  editor =       "Lawrence J. Fogel and Peter J. Angeline and Thomas
                 Baeck",
  pages =        "277--286",
  address =      "San Diego",
  publisher_address = "Cambridge, MA, USA",
  month =        feb # " 29-" # mar # " 3",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-06190-2",
  notes =        "EP-96 http://www.natural-selection.com/eps/EP96.html",
}

@InProceedings{qin:2002:gecco:workshop,
  title =        "Improved Genetic Programming with Genetic and
                 Probabilistic Populations",
  author =       "Ping Qin and Yue Shen and Jun Zhu",
  pages =        "296--299",
  booktitle =    "Graduate Student Workshop",
  editor =       "Sean Luke and Conor Ryan and Una-May O'Reilly",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InCollection{quartetti:1998:EPPGM,
  author =       "Chris Quartetti",
  title =        "Evolving a Program to Play the Game Minesweeper",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "137--146",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@TechReport{qureshi:1996:eaRN,
  author =       "A. Qureshi",
  title =        "Evolving Agents",
  institution =  "UCL",
  year =         "1996",
  type =         "Research Note",
  number =       "RN/96/4",
  address =      "Gower Street, London, WC1E 6BT, UK",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Agent Based
                 Computing, Distributed AI, Automatic Code Generation,
                 Automatic Programming, Machine Learning, Artificial
                 Evolution, Communication, Automatically Defined
                 Functions (ADF)",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/AQ.gp96.ps",
  abstract =     "

                 The paradigm of agent based computing is becoming
                 increasingly popular both in distributed artificial
                 intelligence and as a general software engineering
                 technique. The difficulty with agent based computing is
                 that success depends not on the correctness of any one
                 agent, but on the emergent behaviour arising from the
                 interaction of a society of agents. As a consequence,
                 the problem of programming agents is non trivial and
                 poorly understood.

                 In this paper we show that genetic programming can be
                 used to automatically program agents which communicate
                 and interact to solve problems. The programs evolved
                 simulataneously define when and what to communicate,
                 and how to use the communicated information to solve
                 the given problem.",
  notes =        "Submitted to GP96",
  size =         "10 pages",
}

@InProceedings{qureshi:1996:ea,
  author =       "Adil Qureshi",
  title =        "Evolving Agents",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "369--374",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@PhdThesis{qureshi:thesis,
  author =       "Mohammad Adil Qureshi",
  title =        "The Evolution of Agents",
  school =       "University College, London",
  year =         "2001",
  address =      "UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/maq-thesis-14072k1.ps.gz",
  size =         "139 pages",
  abstract =     "Genetic Programming (GP) is a technique that can be
                 used to automatically program computers to perform some
                 required task. The technique is a kind of genetic
                 algorithm in which the representation is a program tree
                 instead of a bit-string and the fitness of each tree is
                 evaluated by executing the computer program that it
                 represents. The subject of this thesis is to
                 investigate the use of GP to automatically program
                 multiagent systems. To achieve this goal, we consider
                 the general problems in creating multiagent systems,
                 and show how GP can be used to provide solutions to
                 many of them. Our key contributions are as follows:

                 We show that it is possible to evolve multiagent
                 systems using GP that:

                 exhibit coordinated, coherent behaviour

                 communicate explicitly, and in doing so decide what to
                 communicate and how

                 can resolve conflicts

                 can be integrated into an existing society of agents

                 We also consider the technical scalability issues
                 involved in the use of GP, both generally and in
                 particular as a technique for automatically programming
                 agents and propose some solutions to these problems.",
}

@InCollection{rabkin:2002:EGAMSTAPAVPD,
  author =       "Mark Rabkin",
  title =        "Efficient use of Genetic Algorithms for the Minimal
                 Steiner Tree and Arborescence Problems with
                 Applications to {VLSI} Physical Design",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "195--202",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{racine:1998:dlehss,
  author =       "Alain Racine and Marc Schoenauer and Philippe Dague",
  title =        "A dynamic Lattice to Evolve Hierarchically Shared
                 Subroutines: {DL}'{GP}",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "220--232",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "In order to enhance performance of Genetic Programming
                 (GP) search, we have been develop a homogeneous system
                 allowing to construct simultaneously a solution and
                 sub-parts of it within a GP framework. This problem is
                 a crucial point in GP research lately since this is
                 intimately linked with building blocks existence
                 problem. This paper presents an {"}on-going{"} work
                 concerning DLGP (Dynamic Lattice Genetic Programming),
                 a new GP system to evolve shared specific modules using
                 a hierarchical cooperative coevolution paradigm. This
                 scheme attempts to improve efficiency of GP by taking
                 one's inspiration of the organization of natural
                 entities, especially the emergence of complexity.",
  notes =        "EuroGP'98",
}

@InProceedings{racine:1999:pcGPcs,
  author =       "Alain Racine and Sana Ben Hamida and Marc Schoenauer",
  title =        "Parametric Coding vs Genetic Programming: {A} Case
                 Study",
  booktitle =    "Late-Breaking Papers of EuroGP-99",
  year =         "1999",
  editor =       "W. B. Langdon and Riccardo Poli and Peter Nordin and
                 Terry Fogarty",
  pages =        "13--22",
  address =      "Goteborg, Sweeden",
  month =        "26-27 " # may,
  organisation = "EvoGP",
  keywords =     "genetic algorithms, genetic programming,
                 evolutionstrategies",
  abstract =     "The goal is to design the 2-dimensional profile of an
                 optical lens in order to control focalplane irradiance
                 of some laser beam. The numerical simulations of
                 irradiance of the beam through the lens, including some
                 technological constraints on the correlation radius of
                 the phase of the lens, involves two FFT (fast Fourier
                 transforms) computations, whose computational cost
                 heavily depends upon the chosen discretization.

                 A straightforward representation of a solution is that
                 of a matrix of thicknesses, based on a N by N (with N a
                 power of two) discretization of the lens. However, even
                 though some technical simplifications allow us to
                 reduce the size of the search space, its complexity
                 increased quadratically with N, making physically
                 realistic cases (e.g. N >= 256) almost untractable
                 (more than 2000 variables). An alternative
                 representation is brought by GP parse trees, searching
                 in functional space: the genotype does not depend
                 anymore on the chosen discretization.

                 The implementation of both parametric representation
                 (using ES algorithms) and functional approach (using
                 standard GP) for the lens design are described. Both
                 achieve good results compared to the sate-of-the-art
                 methods for small to medium values of the
                 discretization parameter N (up to 256). Moreover,
                 preliminary comparative results are presented between
                 the two representations, and some counter-intuitive
                 results are discussed.",
  notes =        "EuroGP'99LB part of langdon:1999:egplb",
}

@Article{racine:1999:evorobot,
  author =       "Alain Racine and Jerorn Eggermont",
  title =        "EvoRobot Workshop report",
  journal =      "EvoNEWS",
  year =         "1999",
  volume =       "11",
  pages =        "13",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dcs.napier.ac.uk/evonet/Coordinator/evonews/evonews11.pdf
                 ?",
  size =         "0.3 page",
}

@InProceedings{radi:1998:GPcdfglrNN,
  author =       "Amr Radi and Riccardo Poli",
  title =        "Genetic Programming Can Discover Fast and General
                 Learning Rules for Neural Networks",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "314--323",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{radi:1999:GPdelrholffNN,
  author =       "Amr Radi and Riccardo Poli",
  title =        "Genetic Programming Discovers Efficient Learning Rules
                 for the Hidden and Output Layers of Feedforward Neural
                 Networks",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "120--134",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@InProceedings{radi:1999:EDLRFNNSAF,
  author =       "Amr Radi and Riccardo Poli",
  title =        "Evolutionary Discovery of Learning Rules for
                 Feedforward Neural Networks with Step Activation
                 Function",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1178--1183",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Unpublished{raich:1997:agrGAr,
  author =       "Anne M. Raich and Jamshid Ghaboussi",
  title =        "Autogenesis and redundancy in {GA} representation",
  note =         "Position paper at the Workshop on Exploring Non-coding
                 Segments and Genetics-based Encodings at ICGA-97",
  month =        "21 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, introns",
  URL =          "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/raich.ps",
  notes =        "http://www.aic.nrl.navy.mil/~aswu/icga97.ws/",
  size =         "3 pages",
}

@InProceedings{raich:1999:FDSUIRGA,
  author =       "Anne M. Raich and Jamshid Ghaboussi",
  title =        "Frame Design Synthesis Using Implicit Redundant
                 Genetic Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1691--1698",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{raidl:1998:hGPnrsr,
  author =       "Gunther R. Raidl",
  title =        "A Hybrid {GP} Approach for Numerically Robust Symbolic
                 Regression",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "323--328",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{raidl:1999:AEAPLP,
  author =       "Gunther R. Raidl",
  title =        "An Evolutionary Approach to Point-Feature Label
                 Placement",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "807",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{raidl:1999:O,
  author =       "Gunther R. Raidl and Jens Gottlieb",
  title =        "On the importance of phenotypic duplicate elimination
                 in decoder-based evolutionary algorithms",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "204--211",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "PBIL, TSP",
  notes =        "GECCO-99LB",
}

@InProceedings{RaikDurnota94,
  author =       "Simon Raik and Bohdan Durnota",
  title =        "The Evolution of Sporting Strategies",
  editor =       "Russel J. Stonier and Xing Huo Yu",
  pages =        "85--92",
  booktitle =    "Complex Systems: Mechanisms of Adaption",
  year =         "1994",
  publisher_address = "Amsterdam, Netherlands",
  publisher =    "IOS Press",
  email =        "simonr@sd.monash.edu.au,
                 Bohdan.Durnota@sd.monash.edu.au",
  keywords =     "genetic algorithms, genetic programming, collective
                 behaviour",
  ISBN =         "90-5199-186-X",
  URL =          "http://www.sd.monash.edu.au/~simonr/publications/gpSport.ps",
  size =         "415961 Kb, 8 pages",
  abstract =     "This paper describes Coach, an evolutionary simulator
                 developed to investigate strategies for teams in a
                 competitive environment, and discusses some of the
                 issues surrounding the evolution of cooperation among
                 team members. As its title suggests, Coach is a
                 simulator of sporting environments, as these possess
                 the required motivations for such behaviour and have
                 the potential to benefit the real sporting world. In
                 addition to describing Coach, this paper describes two
                 applications illustrating the potential and value of
                 the system. It examines cooperation within a simplified
                 model of the game of volleyball in which cooperation
                 emerged in the form of players passing the ball between
                 them in order to control it for an effective offensive
                 hit. Each player learnt a specialized skill which, in
                 close harmony with the other team member's skills,
                 provided the team with a strategy not unlike those used
                 by real volleyball players. Some preliminary
                 experiments were also carried out to investigate
                 communication among team members.",
  notes =        "Presents a model for cooperation between members of
                 teams.

                 ",
}

@InProceedings{raik:1996:ie,
  author =       "Simon E. Raik and David G. Browne",
  title =        "Implicit versus Explicit: {A} Comparison of State in
                 Genetic Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "151--159",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sd.monash.edu.au/~simonr/publications/gp96.ps",
  abstract =     "This paper examins memory and state in autonomous
                 vehicle controllers...",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{Raik:1997:ppe,
  author =       "Simon Raik",
  title =        "Parallel Program Execution",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "297",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@InProceedings{rana:1999:TDBCO,
  author =       "Soraya Rana",
  title =        "The Distributional Biases of Crossover Operators",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "549--556",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{rao:1996:erp2,
  author =       "Sathyanarayan S. Rao and Kumar Chellapilla",
  title =        "Evolving Reduced Parameter Bilinear Models for Time
                 Series Prediction using Fast Evolutionary Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Evolutionary Programming",
  pages =        "528--535",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 EP paper",
}

@InProceedings{rasheed:1998:apacGAo,
  author =       "Khaled Rasheed",
  title =        "An Adaptive Penalty Approach for Constrained
                 Genetic-Algorithm Optimization",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "584--590",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{rasheed:1998:iGAcgx,
  author =       "Khaled Rasheed",
  title =        "Improving {GA} Convergence Using Guided Crossover",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "591",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InCollection{rasmussen:2002:SLAOEEEMGS,
  author =       "Craig E. Rasmussen",
  title =        "Sex, Love, and Anger: On the Evolutionary Emergence of
                 Emotionally Motivated Gaming Strategies",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "203--212",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{RatlePPSN2000,
  author =       "Alain Ratle and Michele Sebag",
  title =        "Genetic Programming and Domain Knowledge: Beyond the
                 Limitations of Grammar-Guided Machine Discovery",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VI 6th
                 International Conference",
  editor =       "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
                 Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
                 Merelo and Hans-Paul Schwefel",
  year =         "2000",
  publisher =    "Springer Verlag",
  address =      "Paris, France",
  month =        "16-20 " # sep,
  pages =        "211--220",
  note =         "LNCS 1917",
  keywords =     "genetic algorithms, genetic programming, context-free
                 grammars",
  URL =          "http://www.eeaax.polytechnique.fr/papers.html#PPSN_RS:2000",
  abstract =     "Application of Genetic Programming to the discovery of
                 empirical laws is often impaired by the huge size of
                 the domains involved. In physical applications,
                 dimensional analysis is a powerful way to trim out the
                 size of these spaces This paper presents a way of
                 enforcing dimensional constraints through formal
                 grammars in the GP framework. As one major limitation
                 for grammar-guided GP comes from the initialization
                 procedure (how to find admissible and sufficiently
                 diverse trees with a limited depth), an initialization
                 procedure based on dynamic grammar pruning is proposed.
                 The approach is validated on the problem of
                 identification of a materials response to a mechanical
                 test.",
}

@Article{Ratle:2001:ASC,
  author =       "Alain Ratle and Michele Sebag",
  title =        "Grammar-guided genetic programming and dimensional
                 consistency: application to non-parametric
                 identification in mechanics",
  journal =      "Applied Soft Computing",
  volume =       "1",
  pages =        "105--118",
  year =         "2001",
  number =       "1",
  keywords =     "genetic algorithms, genetic programming, context-free
                 grammars",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-43S6W98-B/1/38e0fa6ac503a5ef310e2287be01eff8",
  abstract =     "Although genetic programming has often successfully
                 been applied to non-parametric modeling, it is
                 frequently impaired by the huge size of the search
                 space explored. Domain knowledge is a powerful way to
                 trim out the size of the space, by restricting the
                 search to a priori relevant models. A most natural
                 domain knowledge in scientific modeling is known as
                 dimensional analysis, stipulating that the models must
                 be consistent with regards to the variable measurement
                 units.In this paper, it is shown that dimensional
                 analysis can automatically be expressed as a context
                 free grammar. Dimensionally-aware GP is thus achieved
                 by employing the dimensional grammar within the
                 grammar-guided GP framework first investigated by Gruau
                 [On using syntactic constraints with genetic
                 programming, in: P. Angeline, K.E. Kinnear Jr. (Eds.),
                 Advances in Genetic Programming II, MIT Press,
                 Cambridge, MA, 1996, pp. 377-394.]. However,
                 grammar-guided genetic programming encounters severe
                 difficulties when it involves a complex grammar, which
                 might explain why this approach has not been widely
                 used so far. The drawback is blamed on the
                 initialization step, which hardly constructs a
                 sufficiently diversified initial population, thus
                 hindering the success of evolution. This limitation is
                 addressed by a new CFG compliant initialization
                 procedure.The approach is validated on two problems
                 related to the identification of mechanical properties
                 of materials.",
}

@InProceedings{Ratle:2001:EA,
  author =       "Alain Ratle and Michele Sebag",
  title =        "Avoiding the bloat with Probabilistic Grammar-guided
                 Genetic Programming",
  booktitle =    "Artificial Evolution 5th International Conference,
                 Evolution Artificielle, EA 2001",
  year =         "2001",
  editor =       "P. Collet and C. Fonlupt and J.-K. Hao and E. Lutton
                 and M. Schoenauer",
  volume =       "2310",
  series =       "LNCS",
  pages =        "255--266",
  address =      "Creusot, France",
  month =        oct # " 29-31",
  publisher =    "Springer Verlag",
  ISBN =         "3-540-43544-1",
  URL =          "http://link.springer.de/link/service/series/0558/papers/2310/23100255.pdf",
  keywords =     "genetic algorithms, genetic programming, context-free
                 grammars",
  abstract =     "The application of Genetic Programming to the
                 discovery of empirical laws is often impaired by the
                 huge size of the search space, and consequently by the
                 computer resources needed. In many cases, the extreme
                 demand for memory and CPU is due to the massive growth
                 of non-coding segments, the introns. The paper presents
                 a new program evolution framework which combines
                 distribution-based evolution in the PBIL spirit, with
                 grammar-based genetic programming; the information is
                 stored as a probability distribution on the grammar
                 rules, rather than in a population. Experiments on a
                 real-world like problem show that this approach gives a
                 practical solution to the problem of introns growth.",
  notes =        "EA'01",
}

@InProceedings{ratle:2002:gecco:workshop,
  title =        "Stochastic Grammatical Programming to Avoid the
                 Bloat",
  author =       "Alain Ratle and Michle Sebag",
  pages =        "165--169",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, PBIL",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{Rauss:2000:GECCO,
  author =       "Patrick J. Rauss and Jason M. Daida and Shahbaz
                 Chaudhary",
  title =        "Classification of Spectral Imagery Using Genetic
                 Programming",
  pages =        "726--733",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{icga91:ray,
  author =       "Thomas S. Ray",
  title =        "Is it Alive or is it {GA}",
  booktitle =    "Proceedings of the Fourth International Conference on
                 Genetic Algorithms",
  year =         "1991",
  editor =       "Richard K. Belew and Lashon B. Booker",
  pages =        "527--534",
  address =      "University of California - San Diego, La Jolla, CA,
                 USA",
  publisher_address = "San Mateo, CA, USA",
  month =        "13-16 " # jul,
  organisation = "International Society for Genetic Algorithms",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  size =         "8 pages",
  abstract =     "Evolution of programs, written in red-code? Does not
                 use a GA, but loads of mutation. Doesnt give details of
                 Tierra -- try Alife proceedings",
  notes =        "ICGA-91 conference info
                 http://www.aic.nrl.navy.mil/galist/digests/v5n1",
}

@InProceedings{ray:1997:snd,
  author =       "Thomas S. Ray",
  title =        "Selecting Naturally for Differentiation",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Artifical life and evolutionary robotics",
  pages =        "414",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{raymer:1996:GPidm:bpi,
  author =       "M. L. Raymer and W. F. Punch and E. D. Goodman and L.
                 A. Kuhn",
  title =        "Genetic Programming for Improved Data Mining: An
                 Application to the Biochemistry of Protein
                 Interactions",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "375--380",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://isl.cps.msu.edu/GA/papers/GARAGe96-04-01.ps",
  size =         "6 pages",
  notes =        "GP-96 Also available as TR GARAGe96-04-01",
}

@InProceedings{raynal:1999:MNIAUGP,
  author =       "Frédéric Raynal and Evelyne Lutton and Pierre Collet
                 and Marc Schoenauer",
  title =        "Manipulation of Non-Linear {IFS} Attractors Using
                 Genetic Programming",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1171--1177",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, scheduling",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://www-rocq.inria.fr/fractales/Publications/PapierCEC99-Final.ps.gz",
  abstract =     "Non-linear Iterated Function Systems (IFSs) are very
                 powerful mathematical objects related to fractal
                 theory, that can be used in order to generate (or
                 model) very irregular shapes. We investigate in this
                 paper how Genetic Programming techniques can be
                 efficiently exploited in order to generate randomly or
                 interactively artistic {"}fractal{"} 2D shapes. Two
                 applications are presented for different types of
                 non-linear IFSs: interactive generation of Mixed IFSs
                 attractors using a classical GP scheme, random
                 generation of Polar IFSs attractors based on an
                 {"}individual{"} approach of GP.",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{rechsteiner:1999:AGNMMEEAG,
  author =       "Andreas Rechsteiner and Mark A. Bedau",
  title =        "A Generic Neutral Model for Measuring Excess
                 Evolutionary Activity of Genotypes",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1366--1373",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{reiser:1998:toolbox,
  author =       "Philip G. K. Reiser",
  title =        "Evolutionary Computation and the Tinkerer's Evolving
                 Toolbox",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "209--219",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "In nature, variation mechanisms have evolved that
                 permit increasingly rapid and complex adaptations to
                 the environment. Similarly, it may be observed that
                 evolutionary learning systems are adopting increasingly
                 sophisticated variation mechanisms. In this paper, we
                 draw parallels between the adaptation mechanisms in
                 nature and those in evolutionary learning systems.
                 Extrapolating this trend, we indicate an interesting
                 new direction for future work on evolutionary learning
                 systems.",
  notes =        "EuroGP'98",
}

@InProceedings{reiser:1998:elpccegp,
  author =       "Philip G. K. Reiser and Patricia J. Riddle",
  title =        "Evolving Logic Programs to Classify Chess-Endgame
                 Positions",
  booktitle =    "Second Asia-Pacific Conference on Simulated Evolution
                 and Learning",
  year =         "1998",
  editor =       "Charles Newton",
  address =      "Australian Defence Force Academy, Canberra,
                 Australia",
  month =        "24-27 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "SEAL'98 Possible publication in springer-verlag LNAI
                 series SEAL98#026 Session 7: Genetic Programming Chair:
                 Sung-Bae Cho, Yonsei Univ., Korea",
}

@InProceedings{ren:1999:RMPTMUMCP,
  author =       "Fengrong Ren and Hiroshi Tanaka and Toshitsugu Okayama
                 and Takashi Gojobori",
  title =        "Reconstructing Molecular Phylogenetic Tree with
                 Multifurcation by Using Minimum Complexity Principle",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1825--1828",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "dna and molecular computing",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{alife3:reynolds,
  author =       "Craig W. Reynolds",
  title =        "An Evolved, Vision-Based Behavioral Model of Obstacle
                 Avoidance Behaviour",
  booktitle =    "Artificial Life III",
  publisher =    "Addison-Wesley",
  year =         "1994",
  editor =       "Christopher G. Langton",
  volume =       "XVII",
  series =       "SFI Studies in the Sciences of Complexity",
  address =      "Santa Fe Institute, New Mexico, USA",
  month =        "15-19 " # jun # " 1992",
  pages =        "327--346",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Held June 1992 in Santa Fe, New Mexico, USA. Updates
                 sab92:reynolds",
  size =         "21 pages",
}

@InCollection{kinnear:reynolds,
  author =       "Craig W. Reynolds",
  title =        "Evolution of Obstacle Avoidance Behaviour:Using Noise
                 to Promote Robust Solutions",
  institution =  "Electronic Arts",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "221--241",
  chapter =      "10",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "See also SAB92:reynolds, Alife3:reynolds but only a
                 single vehicle rather than herd, no preditor, task is
                 to move along bendy corridor using noisy sensors.
                 {"}When the variance (in the fitness) due to noise is
                 comparable to the variance due to genotype, the
                 progress of evolution is markedly slow{"} ?

                 Without noise evolved controllers brittle. THESE
                 EXPERIMENTS HAVE NOT YET PRODUCED A ROBUST CONTROLLER",
  size =         "21 pages",
}

@InProceedings{sab92:reynolds,
  author =       "Craig W. Reynolds",
  title =        "An Evolved, Vision-Based Behavioral Model of
                 Coordinated Group Motion",
  booktitle =    "From Animals to Animats (Proceedings of Simulation of
                 Adaptive Behaviour)",
  year =         "1992",
  editor =       "Meyer and Wilson",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "9 pages",
  abstract =     "GP used to evolve a controller for critters. Run as
                 small (~20) herds being prayed upon. Fitness = mean
                 life of herd. Population of GPs 30-50. Steady state.
                 References.",
}

@InProceedings{Reynolds:1994:eye,
  author =       "Craig W. Reynolds",
  title =        "The difficulty of roving eyes",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  pages =        "262--267",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  size =         "6 pages",
  notes =        "The difficulty for GP to produce a corridor following
                 robot controller is found to depend dramatically on how
                 the sensor primitive ``look-for-obstacle'' is used by
                 GP. With no constraints very difficult. Readily solved
                 if syntax rules are imposed which force its argument to
                 be a constant.",
}

@InProceedings{Reynolds:1994:tag,
  author =       "Craig W. Reynolds",
  title =        "Competition, Coevolution and the Game of Tag",
  booktitle =    "Proceedings of the Fourth International Workshop on
                 the Synthesis and Simulation of Living Systems",
  year =         "1994",
  editor =       "Rodney A. Brooks and Pattie Maes",
  pages =        "59--69",
  address =      "MIT, Cambridge, MA, USA",
  month =        "6-8 " # jul,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/cwrALifeIV.ps.Z",
  size =         "11 pages",
  abstract =     "Tag is a children's game based on symmetrical pursuit
                 and evasion. In these experiments, control programs for
                 mobile agents (simulated vehicles) are created through
                 artificial evolution, based on their skill at the game
                 of tag. Each controller is composed of distinct
                 behavior components for pursuit and evasion. A player's
                 fitness is based entirely on how well it performs in
                 competition with several other players chosen randomly
                 from the coevolving population of players. This
                 approach avoids the need for an expert player as a
                 fitness reference. In the beginning, the quality of
                 play is very low. This provides a fertile field for
                 slightly better strategies to exploit the weaknesses of
                 others. Through evolution, guided by competitive
                 fitness, increasingly better strategies emerge over
                 time.",
  notes =        "alife-4 Gives introduction to existing work on using
                 co-evolution in GPs and GAs. Uses Steady state GP with
                 tournament (7) selection. However 50% of deletions are
                 at random and 50% by inverse tournament. Some runs use
                 mutation. Some times uses Kinnear's Hoist crossover.

                 ",
}

@InProceedings{Reynolds:1994:sab,
  author =       "Craig W. Reynolds",
  title =        "Evolution of Corridor Following Behavior in a Noisy
                 World",
  booktitle =    "Simulation of Adaptive Behaviour (SAB-94)",
  year =         "1994",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/cwrSAB94.ps.Z",
  abstract =     "Robust behavioral control programs for a simulated 2d
                 vehicle can be constructed by artificial evolution.
                 Corridor following serves here as an example of a
                 behavior to be obtained through evolution. A
                 controller's fitness is judged by its ability to steer
                 its vehicle along a collision free path through a
                 simple corridor environment. The controller's inputs
                 are noisy range sensors and its output is a noisy
                 steering mechanism. Evolution determines the quantity
                 and placement of sensors. Noise in fitness tests
                 discourages brittle strategies and leads to the
                 evolution of robust, noise-tolerant controllers.
                 Genetic Programming is used to model evolution, the
                 controllers are represented as deterministic computer
                 programs.",
  notes =        "

                 ",
}

@InCollection{rhee:2000:ESMGGP,
  author =       "Stephen J. Rhee",
  title =        "Evolving Strategies for the Minesweeper Game using
                 Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "312--318",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InCollection{rhodes:1994:resistance,
  author =       "Bradley Rhodes",
  title =        "The Evolution of Resistance to Crossover and Mutation
                 in {GP}",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "156--162",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-182105-2",
  notes =        "GP evolves a large number of replaceable subtrees, ie
                 code who's results are completly ignored. 5-parity
                 problem test bed. 400 generations. Population of 2000.
                 Given XOR. ~6% replaceable code early (before gen 100,
                 rises to 50+% after gen 200.

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@Article{RichWaters88,
  author =       "Charles Rich and Richard C. Waters",
  title =        "Automatic Programming: Myths and Prospects",
  journal =      "Computer",
  pages =        "40--51",
  volume =       "21",
  number =       "8",
  month =        aug,
  year =         "1988",
  keywords =     "automatic programming, technological forecasting,
                 Programmer's Appentice",
  ISSN =         "0018-9162",
  URL =          "http://ieeexplore.ieee.org/iel1/2/8/00000075.pdf?isNumber=8",
  abstract =     "The authors consider five common myths about automatic
                 programming and expose the fallacies on which they
                 rest. They attempt to provide an accurate picture of
                 these systems in terms of what the user sees, how the
                 system works, and what the system knows. They describe
                 commercially available systems and discuss what is on
                 the horizon.",
  notes =        "Domain and programming cliches",
}

@Article{rich:overview,
  author =       "Charles Rich and Richard C. Waters",
  title =        "The programmer's apprentice",
  journal =      "Computer",
  year =         "1988",
  volume =       "21",
  number =       "11",
  pages =        "10--25",
  month =        nov,
  keywords =     "programming environments, knowledge based system,
                 Programmer's Apprentice, programming, knowledge based
                 systems, programming environments",
  ISSN =         "0018-9162",
  URL =          "http://ieeexplore.ieee.org/iel1/2/2826/00086782.pdf?isNumber=2826",
  abstract =     "The long-term goal of the Programmer's Apprentice
                 project is to develop a theory of how expert
                 programmers analyze, synthesize, modify, explain,
                 specify, verify, and document programs. The authors
                 present their vision of the Programmer's Apprentice,
                 the principles and techniques underlying it, and their
                 progress toward it. The primary vehicle for this
                 exposition is three scenarios illustrating the use of
                 the Apprentice in three phases of the programming task:
                 implementation, design, and requirements. The first
                 scenario is taken from a completed working prototype.
                 The second and third scenarios are the targets for
                 prototype systems currently under construction.",
}

@InProceedings{richards:1996:3dsoLCS,
  author =       "Robert A. Richards and Sheri D. Sheppard",
  title =        "Three-Dimensional Shape Optimization Utilizing a
                 Learning Classifier System",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Classifier Systems, Genetic Algorithms",
  pages =        "539--546",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 Classifier paper",
}

@InProceedings{richards:1998:csmgd,
  author =       "Robert A. Richards",
  title =        "Classifier System Metrics: Graphical Depictions",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "652--657",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{richter:1998:etssnGPab,
  author =       "Charles W. {Richter Jr.} and Daniel Ashlock and Gerald
                 Shebl",
  title =        "Effects of Tree Size and State Number on {GP}-Automata
                 Bidding Strategies",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "329--337",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{rintala:1996:NWGA,
  author =       "Tommi Rintala",
  title =        "2nd order equation",
  year =         "1996",
  editor =       "Jarmo T. Alander",
  booktitle =    "Proceedings of the Second Nordic Workshop on Genetic
                 Algorithms and their Applications (2NWGA)",
  series =       "Proceedings of the University of Vaasa, Nro. 13",
  publisher =    "University of Vaasa",
  address =      "Vaasa (Finland)",
  month =        "19.-23.~" # aug,
  organisation = "Finnish Artificial Intelligence Society",
  keywords =     "genetic algorithms, genetic programming, algebra",
  URL =          "http://www.uwasa.fi/cs/publications/2NWGA/node210.html",
  abstract =     "In this work we have tried to use genetic programming
                 to solve the simple second order equation.",
  notes =        "lil-gp does not seem to be robust to find the solution
                 formula of 2nd order equation",
}

@InProceedings{riolo:1995:adcea,
  author =       "Rick L. Riolo and Mark P. Line",
  title =        "Automatic Discovery of Classification and Estimation
                 Algorithms for Earth-Observation Satellite Imagery",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "73--77",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP, {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InProceedings{riquelme:2001:IEA,
  author =       "J. C. Riquelme and R. Giraldez and J. S. Aguilar and
                 R. Ruiz",
  title =        "Separation Surfaces Through Genetic Programming",
  booktitle =    "14th International Conference on Industrial and
                 Engineering Applications of Artificial Intelligence and
                 Expert Systems (IEA/AIE 2001)",
  year =         "2001",
  editor =       "Moonis Ali",
  address =      "Budapest",
  month =        "4-7 " # jun,
  note =         "Forthcomming",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "(Departamento de Lenguajes y Sistemas Informaticos)
                 IEA/AIE-2001 LNAI????",
}

@InProceedings{rizk:1998:ecapr,
  author =       "Mateen M. Rizki and Louis A. Tamburino",
  title =        "Evolutionary Computing Applied To Pattern
                 Recognition",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "777--785",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolutionary programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{robbins:1999:IESALRE,
  author =       "Andrew Robbins",
  title =        "Interaction between the Evolution of Species in an
                 Artificial Limited Resource Environment",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "184--193",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{roberts:1999:evdIRLI,
  author =       "Simon C. Roberts and Daniel Howard",
  title =        "Evolution of Vehicle Detectors for Infrared Linescan
                 Imagery",
  booktitle =    "Evolutionary Image Analysis, Signal Processing and
                 Telecommunications: First European Workshop, EvoIASP'99
                 and EuroEcTel'99",
  year =         "1999",
  editor =       "Riccardo Poli and Hans-Michael Voigt and Stefano
                 Cagnoni and Dave Corne and George D. Smith and Terence
                 C. Fogarty",
  volume =       "1596",
  series =       "LNCS",
  pages =        "110--125",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "28-29 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65837-8",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-65837-8",
  abstract =     "The paper addresses an important and difficult problem
                 of object recognition in poorly constrained
                 environments and with objects having large variability.
                 This research uses genetic programming (GP) to develop
                 automatic object detectors. The task is to detect
                 vehicles in infrared line scan (IRLS) images gathered
                 by low flying aircraft. This is a difficult task due to
                 the diversity of vehicles and the environments in which
                 they can occur, and because images vary with numerous
                 factors including fly-over, temporal and weather
                 characteristics. A novel multi-stage approach is
                 presented which addresses automatic feature detection,
                 automatic object segregation, rotation invariance and
                 generalisation across diverse objects whilst
                 discriminating from a myriad of potential non-objects.
                 The approach does not require imagery to be
                 pre-processed.",
  notes =        "EvoIASP99'99",
}

@InProceedings{Roberts:2000:GECCO,
  author =       "Simon C. Roberts and Daniel Howard",
  title =        "Genetic Programming for Image Analysis: Orientation
                 Detection",
  pages =        "651--657",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{roberts:2001:EuroGP,
  author =       "Simon C. Roberts and Daniel Howard and John R. Koza",
  title =        "Evolving modules in Genetic Programming by subtree
                 encapsulation",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "160--175",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming,
                 Modularisation, Code Reuse, Subtree Encapsulation,
                 Image Processing,",
  ISBN =         "3-540-41899-7",
  size =         "16 pages",
  abstract =     "In tree-based genetic programming (GP), the most
                 frequent subtrees on later generations are likely to
                 constitute useful partial solutions. This paper
                 investigates the effect of encapsulating such subtrees
                 by representing them as atoms in the terminal set, so
                 that the subtree evaluations can be exploited as
                 terminal data. The encapsulation scheme is compared
                 against a second scheme which depends on random subtree
                 selection. Empirical results show that both schemes
                 improve upon standard GP.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{roberts:2001:gecco,
  title =        "Subtree encapsulation versus {ADF}s in {GP} for parity
                 problems",
  author =       "Simon C. Roberts and Daniel Howard and John R. Koza",
  pages =        "186",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 modularisation, subtree encapsulation, ADFs",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{roberts:2001:eodaia,
  author =       "Daniel Howard and Simon C. Roberts and Conor Ryan",
  title =        "Evolution of an Object Detection Ant for Image
                 Analysis",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "168--175",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, ADF",
  notes =        "GECCO-2001LB, Staged GP, population of Ants, move
                 flag, vehicle detection, three slot working memory 10
                 high level terminals eg Skewness, kurtosis, covariance.
                 Branch typing. TUM. {"}one point crossover{"} p172.
                 Truncation mutation.",
}

@InProceedings{roberts:2001:sevgpep,
  author =       "Simon C. Roberts and Daniel Howard and John R. Koza",
  title =        "Subtree Encapsulation Versus {ADFs} in Genetic
                 Programming for the Even-5-Parity Problem",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "359--365",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, automatically
                 define functions",
  notes =        "GECCO-2001LB",
}

@InProceedings{Roberts:2002:EvoWorkshops,
  author =       "Simon C. Roberts and Daniel Howard",
  title =        "Detection of Incidents on Motorways in Low Flow High
                 Speed Conditions by Genetic Programming",
  booktitle =    "Applications of Evolutionary Computing, Proceedings of
                 EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN",
  year =         "2002",
  editor =       "Stefano Cagnoni and Jens Gottlieb and Emma Hart and
                 Martin Middendorf and G{"}unther Raidl",
  volume =       "2279",
  series =       "LNCS",
  pages =        "243--252",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-4 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, applications",
  ISBN =         "3-540-43432-1",
  size =         "10 pages",
  notes =        "EvoWorkshops2002, part of cagnoni:2002:ews",
}

@InCollection{robertson:1999:ECFFLPAPAGHC,
  author =       "Thomas S. Robertson",
  title =        "Evolution of Communication to Facilitate Food Locating
                 and Predator Avoidance in a Population of Autonomous
                 Genetically Homogeneous Creatures",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "194--203",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{robilliard:1999:A,
  author =       "Denis Robilliard and Cyril Fonlupt",
  title =        "An evolutionary computation scheme based on attraction
                 and repulsion",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "212--222",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{Robilliard:2000:oo,
  author =       "Denis Robilliard and Malik Chami and Cyril Fonlupt and
                 Richard Santer",
  title =        "Using Genetic Programming to tackle the ocean color
                 problem",
  booktitle =    "proceeding of Ocean Optics XV",
  year =         "2000",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{robilliard:2001:EA,
  author =       "Denis Robilliard and Cyril Fonlupt",
  title =        "Backwarding : An Overfitting Control for Genetic
                 Programming in a Remote Sensing Application",
  booktitle =    "Artificial Evolution 5th International Conference,
                 Evolution Artificielle, EA 2001",
  year =         "2001",
  editor =       "P. Collet and C. Fonlupt and J.-K. Hao and E. Lutton
                 and M. Schoenauer",
  volume =       "2310",
  series =       "LNCS",
  pages =        "245--254",
  address =      "Creusot, France",
  month =        oct # " 29-31",
  publisher =    "Springer Verlag",
  ISBN =         "3-540-43544-1",
  URL =          "http://link.springer.de/link/service/series/0558/papers/2310/23100245.pdf",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Overfitting the training data is a common problem in
                 supervised machine learning. When dealing with a remote
                 sensing inverse problem, the PAR, over-fitting prevents
                 GP evolved models to be successfully applied to real
                 data. We propose to use a classic method of over
                 fitting control by the way of a validation set. This
                 allows to go backward in the evolution process in order
                 to retrieve previous, not yet over fitted models.
                 Although this {"}backwarding{"} method performs well on
                 academic benchmarks, there is not enough improvement to
                 deal with the PAR. A new backwarding criterion is then
                 derived using real satellite data and the knowledge of
                 plausible physical bounds for the PAR coefficient in
                 the geographical area that is monitored. This leads to
                 satisfactory GP models and drastically improved
                 images.",
  notes =        "EA'01",
}

@MastersThesis{Robinson:2001:GPtieus,
  author =       "Alan Robinson",
  title =        "Genetic Programming: Theory, Implementation, and the
                 Evolution of Unconstrained Solutions",
  school =       "Hampshire College",
  year =         "2001",
  type =         "Division III thesis",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, PushGP, LJGP
                 Linear Java GP, Lawnmower problem, Grazer problem",
  URL =          "http://hampshire.edu/lspector/robinson-div3.pdf",
  size =         "127 pages",
  abstract =     "Part I: Background

                 1 INTRODUCTION

                 1.1 BACKGROUND ? AUTOMATIC PROGRAMMING

                 1.2 THIS PROJECT

                 1.3 SUMMARY OF CHAPTERS 2 GENETIC PROGRAMMING
                 REVIEW

                 Part II: PushGP

                 3 THE PUSH LANGUAGE & PUSHGP

                 4 PUSHGP COMPARED TO GP2 WITH ADFS

                 4.1 CAN A MORE FLEXIBLE SYSTEM PERFORM AS WELL?

                 4.2 THE COMPUTATIONAL EFFORT METRIC

                 4.3 MEASURING MODULARITY

                 4.4 SOLVING SYMBOLIC REGRESSION 4.5 EVEN PARITY AS A GP
                 BENCHMARK 4.6 SOLVING EVEN-FOUR-PARITY USING PUSHGP AND
                 STACK INPUT

                 4.7 EVEN-FOUR-PARITY WITH INPUT FUNCTIONS

                 4.8 EVEN-SIX-PARITY

                 4.9 SOLVING EVEN-N-PARITY

                 4.10 CONCLUSIONS DRAWN FROM THIS CHAPTER

                 5 VARIATIONS IN GENETIC OPERATORS

                 5.1 PERFORMANCE OF BASE PUSHGP OPERATORS

                 5.2 VARIATIONS IN CROSSOVER

                 5.3 VARIATIONS IN MUTATION

                 5.4 EMPIRICAL TESTS WITH NEW OPERATORS

                 5.5 CONCLUSIONS DRAWN FROM THESE RUNS

                 6 NEWGROUND ? EVOLVING FACTORIAL

                 Part III: LJGP

                 7 LINEAR CODED GENETIC PROGRAMMING IN JAVA

                 7.4 DISTRIBUTED PROCESSING

                 8 LJGP USER?S GUIDE

                 8.1 ENCODING A PROBLEM

                 8.2 LJGP PACKAGES AND CLASSES OVERVIEW

                 8.3 VCPU PROGRAMS

                 9 LJGP APPLIED

                 9.1 LAWNMOWER PILOT STUDY

                 9.2 PROBLEM DESCRIPTION

                 9.3 THE GENETIC MAKEUP OF AN INDIVIDUAL

                 9.4 THE MECHANICS OF EVOLUTION

                 9.5 PILOT RUNS OF THE LAWNMOWER PROBLEM

                 9.6 GRAZER PILOT STUDY

                 9.7 CONCLUSION TO LJGP APPLIED

                 Conclusion

                 APPENDIX A. COMPUTATIONAL EFFORT ? LISP CODE

                 APPENDIX B. GENETIC PROGRAMMING SYSTEMS IN
                 JAVA

                 APPENDIX C. LJGP/JAVA-VM BENCHMARKS",
}

@InProceedings{robinson:2002:gecco:lbp,
  title =        "Using Genetic Programming with Multiple Data Types and
                 Automatic Modularization to Evolve Decentralized and
                 Coordinated Navigation in Multi-Agent Systems",
  author =       "Alan Robinson and Lee Spector",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "391--396",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming, pushGP",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 3D opera multi-agent",
}

@TechReport{robinson:1995:ecaGP,
  author =       "Gerald Robinson and Paul McIlroy",
  title =        "Exploring some Commercial Applications of Genetic
                 Programming",
  institution =  "British Telecom, Systems Research Division",
  year =         "1995",
  type =         "Project",
  number =       "4487",
  address =      "Martelsham, Ipswitch, UK",
  month =        "9/3/95",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.labs.bt.com/library/papers/5119857.htm",
  abstract =     "Using data previously prepared for other research
                 projects on machine learning at BT, some trials using
                 genetic programming (GP) techniques for commercial
                 problem solving were conducted. Three very distinct
                 applications were trialled. 1. Locating the eyes in
                 bitmap images of faces. 2. Predicting future trends in
                 the fluctuation of share prices. 3. Identifying
                 {"}problematic{"} modules in software systems from
                 software metrics. The purpose of the trials was to
                 evaluate both how effectively and how expensively (in
                 terms of computing time), such relatively difficult
                 problems might be solved using GP.

                 Share Price Prediction. Software Fault number
                 prediction. Eye location within static face picturs.",
  notes =        "Presented at the AISB Evolutionary Computing Workshop,
                 Sheffield, UK 3--4 April 1995. see
                 robinson:1995:ecaGPsv",
}

@InCollection{robinson:1995:ecaGPsv,
  author =       "Gerald Robinson and Paul McIlroy",
  title =        "Exploring some Commercial Applications of Genetic
                 Programming",
  booktitle =    "Evolutionary Computing",
  publisher =    "Springer-Verlag",
  year =         "1995",
  editor =       "T. C. Fogarty",
  number =       "993",
  series =       "Lecture Notes in Computer Science",
  address =      "Sheffield, UK",
  month =        "3-4 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-60469-3",
  notes =        "The post-workshop proceedings of the 1995 AISB
                 workshop on evolutionary computing. see
                 robinson:1995:ecaGP",
}

@InProceedings{rodriguez:1999:FWPBEDA,
  author =       "Alberto Ochoa Rodriguez",
  title =        "Finding Wavelet Packet Bases with an Estimation
                 Distribution Algorithm",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "802",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{rodriguez:1998:DHEEFSTAWGPA,
  author =       "Andres Rodriguez",
  title =        "Discovery of Hunting, Escaping, Eating and Food Saving
                 Techniques in an Artificial World by means of a Genetic
                 Programming Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "147--156",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{Rodriguez-Vazquez:1997:moGPnsia,
  author =       "Katya Rodriguez-Vazquez and Carlos M. Fonseca and
                 Peter J. Fleming",
  title =        "Multiobjective Genetic Programming: {A} Nonlinear
                 System Identification Application",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "207--212",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{Rodriguez-Vazquez:1997:GPnarmax,
  author =       "K. Rodriguez-Vazquez and P. Fleming",
  title =        "A Genetic Programming/Narmax Approach to Nonlinear
                 System Identification",
  booktitle =    "Second International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1997",
  editor =       "Ali Zalzala",
  address =      "University of Strathclyde, Glasgow, UK",
  publisher_address = "Savoy Place, London WC2R 0BL, UK",
  month =        "1-4 " # sep,
  publisher =    "Institution of Electrical Engineers",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GALESIA'97",
}

@InProceedings{rodriguez-vazquez:1999:GPDCSM,
  author =       "Katya Rodriguez-Vazquez and Peter J. Fleming",
  title =        "Genetic Programming for Dynamic Chaotic Systems
                 Modelling",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "22--28",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 multi-objective optimization",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{rodriguez-vazquez:2000:GPirms,
  author =       "Katya Rodriguez-Vazquez and Peter J. Fleming",
  title =        "Use of Genetic Programming In The Identification Of
                 Rational Model Structures",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "181--192",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "This paper demonstrates how genetic programming can be
                 used for solving problems in the field of non-linear
                 system identification of rational models. By using a
                 two-tree structure rather than introducing the division
                 operator in the function set, this genetic programming
                 approach is able to determine the `true' model
                 structure of the system under investigation. However,
                 unlike use of the polynomial, which is linear in the
                 parameters, use of rational model is non-linear in the
                 parameters and thus noise terms cannot be estimated
                 properly. By means of a second optimisation process
                 (real-coded GA) which has the aim of tunning the
                 coefficients to the `true' values, these parameters are
                 then correctly computed. This approach is based upon
                 the well-known NARMAX model representation, widely used
                 in non-linear system identification.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{rodriguez-vazquez:2001:gptsmaamd,
  author =       "Katya Rodriguez-Vazquez",
  title =        "Genetic Programming in Time Series Modelling: An
                 Application to Meteorological Data",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "261--266",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, SISO NARMAX",
  ISBN =         "0-7803-6658-1",
  URL =          "http://ieeexplore.ieee.org/iel5/7440/20223/00934399.pdf?isNumber=20223",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = Applied to weather
                 forecasting in Mexico.",
}

@InProceedings{rogers:1995:GFA,
  author =       "David Rogers",
  title =        "Development of the Genetic Function Approximation
                 Algorithm",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "589--596",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Algorithms",
  ISBN =         "1-55860-370-0",
  size =         "8 pages",
  notes =        "Commercial drug modelling tool (Molecular Simulation
                 Incorporated) Well is it a GP? Chromosome has variable
                 structure (different terminals may be included).
                 Constants derived by least-squares fit not genetic
                 evolution. Only allows polynomial (quardratic?)
                 models.",
}

@InCollection{rollin:1999:EGPHTPLBRSE,
  author =       "Michael Rollin",
  title =        "Evolution by Genetic Programming of a
                 Heuristically-Drive Tic-Tac-Toe Player which Learns
                 Both Rules and Strategy by Experience",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "204--208",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{Ronge:1996:GPce,
  author =       "Andreas Ronge and Mats G. Nordahl",
  title =        "Genetic Programs and Co-Evolution Developing robust
                 general purpose controllers using local mating in two
                 dimensional populations",
  booktitle =    "Parallel Problem Solving from Nature IV, Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  series =       "LNCS",
  volume =       "1141",
  pages =        "81--90",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  abstract =     "

                 A co-evolutionary approach for developing programs for
                 controlling a very simple {"}robot-like{"} simulated
                 vehicle is presented. The main goal is to find programs
                 that can generalize and solve other similar problems.
                 Good results are achieved by co-evolving the test cases
                 and the simulated vehicles and using locality in both
                 the reproduction and evaluation phases. The fitness of
                 a controller is determined by its performance in
                 competition with its neighbours in the test case
                 population. The fitness of a test case is similarly
                 determined through competition with its neighbours in
                 the controller population. The co-evolved controllers
                 are more robust and general than a simple hand-designed
                 algorithm or controllers evolved using a fixed training
                 set.",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4 256
                 indexed memory cells, single ADF, depth restriction.
                 Mentions {"}guided crossover{"} ?how directed? {"}The
                 introduction of geographic separation tends to improve
                 population diversity{"}",
}

@InProceedings{Rosca:1994:larGP,
  author =       "J. P. Rosca and D. H. Ballard",
  title =        "Learning by adapting representations in genetic
                 programming",
  year =         "1994",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence, Orlando, Florida, USA",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/94.ieee.adaptive_repr.ps.gz",
  abstract =     "Machine learning aims towards the acquisition of
                 knowledge based on either experience from the
                 interaction with the external environment or by
                 analyzing the internal problem-solving traces. Genetic
                 Programming (GP) has been effective in learning via
                 interaction but so far there have not been any
                 significant tests to show that GP can take advantage of
                 its own search traces. This paper demonstrates how an
                 analysis of the evolution trace enables the genetic
                 search to discover useful genetic material and to use
                 it in order to accelerate the search process. The key
                 idea is that of genetic material discovery which
                 enables a restructuring of the search space so that
                 solutions can be much more easily found.",
  notes =        "See Rosca94 for more details",
}

@TechReport{Rosca94,
  author =       "Justinian P. Rosca and Dana H. Ballard",
  title =        "Genetic Programming with Adaptive Representations",
  institution =  "University of Rochester, Computer Science Department",
  address =      "Rochester, NY, USA",
  number =       "TR 489",
  month =        feb,
  year =         "1994",
  keywords =     "genetic algorithms, genetic programming, learning,
                 adaptive representation",
  URL =          "ftp://ftp.cs.rochester.edu/pub/papers/robotics/94.tr489.Genet
                 ic_programming_with_adaptive_representations.ps.Z",
  abstract =     "Machine learning aims towards the acquisition of
                 knowledge based on either experience from the
                 interaction with the external environment or by
                 analyzing the internal problem-solving traces. Both
                 approaches can be implemented in the Genetic
                 Programming (GP) paradigm. \cite{Hillis90} proves in an
                 ingenious way how the first approach can work. There
                 have not been any significant tests to prove that GP
                 can take advantage of its own search traces. This paper
                 presents an approach to automatic discovery of
                 functions in GP based on the ideas of discovery of
                 useful building blocks by analyzing the evolution
                 trace, generalizing of blocks to define new functions
                 and finally adapting of the problem representation
                 on-the-fly. Adaptation of the representation determines
                 a hierarchical organization of the extended function
                 set which enables a restructuring of the search space
                 so that solutions can be found more easily. Complexity
                 measures of solution trees are defined for an adaptive
                 representation framework and empirical results are
                 presented.",
  notes =        "Jan 1995, Our printer barfed on
                 ftp://ftp.cs.rochester.edu /pub/papers/robotics/
                 94.tr489.Genetic_programming_with_adaptive_representations.ps.Z
                 file at page 23 figure 13.

                 Thu, 16 Feb 95 TR489 can be found in the same ftp
                 directory (pub/u/rosca/gp) under the name
                 94.tr489.ps.Z

                 A shorter version can be found in Rosca:1994:larGP",
}

@InProceedings{Rosca:1994:hsoGP,
  author =       "Justinian P. Rosca and Dana H. Ballard",
  title =        "Hierarchical Self-Organization in Genetic
                 Programming",
  booktitle =    "Proceedings of the Eleventh International Conference
                 on Machine Learning",
  year =         "1994",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/94.ml.hierarchical_so_gp.ps.Z",
  size =         "6 pages",
  abstract =     "This paper presents an approach to automatic discovery
                 of functions in Genetic Programming. The approach is
                 based on discovery of useful building blocks by
                 analyzing the evolution trace, generalizing blocks to
                 define new functions, and finally adapting the problem
                 representation on-the-fly. Adapting the representation
                 determines a hierarchical organization of the extended
                 function set which enables a restructuring of the
                 search space so that solutions can be found more
                 easily. Measures of complexity of solution trees are
                 defined for an adaptive representation framework. The
                 minimum description length principle is applied to
                 justify the feasibility of approaches based on a
                 hierarchy of discovered functions and to suggest
                 alternative ways of defining a problem's fitness
                 function. Preliminary empirical results are
                 presented.",
  notes =        "

                 ",
}

@InProceedings{Rosca:1995:GPexpdf,
  author =       "Justinian P. Rosca",
  title =        "Genetic Programming Exploratory Power and the
                 Discovery of Functions",
  booktitle =    "Evolutionary Programming {IV} Proceedings of the
                 Fourth Annual Conference on Evolutionary Programming",
  year =         "1995",
  editor =       "John Robert McDonnell and Robert G. Reynolds and David
                 B. Fogel",
  pages =        "719--736",
  address =      "San Diego, CA, USA",
  month =        "1-3 " # mar,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-13317-2",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/.ep.exploratory.ps.gz",
  size =         "18 pages",
  abstract =     "Hierarchical genetic programming (HGP) approaches rely
                 on the discovery, modification, and use of new
                 functions to accelerate evolution. This paper provides
                 a qualitative explanation of the improved behavior of
                 HGP, based on an analysis of the evolution process from
                 the dual perspective of diversity and causality. From a
                 static point of view, the use of an HGP approach
                 enables the manipulation of a population of higher
                 diversity programs. Higher diversity increases the
                 exploratory ability of the genetic search process, as
                 demonstrated by theoretical and experimental fitness
                 distributions and expanded structural complexity of
                 individuals. From a dynamic point of view, an analysis
                 of the causality of the crossover operator suggests
                 that HGP discovers and exploits useful structures in a
                 bottom-up, hierarchical manner. Diversity and causality
                 are complementary, affecting exploration and
                 exploitation in genetic search. Unlike other machine
                 learning techniques that need extra machinery to
                 control the tradeoff between them, HGP automatically
                 trades off exploration and exploitation.",
  notes =        "EP-95

                 Netscape v1.1 barfs on the url but ftp seems
                 ok.

                 Compares his own adaptive representation GP and Koza's
                 ADFs (together called hierarchical GP) with GP without
                 them using parity functions as the test case. Claims
                 evidence for {"}bottom up evolution thesis{"} ie later
                 in the (successfull) evolutionary process changes are
                 made higher up the function calling hierarchy and they
                 have small rather than dramatic effects.",
}

@TechReport{Rosca:1995:aHGP,
  author =       "Justinian P. Rosca",
  title =        "An Analysis of Hierarchical Genetic Programming",
  institution =  "University of Rochester",
  address =      "Rochester, NY, USA",
  year =         "1995",
  type =         "Technical Report",
  number =       "566",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.tr566.ps.gz",
  abstract =     "Hierarchical genetic programming (HGP) approaches rely
                 on the discovery, modification, and use of new
                 functions to accelerate evolution. This paper provides
                 a qualitative explanation of the improved behavior of
                 HGP, based on an analysis of the evolution process from
                 the dual perspective of diversity and causality. From a
                 static point of view, the use of an HGP approach
                 enables the manipulation of a population of higher
                 diversity programs. Higher diversity increases the
                 exploratory ability of the genetic search process, as
                 demonstrated by theoretical and experimental fitness
                 distributions and expanded structural complexity of
                 individuals. From a dynamic point of view, this report
                 analyzes the causality of the crossover operator.
                 Causality relates changes in the structure of an object
                 with the effect of such changes, i.e. changes in the
                 properties or behavior of the object. The analyses of
                 crossover causality suggests that HGP discovers and
                 exploits useful structures in a bottom-up, hierarchical
                 manner. Diversity and causality are complementary,
                 affecting exploration and exploitation in genetic
                 search. Unlike other machine learning techniques that
                 need extra machinery to control the tradeoff between
                 them, HGP automatically trades off exploration and
                 exploitation.",
  notes =        "Some of the discussions in this report are summarized
                 in Rosca:1995:cause",
}

@TechReport{rosca:1995:ml,
  author =       "Justinian P. Rosca",
  title =        "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  institution =  "University of Rochester, National Resource Laboratory
                 for the Study of Brain and Behavior",
  year =         "1995",
  type =         "Technical Report",
  number =       "95.2",
  address =      "Rochseter, New York, USA",
  month =        "9 " # jul,
  note =         "Held in conjunction with the twelfth International
                 Conference on Machine Learning, Tahoe City, California,
                 USA",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.cs.rochester.edu/users/grads/rosca/ml95gpw.html",
  size =         "133 pages",
}

@InProceedings{rosca:1995:entropy,
  author =       "Justinian P. Rosca",
  title =        "Entropy-Driven Adaptive Representation",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "23--32",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.ml.gpw.ps.gz",
  size =         "10 pages",
  abstract =     "In the first genetic programming (GP) book John Koza
                 noticed that fitness histograms give a highly
                 informative global view of the evolutionary process
                 [Koza, 1992]. The idea is further developed in this
                 paper by discussing GP evolution in analogy to a
                 physical system. I focus on three inter-related major
                 goals: (1) Study the the problem of search effort
                 allocation in GP; (2) Develop methods in the GA/GP
                 framework that allow adaptive control of diversity; (3)
                 Study ways of adaptation for faster convergence to
                 optimal solution. An entropy measure based on phenotype
                 classes is introduced which abstracts fitness
                 histograms. In this context, entropy represents a
                 measure of population diversity. An analysis of entropy
                 plots and their correlation with other statistics from
                 the population enables an intelligent adaptation of
                 search control.",
  notes =        "part of rosca:1995:ml free energy. Shannon, 1949.
                 Chaitin 1987. Brief discussion of second law of
                 thermodynamics in natural evolution of living
                 systems.",
}

@InProceedings{Rosca:1995:cause,
  author =       "Justinian Rosca and Dana H. Ballard",
  title =        "Causality in Genetic Programming",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "256--263",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-370-0",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.icga.causality.ps.gz",
  size =         "8 pages",
  abstract =     "Causality relates changes in the structure of an
                 object with the effects of such changes, that is
                 changes in the properties or behavior of the object.
                 This paper analyzes the concept of causality in Genetic
                 Programming (GP) and suggests how it can be used in
                 adapting control parameters for speeding up GP search.
                 We first analyze the effects of crossover to show the
                 weak causality of the GP representation and operators.
                 Hierarchical GP approaches based on the discovery and
                 evolution of functions amplify this phenomenon.
                 However, selection gradually retains strongly causal
                 changes. Causality is correlated to search space
                 exploitation and is discussed in the context of the
                 exploration-exploitation tradeoff. The results
                 described argue for a bottom-up GP evolutionary thesis.
                 Finally, new developments based on the idea of GP
                 architecture evolution [Koza94] are discussed from the
                 causality perspective.",
  notes =        "Rosca:1995:aHGP is a longer version of this paper",
}

@InProceedings{Rosca-seke95,
  author =       "Justinian P. Rosca",
  title =        "Towards a New Generation of Program Synthesis
                 Approaches",
  booktitle =    "Proceedings of the 7th International Conference on
                 Software Engineering and Knowledge Engineering",
  year =         "1995",
  publisher =    "Knowledge Systems Institute",
  address =      "Skokie, IL 60076, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.seke.prog_synthesis.ps.gz",
  notes =        "This was an invited talk and short position paper
                 published in the proceedings.

                 ",
}

@InProceedings{rosca:1995:tadbb,
  author =       "Justinian Rosca",
  title =        "Towards Automatic Discovery of Building Blocks in
                 Genetic Programming",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "78--85",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.aaai.fs.ps.gz",
  size =         "8 pages",
  abstract =     "This paper presents an algorithm for the discovery of
                 building blocks in genetic programming (GP) called {\it
                 adaptive representation through learning} (ARL). The
                 central idea of ARL is the adaptation of the problem
                 representation, by extending the set of terminals and
                 functions with a set of evolvable subroutines. The set
                 of subroutines extracts common knowledge emerging
                 during the evolutionary process and acquires the
                 necessary structure for solving the problem. ARL
                 supports subroutine creation and deletion. Subroutine
                 creation or discovery is performed automatically based
                 on the differential parent-offspring fitness and block
                 activation. Subroutine deletion relies on a utility
                 measure similar to schema fitness over a window of past
                 generations. The technique described is tested on the
                 problem of controlling an agent in a dynamic and
                 non-deterministic environment. The automatic discovery
                 of subroutines can help scale up the GP technique to
                 complex problems.",
  notes =        "AAAI-95f GP, {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/ Fuller description in
                 rosca:1996:aigp2",
}

@InCollection{rosca:1996:aigp2,
  author =       "Justinian P. Rosca and Dana H. Ballard",
  title =        "Discovery of Subroutines in Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "177--202",
  chapter =      "9",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/96.aigp2.dsgp.ps.gz",
  abstract =     "A fundamental problem in learning from observation and
                 interaction with an environment is defining a good
                 representation, that is a representation which captures
                 the underlying structure and functionality of the
                 domain. This chapter discusses an extension of the
                 genetic programming (GP) paradigm based on the idea
                 that subroutines obtained from blocks of good
                 representations act as building blocks and may enable a
                 faster evolution of even better representations. This
                 GP extension algorithm is called adaptive
                 representation through learning (ARL). It has built-in
                 mechanisms for (1) creation of new subroutines through
                 discovery and generalization of blocks of code; (2)
                 deletion of subroutines. The set of evolved subroutines
                 extracts common knowledge emerging during the
                 evolutionary process and acquires the necessary
                 structure for solving the problem. ARL was successfully
                 tested on the problem of controlling an agent in a
                 dynamic and non-deterministic environment. Results with
                 the automatic discovery of subroutines show the
                 potential to better scale up the GP technique to
                 complex problems.",
  notes =        "

                 The solutions obtained to a complex typical RL problem
                 using a new GP algorithm, Adaptive Representation
                 through Learning, have increased generality and
                 quality.

                 Population entropy used as decision criterion by ARL.",
}

@InProceedings{rosca:1996:gVsGP,
  author =       "Justinian Rosca",
  title =        "Generality Versus Size in Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "381--387",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/96.gp.ps.gz",
  size =         "6 pages",
  abstract =     "Genetic Programming (GP) uses variable size
                 representations as programs. Size becomes an important
                 and interesting emergent property of the structures
                 evolved by GP. The size of programs can be both a
                 controlling and a controlled factor in GP search. Size
                 influences the efficiency of the search process and is
                 related to the generality of solutions. This paper
                 analyzes the size and generality issues in standard GP
                 and GP using subroutines and addresses the question
                 whether such an analysis can help control the search
                 process. We relate the size, generalization and
                 modularity issues for programs evolved to control an
                 agent in a dynamic and non-deterministic environment,
                 as exemplified by the Pac-Man game.",
  notes =        "GP-96",
}

@InProceedings{rosca:1996:edhb,
  author =       "J. Rosca and D. H. Ballard",
  title =        "Evolution-based discovery of hierarchical behaviors",
  booktitle =    "Proceedings of the Thirteenth National Conference on
                 Artificial Intelligence (AAAI-96)",
  year =         "1996",
  publisher =    "AAAI / The MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/96.aaai.ps.gz",
  abstract =     "The complexity of policy learning in a reinforcement
                 learning task deteriorates primarily with the increase
                 of the number of observations. Unfortunately, the
                 number of observations may be unacceptably high even
                 for simple problems. In order to cope with the scale up
                 problem we adopt procedural representations of
                 policies. Procedural representations have two
                 advantages. First they are implicit, allowing for good
                 inductive generalization over a very large set of input
                 states. Second they facilitate modularization. In this
                 paper we compare several randomized algorithms for
                 learning modular procedural representations. The main
                 algorithm, called Adaptive Representation through
                 Learning (ARL) is a genetic programming extension that
                 relies on the discovery of subroutines. ARL is suitable
                 for learning hierarchies of subroutines and for
                 constructing policies to complex tasks. When the
                 learning problem cannot be solved because the
                 specification is too loose and the domain is not well
                 understood, ARL will discover regularities in the
                 problem environment in the form of subroutines, which
                 often lead to an easier problem solving. ARL was
                 successfully tested on a typical reinforcement learning
                 problem of controlling an agent in a dynamic and
                 non-deterministic environment where the discovered
                 subroutines correspond to agent behaviors.",
  notes =        "

                 Pac-Man ARL {"}differential fitness and block
                 activation heuristics{"} {"}two distinct tiers{"}
                 typed, random search, simulated annealing (PSA),
                 hand-coding, See also rosca:1996:video",
}

@Misc{rosca:1996:video,
  author =       "Justinian Rosca",
  booktitle =    "AAAI-96 Video Program",
  year =         "1996",
  address =      "Portland, Oregon, USA",
  month =        "4-8 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Shows behaviour of evolved programs which play
                 Pac-Man. See also rosca:1996:edhb",
}

@TechReport{rosca:1996:cdectr,
  author =       "Justinian P. Rosca and Dana H. Ballard",
  title =        "Complexity Drift in Evolutionary Computation with Tree
                 Representations",
  institution =  "University of Rochester, Computer Science Department",
  year =         "1996",
  type =         "Technical Report",
  number =       "NRL5",
  address =      "Rochester, NY, USA",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "One serious problem of standard Genetic Programming
                 (GP) is that evolved expressions appear to drift
                 towards large and slow forms on average. This report
                 presents a novel analysis of the role played by
                 variable complexity in the selection and survival of GP
                 expressions. It defines a particular property of GP
                 representations, called {\it rooted tree-schema}, that
                 sheds light on the role of variable complexity of
                 evolved representations. A tree-schema is a relation on
                 the space of tree-shaped structures which provides a
                 quantifiable partitioning of the search space. The
                 present analysis answers questions such as: What role
                 does variable complexity play in the selection and
                 survival of evolved expressions? What is the influence
                 of a parsimony penalty? How heavy should parsimony
                 penalty be weighted or how should it be adapted in
                 order to preserve the underlying optimization process?
                 Are there alternative approaches to simulating a
                 parsimony penalty that do not result in a change of the
                 fitness landscape? The present report provides
                 theoretical answers to these questions, interpretation
                 of these results, and an experimental perspective.",
  notes =        "Section 2 Schemata Theory",
  size =         "30 pages",
}

@InProceedings{Rosca:1997:cdGP,
  author =       "Justinian P. Rosca",
  title =        "Analysis of Complexity Drift in Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "286--294",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@PhdThesis{rosca:thesis,
  author =       "Justinian P. Rosca",
  title =        "Hierarchical Learning with Procedural Abstraction
                 Mechanisms",
  school =       "Department of Computer Science, The College of Arts
                 and Sciences, University of Rochester",
  year =         "1997",
  address =      "Rochester, NY 14627, USA",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Procedural
                 Representations, Stochastic Search, Reinforcement
                 Learning, Hierarchical Abstraction, Problem
                 Decomposition, Generalization and Complexity",
  URL =          "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/jrphdd.ps.gz",
  size =         "260 pages",
  abstract =     "The emerging field called evolutionary computation
                 (EC) consists of the design and analysis of
                 probabilistic algorithms inspired by the principles of
                 natural selection and variation. Genetic Programming
                 (GP) is one subfield of EC that emphasizes (1) the
                 capability to discover and exploit intrinsic
                 characteristics of the problem domain and (2) the
                 flexibility to adapt the shape and complexity of
                 structures manipulated in order to fit the specificity
                 of the application. GP appears to be a robust
                 stochastic search technique that can be applied to
                 complex design, control, and knowledge discovery
                 applications. However, the computational load when
                 applying it to non-trivial problems is considerable.
                 The problem of understanding and controlling the GP
                 technique is notoriously difficult.

                 This dissertation describes theoretical and
                 experimental work aimed at (1) understanding the
                 characteristics and limitations of GP search, and (2)
                 extending the capabilities of GP in order to cope with
                 problems of increasing complexity. For the first
                 challenge, we focus on the properties and the role of
                 the problem representation and analyze the implicit
                 biases when manipulating variable length structures
                 represented as trees. We analyze uniform random
                 sampling on the space of tree structures to offer
                 insights into the role of procedural representations.
                 We describe the dynamics of a useful structural
                 property of representations called rooted tree
                 schemata. This demonstrates the role played by the
                 adaptive complexity of evolved structures. We also
                 capture the dynamics of behaviors using the concept of
                 population entropy.

                 The solution to the second challenge relies on the
                 observation that on non-trivial problems GP essentially
                 assembles and adapts the bits and pieces of a huge
                 monolithic model. We propose, instead, that the
                 learning system provide abstraction mechanisms for
                 adaptively creating and exploiting modularity and
                 problem decomposition. We evaluate previous steps in
                 this direction by looking at GP search biases and
                 complexity measures of solutions, such as expanded and
                 descriptional complexity, and we characterize the types
                 of modular structures that would result in a minimum
                 description length of solutions. Then we describe two
                 GP extension approaches for creating and exploiting
                 procedural abstractions in the form of subroutines in
                 order to facilitate scale-up. The first extension,
                 called Adaptive Representation (AR) is a heuristic
                 modular learning approach based on the discovery of
                 hierarchies of subroutines. The second extension,
                 called Evolutionary Divide-and-Conquer (EDC) views the
                 population as a pool of candidates for selecting a team
                 that solve the problem. Both techniques extract simple
                 or {"}natural{"} relationships and build modular
                 representations to explain data. The techniques are
                 brought to life in several increasingly complex
                 algorithms.

                 The effects of embedding procedural and data
                 abstraction mechanisms in the learning process are
                 evaluated from several perspectives, such as reuse of
                 code or structure, automatic problem decomposition,
                 generalization, and automatic discovery of features on
                 several challenging problems. AR was successfully
                 applied to the intelligent control of an agent in a
                 dynamic and non-deterministic environment. Ideas are
                 further extended for designing graphical models. EDC
                 was applied to regression problems characterized by
                 complex input spaces.",
  notes =        "

                 ",
}

@Unpublished{Rosca:1997:zfigs,
  author =       "Justinian P. Rosca",
  title =        "Fitness-Size Interplay in Genetic Search",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 variable size representation",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "3 pages",
}

@InCollection{rosca:1999:aigp3,
  author =       "Justinian P. Rosca and Dana H. Ballard",
  title =        "Rooted-Tree Schemata in Genetic Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "11",
  pages =        "243--271",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{rosca:1999:fogp,
  author =       "Justinian Rosca",
  title =        "Genetic Programming Acquires Solutions by Combining
                 Top-Down and Bottom-Up Refinement",
  booktitle =    "Foundations of Genetic Programming",
  year =         "1999",
  editor =       "Thomas Haynes and William B. Langdon and Una-May
                 O'Reilly and Riccardo Poli and Justinian Rosca",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/rosca.ps.gz",
  size =         "2 pages",
  notes =        "GECCO'99 WKSHOP, part of haynes:1999:fogp",
}

@Article{rosca:2000:gecco99,
  author =       "Justinian Rosca",
  title =        "Remembering {GECCO}-99 -- {A} Short Summary",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "3",
  pages =        "297--300",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISSN =         "1389-2576",
  notes =        "banzhaf:1999:gecco99",
}

@Unpublished{rosca:esgp,
  author =       "Justinian Rosca and Michael Patrick Johnson and Pattie
                 Maes",
  title =        "Evolutionary Specialization for Problem
                 Decomposition",
  keywords =     "genetic algorithms, genetic programming, species,
                 symbiosis, niches",
  URL =          "http://aries.www.media.mit.edu/people/aries/esgp.ps.gz",
  notes =        "one input variable symbolic regression",
  size =         "11 pages",
}

@PhdThesis{rose:thesis,
  author =       "Carolyn Penstein Rose",
  title =        "Robust Interactive Dialogue Interpretation",
  school =       "Language Technologies Insititute, Carnegie Mellon
                 University",
  year =         "1997",
  note =         "Tech. Rept. CMU-LTI-97-151",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  notes =        "Thu, 26 Feb 1998 16:03:59 EST Eventually when I get
                 around to clearing off some space in my unix account, I
                 plan to put my dissertation on the WWW, but in the mean
                 time, you can order one from CMU. You can email Debra
                 Germany at debra@cs.cmu.edu.

                 ",
}

@InCollection{rose:1999:aigp3,
  author =       "Carolyn Penstein Rose",
  title =        "A Genetic Programming Approach For Robust Language
                 Interpretation",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "4",
  pages =        "67--88",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{rose:1997:DNAFSM,
  author =       "J. A. Rose and Y. Gao and M. Garzon and R. C. Murphy",
  title =        "{DNA} Implementation of Finite-State Machines",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "DNA Computing",
  pages =        "479--490",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{rose:1999:ASMTEABDC,
  author =       "John A. Rose and Russell J. Deaton and Donald R.
                 Franceschetti and Max Garzon and S. Edward Stevens
                 Jr.",
  title =        "A Statistical Mechanical Treatment of Error in the
                 Annealing Biostep of {DNA} Computation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1829--1834",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "dna and molecular computing",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{rosete-suarez:1999:AGDSHC,
  author =       "Alejandro Rosete-Suarez and Alberto Ochoa-Rodriguez
                 and Michele Sebag",
  title =        "Automatic Graph Drawing and Stochastic Hill Climbing",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1699--1706",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{rosete-suarez:1999:E,
  author =       "Alejandro Rosete-Suarez and Alberto Ochoa-Rodriguez
                 and Michele Sebag",
  title =        "Efficient-discarding fitness functions",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "223--228",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{rosin:2000:cecfcasa,
  author =       "Paul L. Rosin and Henry O. Nyongesa",
  title =        "Combining Evolutionary, Connectionist, and Fuzzy
                 Classification Algorithms for Shape Amalysis",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and George D. Smith
                 and David Corne and Martin Oates and Emma Hart and Pier
                 Luca Lanzi and Egbert Jan Willem and Yun Li and Ben
                 Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "87--96",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, C4.5, OC1,
                 Fuzzy IF-THEN rules, ANN, lilgp",
  ISBN =         "3-540-67353-9",
  URL =          "http://www.cs.cf.ac.uk/resources/papers/beans.ps.gz",
  notes =        "{"}...fuzzy classification rules of arbitrary size and
                 structure can be generated using genetic programming{"}
                 page90. Voting Schemes, confusion matrix. Seed shapes:
                 130 examples each with 17 continuous attributes from 9
                 species. {"}...no significant differences between the
                 individual techniques on our classification problem.
                 However, we have shown improvements can be achieved
                 through different combinations of these
                 techniques.

                 EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM,
                 EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April
                 17, 2000
                 Proceedings

                 http://evonet.dcs.napier.ac.uk/evoworkshops/

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67353-9",
}

@Article{ross:1998:ecp,
  author =       "Brian J. Ross",
  title =        "The Evolution of Concurrent Programs",
  journal =      "Applied Intelligence",
  year =         "1998",
  volume =       "8",
  number =       "5",
  pages =        "21--32",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, process
                 algebra, Milner CCS, concurency",
  ISSN =         "0924-669X",
  abstract =     "Process algebra are formal languages used for the
                 rigorous specification and analysis of concurrent
                 systems. By using a process algebra as the target
                 language of a genetic programming system, the
                 derivation of concurrent programs satisfying given
                 problem specifications is possible. A genetic
                 programming system based on Koza's model has been
                 implemented. The target language used is Milner's CCS
                 process algebra, and is chosen for its conciseness and
                 simplicity. The genetic programming environment needs a
                 few adaptations to the computational characteristics of
                 concurrent programs. In particular, means for
                 efficiently controlling the exponentially large
                 computation spaces that are common with process algebra
                 must be addressed. Experimental runs of the system
                 successfully evolved a number of non--iterative CCS
                 systems, hence proving the potential of evolutionary
                 approaches to concurrent system development.",
  notes =        "Special Issues on Evolutionary Learning, Xin Yao and
                 Don Potter, Guest Editors. not recursive, agents,
                 parity",
}

@InProceedings{ross:1998:pscGPicp,
  author =       "Brian J. Ross",
  title =        "Pairwise Sequence Comparison and the Genetic
                 Programming of Iterative Concurrent Programs",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "338--343",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{ross:1999:LGPDCTG,
  author =       "Brian J. Ross",
  title =        "Logic-based Genetic Programming with Definite Clause
                 Translation Grammars",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1236",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ross:1999:P,
  author =       "Brian J. Ross",
  title =        "Probabilistic pattern matching and the evolution of
                 stochastic regular expressions",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "229--237",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms, Genetic Programming",
  notes =        "GECCO-99LB",
}

@InCollection{Ross1999,
  author =       "Brian J. Ross",
  title =        "A Lamarckian Evolution Strategy for Genetic
                 Algorithms",
  booktitle =    "Practical Handbook of Genetic Algorithms: Complex
                 Coding Systems",
  publisher =    "CRC Press",
  year =         "1999",
  editor =       "Lance D. Chambers",
  volume =       "3",
  address =      "Boca Raton, Florida",
  edition =      "3",
  keywords =     "genetic algorithms, Lamarckian evolution, hill
                 climbing",
  ISBN =         "0-8493-2539-0",
  URL =          "http://www.cosc.brocku.ca/~bross/research/Lamarck.ps",
  pages =        "1--16",
  notes =        "

                 Prolog, TSP",
  size =         "16 pages",
}

@InCollection{Ross2000,
  author =       "Brian J. Ross",
  title =        "The Evolution of Concurrent Systems",
  booktitle =    "Evolution of Engineering and Information Systems and
                 Their Applications",
  publisher =    "CRC Press",
  year =         "2000",
  editor =       "Lakhmi C. Jain",
  series =       "CSC Press international series on computational
                 intelligence",
  address =      "Boca Raton, Florida",
  keywords =     "genetic programming, process algebra, concurrency,
                 CCS",
  ISBN =         "0-8493-1965-X",
  pages =        "33--64",
  abstract =     "The use of evolutionary programming towards evolving
                 concurrent programs is investigated. Using Koza's
                 genetic programming paradigm, concurrent programs
                 taking the form of process algebra expressions are
                 evolved. The process algebra used is Milner's Calculus
                 of Communicating Systems. One experiment evolves a
                 program exploiting interleaving. The other experiment
                 evolves a cycling concurrent program. Related work in
                 the literature is also surveyed.",
  notes =        "

                 scheduler. (DNA) sequence comparison, edit distance, up
                 to 8 levels of iteration",
  size =         "33 pages",
}

@InProceedings{BRoss:2000:GECCO,
  author =       "Brian J. Ross",
  title =        "The Effects of Randomly Sampled Training Data on
                 Program Evolution",
  pages =        "443--450",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{BRoss2:2000:GECCO,
  author =       "Brian J. Ross and Frank Fueten and Dmytro Y. Yashkir",
  title =        "Edge Detection of Petrographic Images Using Genetic
                 Programming",
  pages =        "658--665",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@Article{Ross:2000:ppmGP,
  author =       "Brian J. Ross",
  title =        "Probabilistic Pattern Matching and the Genetic
                 Programming of Stochastic Regular Expressions",
  journal =      "International Journal of Applied Intelligence",
  year =         "2000",
  volume =       "3",
  number =       "3",
  pages =        "285--300",
  month =        nov # "/" # dec,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The use of genetic programming for probabilistic
                 pattern matching is investigated. A stochastic regular
                 expression language is used. The language features a
                 statistically sound semantics, as well as a syntax that
                 promotes efficient manipulation by genetic programming
                 operators. An algorithm for efficient string
                 recognition based on approaches in conventional regular
                 language recognition is used. When attempting to
                 recognize a particular test string, the recognition
                 algorithm computes the probabilities of generating that
                 string and all its prefixes with the given stochastic
                 regular expression. To promote efficiency, intermediate
                 computed probabilities that exceed a given cut-off
                 value will pre-empt particular interpretation paths,
                 and hence prune unconstructive interpretation. A few
                 experiments in recognizing stochastic regular languages
                 are discussed. Application of the technology in
                 bioinformatics is in progress.",
}

@InProceedings{ross:1996:vesaGP,
  author =       "Steven J. Ross and Jason M. Daida and Chau M. Doan and
                 Tommaso F. Bersano-Begey and Jeffrey J. McClain",
  title =        "Variations in Evolution of Subsumption Architectures
                 Using Genetic Programming: The Wall Following Robot
                 Revisited",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "191--199",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/GP96_robot/GP96_robot.pdf",
  size =         "9 pages",
  notes =        "GP-96, There is also a figure page
                 ftp://ftp.eecs.umich.edu/people/daida/papers/GP96_robot/GP96_robot_fig.pdf
                 and code http://www.sprl.umich.edu/acers/wfr/wfr.html",
}

@InProceedings{ross:2001:gecco,
  title =        "The Evaluation of a Stochastic Regular Motif Language
                 for Protein Sequences",
  author =       "Brian J. Ross",
  pages =        "120--128",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, motif,
                 stochastic regular expressions, grammatical genetic",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@Article{ross:2001:ngc,
  author =       "Brian J. Ross",
  title =        "Logic-based Genetic Programming with Definite Clause
                 Translation Grammars",
  journal =      "New Generation Computing",
  year =         "2001",
  volume =       "19",
  number =       "4",
  pages =        "313--337",
  keywords =     "genetic algorithms, genetic programming, definite
                 clause translation grammars, Prolog",
  abstract =     "DCTG-GP is a genetic programming system that uses
                 definite clause translation grammars. A DCTG is a
                 logical version of an attribute grammar that supports
                 the definition of context--free languages, and it
                 allows semantic information associated with a language
                 to be easily accommodated by the grammar. This is
                 useful in genetic programming for defining the
                 interpreter of a target language, or incorporating both
                 syntactic and semantic problem-specific constraints
                 into the evolutionary search. The DCTG-GP system
                 improves on other grammar-based GP systems by
                 permitting non--trivial semantic aspects of the
                 language to be defined with the grammar. It also
                 automatically analyses grammar rules in order to
                 determine their minimal depth and termination
                 characteristics, which are required when generating
                 random program trees of varied shapes and sizes. An
                 application using DCTG-GP is described.",
  notes =        "http://www.ohmsha.co.jp/ngc/",
}

@Article{ross:2002:ngc,
  author =       "Brian J. Ross",
  title =        "The Evolution of Stochastic Regular Motifs for Protein
                 Sequences",
  journal =      "New Generation Computing",
  year =         "2002",
  volume =       "20",
  number =       "2",
  pages =        "187--213",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, protein,
                 motif, stochastic regular expressions",
  URL =          "http://www.ohmsha.co.jp/ngc/ngc2002.htm",
  abstract =     "Stochastic regular motifs are evolved for protein
                 sequences using genetic programming. The motif
                 language, SRE-DNA, is a stochastic regular expression
                 language suitable for denoting biosequences. Three
                 restricted versions of SRE-DNA are used as target
                 languages for evolved motifs. The genetic programming
                 experiments are implemented in DCTG-GP, which is a
                 genetic programming system that uses logic--based
                 attribute grammars to define the target language for
                 evolved programs. Earlier preliminary work tested
                 SRE-DNA's viability as a representation language for
                 aligned protein sequences. This work establishes that
                 SRE-DNA is also suitable for evolving motifs for
                 unaligned sets of sequences.",
}

@Article{ross:2002:mva,
  author =       "Brian J. Ross and Frank Fueten and Dmytro Yashkir",
  title =        "Automatic Mineral Identification Using Genetic
                 Programming",
  journal =      "Machine Vision and Applications",
  year =         "2001",
  volume =       "13",
  number =       "2",
  pages =        "61--69",
  keywords =     "genetic algorithms, genetic programming, mineral
                 classification, feature space thresholding",
  URL =          "http://link.springer.de/link/service/journals/00138/papers/1013002/10130061.pdf#xml=http://athene.em.springer.de/search97cgi/s97_cgi?action=view&VdkVgwKey=%2Fjour%2Fjour%2F00138%2Fpapers%2F1013002%2F10130061.pdf&doctype=xml&collection=springer02&queryZIP=%28%22computer%22%29and%28%22vision%22%29and%28%22and%22%29and%28%22applications%22%29",
  URL =          "http://www.cosc.brocku.ca/~bross/research/machvisapps.pdf",
  abstract =     "Automatic mineral identification using evolutionary
                 computation technology is discussed. Thin sections of
                 mineral samples are photographed digitally using a
                 computer-controlled rotating polarizer stage on a
                 petrographic microscope. A suite of image processing
                 functions is applied to the images. Filtered image data
                 for identified mineral grains is then selected for use
                 as training data for a genetic programming system,
                 which automatically synthesizes computer programs that
                 identify these grains. The evolved programs use a
                 decision tree structure that compares the mineral image
                 values with one other, resulting in a thresholding
                 analysis of the multi-dimensional colour and textural
                 space of the mineral images",
}

@InProceedings{ross2:2002:gecco,
  author =       "Brian J. Ross and Anthony G. Gualtieri and Frank
                 Fueten and Paul Budkewitsch",
  title =        "Hyperspectral Image Analysis Using Genetic
                 Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1196--1203",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, hyperspectral imaging, mineral
                 classification",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002) See also ross2:2002:geccoTR",
}

@TechReport{ross2:2002:geccoTR,
  author =       "Brian J. Ross and Anthony G. Gualtieri and Frank
                 Fueten and Paul Budkewitsch",
  title =        "Hyperspectral Image Analysis Using Genetic
                 Programming",
  institution =  "Department of Computer Science, Brock University",
  year =         "2002",
  type =         "Technical Report",
  number =       "CS-02-12",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cosc.brocku.ca/Department/Research/TR/cs0212.pdf",
  abstract =     "Genetic programming is used to evolve mineral
                 identification functions for hyperspectral images. The
                 input image set comprises 168 images from different
                 wavelengths ranging from 428 nm (visible blue) to 2507
                 nm (invisible shortwave in the infrared), taken over
                 Cuprite, Nevada, with the AVIRIS hyperspectral sensor.
                 A composite mineral image indicating the overall
                 reflectance percentage of three minerals (alunite,
                 kaolnite, buddingtonite) is used as a reference or
                 {"}solution{"} image. The training set is manually
                 selected from this composite image. The task of the GP
                 system is to evolve mineral identifiers, where each
                 identifier is trained to identify one of the three
                 mineral specimens. A number of different GP experiments
                 were undertaken, which parameterised features such as
                 thresholded mineral reflectance intensity and target GP
                 language. The results are promising, especially for
                 minerals with higher re ectance thresholds (more
                 intense concentrations). One complication in using this
                 technology is the time and expertise required to
                 interpret the data. Hyperspectral imaging systems such
                 as the NASA/JPL AVIRIS 1 sensor can capture over 200
                 bandwidths for a single geographic location (Green et
                 al. 1998). This is denoted by a hyperspectral cube,
                 which takes the form of many hundreds of mega-bytes of
                 information. Interpreting this massive amount of data
                 is difficult, especially considering that the spectra
                 obtained represent mixed spectral signatures of a
                 variety of materials. Moreover, noise and other
                 unwanted effects must be considered. Deciphering this
                 enormous volume of cryptic data is therefore next to
                 impossible for humans to do manually.",
  size =         "9 pages. See also ross2:2002:gecco",
}

@InProceedings{rossi:2001:wsc6,
  author =       "Roberto Rossi and Valentino Liberali and Andrea G. B.
                 Tettamanzi",
  title =        "An Application of Genetic Programming to Electronic
                 Design Automation: from Frequency Specifications to
                 {VHDL} Code",
  booktitle =    "Soft Computing and Industry Recent Applications",
  year =         "2001",
  editor =       "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and
                 Takeshi Furuhashi and Frank Hoffmann",
  pages =        "809--820",
  month =        "10--24 " # sep,
  publisher =    "Springer-Verlag",
  note =         "Published 2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-85233-539-4",
  notes =        "WSC6
                 http://www.springer.de/cgi/svcat/search_book.pl?isbn=1-85233-539-4",
}

@PhdThesis{GeneticDesignTR,
  author =       "Gerald P. Roston",
  title =        "A Genetic Methodology for Configuration Design",
  month =        Dec,
  year =         "1994",
  school =       "Mechanical Engineering, Carnegie Mellon University",
  address =      "Pittsburgh, PA 15213-3891, USA",
  keywords =     "genetic algorithms, genetic programming",
  size =         "170 pages",
  notes =        "

                 Basically, it describes the use of a grammar to
                 generate engineering designs. The grammar is then
                 mapped to an STGP system, which searches for an
                 {"}appropriate{"} design based on a set of
                 condition.

                 some details from
                 http://www.cs.cmu.edu/afs/cs.cmu.edu/user/clamen/mosaic/reports/robotics.html
                 Also available as CMU-RI-TR-94-42",
}

@Article{roston:1995:gdmsc,
  author =       "G. Roston and R. Sturges",
  title =        "A Genetic Design Methodology for Stucture
                 Configuration",
  journal =      "ASME Advances in Design Automation",
  year =         "1995",
  volume =       "DE 82",
  pages =        "73--90",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@Article{roston:1996:scdgdn,
  author =       "G. Roston and R. Sturges",
  title =        "Stucture Configuration-Design using Genetic Design
                 Methods",
  journal =      "MicroComputer in Civil Engineering",
  year =         "1995",
  note =         "Accepted for publication",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{rothermich:2002:gecco:lbp,
  title =        "Studying the Emergence of Multicellularity with
                 Cartesian Genetic Programming in Artificial Life",
  author =       "Joseph A. Rothermich and Julian F. Miller",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "397--403",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming, alife",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 slime mold aggregation simulation",
}

@InProceedings{rothlauf:1999:TA,
  author =       "Franz Rothlauf and David Goldberg",
  title =        "Tree network design with genetic algorithms - An
                 investigation in the locality of the pruefernumber
                 encoding",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "238--244",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InCollection{roumeliotis:2000:ACTJHAEOCB,
  author =       "George Roumeliotis",
  title =        "Avoiding Collisions and Traffic James on the Highway -
                 An Example of Optimizing Cooperative Behavior",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "319--324",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{rowe:1999:F,
  author =       "Jonathan E. Rowe",
  title =        "Finding attractors for periodic fitness functions",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "557--563",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{Rowe01,
  author =       "Jon E Rowe and Nicholas F McPhee",
  title =        "The effects of crossover and mutation operators on
                 variable length linear structures.",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-01-7",
  month =        jan,
  year =         "2001",
  email =        "J.E.Rowe@cs.bham.ac.uk, N.F.McPhee@cs.bham.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  file =         "/2001/CSRP-01-07.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2001/CSRP-01-07.ps.gz",
  abstract =     "In the search space of variable length strings, it is
                 possible to define crossover and mutation operators
                 that are equivalent to those used in genetic
                 programming on tree structures. We study the effects of
                 these operators on the lengths of strings within a
                 population. It is shown that the distributions by which
                 different string lengths are sampled are strongly
                 biased. To investigate these biases, the effects of
                 repeated application of the operators (without regard
                 for fitness) is considered, and in some cases the
                 fixed-point distributions are found.",
}

@InProceedings{rowland:2002:adisabgp,
  author =       "Jem J. Rowland and Janet Taylor",
  title =        "Adaptive Denoising in Spectral Analysis by Genetic
                 Programming",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "133--138",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
}

@TechReport{rowley:1994:tkvGP,
  author =       "Timothy Rowley",
  title =        "A Toolkit for Visual Genetic Programming",
  institution =  "The Geometry Center, University of Minnesota",
  year =         "1994",
  number =       "GCG-74",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.geom.umn.edu/~trowley/genetic/report.ps.gz",
  abstract =     "Genetic programming is an area of research in computer
                 science interested in finding an {"}optimal{"} solution
                 in a large search space. Traditional genetic
                 programming relies on a fitness function which gives an
                 indication of how close a given genome is to optimal.
                 Unfortunately, many interesting problems don't have any
                 natural fitness function, such as images, objects,
                 L-Systems, music, etc... I wrote a general toolkit for
                 investigating such problems. Judging from the reactions
                 of people who have used the toolkit, it is an easily
                 understood, useable, and addicting system for exploring
                 visual genetic programming.",
  notes =        "http://www.geom.umn.edu/~trowley/genetic/",
}

@InCollection{rubinstein:2000:EQCGP,
  author =       "Ben I. P. Rubinstein",
  title =        "Evolving Quantum Circuits using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "325--334",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.student.unimelb.edu.au/~bir/paper.pdf",
  abstract =     "This paper presents a new representation and
                 corresponding set of genetic operators for a scheme to
                 evolve quantum circuits with various properties. The
                 scheme is a variant on the techniques of genetic
                 programming and genetic algorithms, having components
                 borrowed from each. By recognising the foundation of a
                 quantum circuit as being a collection of gates, each
                 operating on various categories of qubits and each
                 taking parameters, the scheme can successfully search
                 for most circuits. The algorithm is applied to the
                 problem of entanglement production.",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{rubinstein:2001:eqcgp,
  author =       "B. I. P. Rubinstein",
  title =        "Evolving Quantum Circuits using Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "144--151",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, quantum
                 algorithms, automated design",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =

                 Quantum Entanglement Production. Run time 30-sec to 2
                 minutes. Schemata counting",
}

@InProceedings{Rush:1994:ecRob,
  author =       "J. R. Rush and A. P. Fraser and Barnes D. P.",
  title =        "Evolving co-operation in autonomous robotic systems",
  booktitle =    "Proceedings of the IEE International Conference on
                 Control, March 21-24, 1994",
  year =         "1994",
  publisher_address = "London, UK",
  organisation = "IEE",
  keywords =     "genetic algorithms, genetic programming",
}

@InCollection{kinnear:ryan,
  title =        "Pygmies and Civil Servants",
  author =       "Conor Ryan",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "243--263",
  chapter =      "11",
  size =         "21 pages",
  URL =          "ftp://odyssey.ucc.ie/pub/genetic/pygmy.tar.Z",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "home page
                 http://odyssey.ucc.ie/www/user-dirs/conor/publications.html

                 Reduce premature convergence by crossing over between
                 two distinct subpopulations, best (civil servants),
                 short and good (Pygmies)",
}

@InProceedings{ryan:1995:paragen,
  author =       "Conor Ryan and Paul Walsh",
  title =        "Automatic conversion of programs from serial to
                 parallel using Genetic Programming - The Paragen
                 System",
  booktitle =    "Proceedings of ParCo'95",
  year =         "1995",
  publisher_address = "Amsterdam, Holland",
  publisher =    "North-Holland",
  keywords =     "genetic algorithms, genetic programming, paragen,
                 parallel",
  URL =          "ftp://odyssey.ucc.ie/pub/genetic/paragen.ps.Z",
  size =         "8 pages",
  notes =        "Uses Conor's Pygmy algorithm (for correctness v speed
                 (ie parallelism)). Uses population of 20 to 80
                 individuals for 20 to 50 generations.",
}

@InProceedings{ryan:1995:robots,
  author =       "Conor Ryan",
  title =        "{GPR}obots and {GPT}eams - Competition, co-evolution
                 and co-operation in Genetic Programming",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "86--93",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://odyssey.ucc.ie/pub/genetic/robots.ps.Z",
  size =         "8 pages",
  notes =        "AAAI-95f GP, {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@PhdThesis{ryan:thesis,
  author =       "Conor Ryan",
  title =        "Reducing Premature Convergence in Evolutionary
                 Algorithms",
  school =       "University College, Cork",
  year =         "1996",
  address =      "Ireland",
  month =        "2 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://odyssey.ucc.ie/pub/genetic/thesis.ps.Z",
  size =         "138 pages",
  abstract =     "

                 We define Evolutionary Algorithms to be those
                 algorithms which employ or model natural evolution.
                 Generally, when an Evolutionary Algorithm fails to
                 produce a satisfactory solution to a problem, it is
                 because the population has prematurely converged to a
                 suboptimal solution. This thesis seeks to improve the
                 performance of Evolutionary Algorithms by reducing the
                 occurrence of premature convergence. All the extensions
                 presented in this thesis are either naturally occurring
                 phenomena, or are methods employed by biologists and /
                 or plant and animal breeders. In all the cases examined
                 in this thesis, it is shown that the less human control
                 there is with evolution, the better a population will
                 perform. A number of standard benchmark problems are
                 examined, and new, biologically-inspired approaches are
                 presented. A new selection scheme involving multiple
                 fitness functions is introduced. This scheme is applied
                 to the optimisation of multi-objective functions and
                 multi-modal functions. Genetic Programming is applied
                 to a new problem area, the autoparallelisation of
                 serial programs, through the use of techniques
                 developed in this thesis. The notion of addditive
                 diploidy, a type of diploidy that occurs naturally in
                 biology, is introduced and applied to Genetic
                 Algorithms. Additive diploidy is shown to outperform
                 traditional, dominance-oriented, diploidy on a
                 difficult test problem. A new benchmark problem for
                 Genetic Programming is introduced. This
                 competition-oriented benchmark permits the direct
                 comparison of two or more possible solutions. In
                 producing individuals for this benchmark, Genetic
                 Programming is also shown to be suitable for the
                 evolution of event driven programs.",
  notes =        "

                 It is general insofar as it covers several EAs, but the
                 algorithm that gets the most coverage is GP.",
}

@InProceedings{ryan:1997:eppp,
  author =       "Conor Ryan and Paul Walsh",
  title =        "The Evolution of Provable Parallel Programs",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "295--302",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{ryan:1997:scr-e,
  author =       "Conor Ryan and J. J. Collins and Jim Buckley and Tony
                 Cahill and Niall Griffith and Paddy Healy",
  title =        "Soft Computing And Re-Engineering",
  booktitle =    "ET'97 Theory and Application of Evolutionary
                 Computation",
  year =         "1997",
  editor =       "Chris Clack and Kanta Vekaria and Nadav Zin",
  pages =        "13--27",
  address =      "University College London, UK",
  month =        "15 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The Soft Computing and Re-engineering groups at the
                 University of Limerick have recently been granted
                 significant funding for collaborative work. The work is
                 based on developing and integrating the concepts for
                 serial to parallel code transformation. The
                 traditional, hard computing, approaches of
                 Re-Engineering will be combined with Soft Computing
                 approaches with two goals in mind. Firstly to produce a
                 general and practical serial to parallel code
                 translator, and secondly, to compare and contrast the
                 different approaches, specifically to investigate the
                 advantages of combining the two approaches.",
  notes =        "http://www.cs.ucl.ac.uk/isrg/et97/ paragen",
}

@InProceedings{ryan:1998:geepal,
  author =       "Conor Ryan and J. J. Collins and Michael {O Neill}",
  title =        "Grammatical Evolution: Evolving Programs for an
                 Arbitrary Language",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "83--95",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "We describe a Genetic Algorithm that can evolve
                 complete programs. Using a variable length linear
                 genome to govern how a Backus Naur Form grammar
                 definition is mapped to a program, expressions and
                 programs of arbitrary complexity may be evolved. Other
                 automatic programming methods are described, before our
                 system, Grammatical Evolution, is applied to a symbolic
                 regression problem.",
  notes =        "EuroGP'98",
}

@InProceedings{ryan:1998:adltap,
  author =       "Conor Ryan and Laur Ivan",
  title =        "Automatic Discovery of Loop Transformations for
                 Autoparallelisation",
  booktitle =    "Late Breaking Papers at EuroGP'98: the First European
                 Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and Marc Schoenauer
                 and Terry Fogarty and Wolfgang Banzhaf",
  pages =        "17--20",
  address =      "Paris, France",
  publisher_address = "School of Computer Science",
  month =        "14-15 " # apr,
  publisher =    "CSRP-98-10, The University of Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  size =         "4 pages",
  notes =        "EuroGP'98LB part of Poli:1998:egplb",
}

@InProceedings{ryan:1998:aplspGP,
  author =       "Conor Ryan and Laur Ivan",
  title =        "Automatic Parallelization of Loops in Sequential
                 Programs using Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "344--349",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{ryan:1998:GEssa,
  author =       "Conor Ryan and Michael O'Neill",
  title =        "Grammatical Evolution: {A} Steady State approach",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{Ryan:1998:mendle,
  author =       "C. Ryan and M. O'Neill and J. J. Collins",
  title =        "Grammatical Evolution: Solving Trigonometric
                 Identities.",
  booktitle =    "In proceedings of Mendel 1998: 4th International
                 Mendel Conference on Genetic Algorithms, Optimisation
                 Problems, Fuzzy Logic, Neural Networks, Rough Sets.",
  year =         "1998",
  pages =        "111--119",
  address =      "Brno, Czech Republic",
  month =        jun # " 24-26",
  publisher =    "Technical University of Brno, Faculty of Mechanical
                 Engineering",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  URL =          "http://www.grammatical-evolution.org/papers/mendel98.ps",
  ISBN =         "80-214-1199-6",
  abstract =     "We describe a Genetic Algorithm that can evolve
                 complete programs using a variable length linear genome
                 to govern the mapping of a Backus Naur Form grammar
                 definition to a program. Expressions and programs of
                 arbitrary complexity may be evolved. Our system,
                 Grammatical Evolution, has already been applied to a
                 symbolic regression problem. Here we apply our system
                 to find Trigonometric Identities for Cos2x.",
}

@InProceedings{ryan:1998:edt,
  author =       "M. D. Ryan and V. J. Rayward-Smith",
  title =        "The Evolution of Decision Trees",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "350--358",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{ryan:1999:apspGP,
  author =       "C. Ryan and L. Ivan",
  title =        "Automatic parallelization of sequential programs using
                 genetic programming",
  booktitle =    "Proceedings 4th International Symposium on Artificial
                 Life and Robotics",
  year =         "1999",
  editor =       "M. Sugisaka",
  address =      "B-Con Plaza, Beppu, Oita, Japan",
  month =        "19-22 " # jan,
  organisation = "Oita University",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AROB'99 Details from www site etc",
}

@InCollection{ryan:1999:aigp3,
  author =       "Conor Ryan and Laur Ivan",
  title =        "An Automatice Software Re-Engineering Tool based on
                 Genetic Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "2",
  pages =        "15--39",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{Ryan:1999:SCASE,
  author =       "Conor Ryan and Laur Ivan",
  title =        "Evolving Equivalent Parallel Programs: Sequences and
                 Iterative Instructions",
  booktitle =    "Proceedings of the 1st International Workshop on Soft
                 Computing Applied to Software Engineering",
  year =         "1999",
  editor =       "Conor Ryan and Jim Buckley",
  pages =        "119--128",
  address =      "University of Limerick, Ireland",
  month =        "12-14 " # apr,
  organisation = "SCARE",
  publisher =    "Limerick University Press",
  keywords =     "genetic algorithms, genetic programming, Paragen",
  ISBN =         "1-874653-52-6",
  notes =        "http://scare.csis.ul.ie/scase99/ SCASE'99",
}

@InProceedings{ryan:1999:apap,
  author =       "Conor Ryan and Laur Ivan",
  title =        "Automatic Parallelization of Arbitrary Programs",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "244--254",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, paragen",
  ISBN =         "3-540-65899-8",
  abstract =     "converting both sequences and loops in a single
                 seamless operation",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@InProceedings{ryan:1999:eepp:sii,
  author =       "Conor Ryan and Laur Ivan",
  title =        "Evolution of Equivalent Parallel Programs: Sequences
                 and Iterative Instructions",
  booktitle =    "Evolutionary computation and parallel processing",
  year =         "1999",
  editor =       "Erick Cantu-Paz and Bill Punch",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  notes =        "GECCO'99 WKSHOP",
}

@InProceedings{ryan:1999:AHGAFCAP,
  author =       "Mark Ryan and Justin Debuse and George Smith and Ian
                 Whittley",
  title =        "A Hybrid Genetic Algorithm for the Fixed Channel
                 Assignment Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1707--1714",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Book{ryan:book,
  author =       "Conor Ryan",
  title =        "Automatic Re-engineering of Software Using Genetic
                 Programming",
  publisher =    "Kluwer Academic Publishers",
  year =         "1999",
  volume =       "2",
  series =       "Genetic Programming",
  month =        "1 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7923-8653-1",
  URL =          "http://www.wkap.nl/book.htm/0-7923-8653-1",
  notes =        "http://www.amazon.com/exec/obidos/ASIN/0792386531/qid%3D943291341/102-9266197-5591202
                 Automatic Re-engineering of Software Using Genetic
                 Programming describes the application of Genetic
                 Programming to a real world application area --
                 software re-engineering in general and automatic
                 parallelization specifically. Unlike most uses of
                 Genetic Programming, this book evolves sequences of
                 provable transformations rather than actual programs.
                 It demonstrates that the benefits of this approach are
                 twofold: first, the time required for evaluating a
                 population is drastically reduced, and second, the
                 transformations can subsequently be used to prove that
                 the new program is functionally equivalent to the
                 original. Automatic Re-engineering of Software Using
                 Genetic Programming shows that there are applications
                 where it is more practical to use GP to assist with
                 software engineering rather than to entirely replace
                 it. It also demonstrates how the author isolated
                 aspects of a problem that were particularly suited to
                 GP, and used traditional software engineering
                 techniques in those areas for which they were adequate.
                 Contents

                 Preface. Acknowledgments. Foreword. 1. Introduction. 2.
                 Genetic Programming. 3. Software Re-Engineering. 4.
                 Multi-Objective Problems. 5. Paragen I. 6. Practical
                 Considerations. 7. Paragen II. 8. Conclusions.
                 References. Index.",
  size =         "160 pages",
}

@InProceedings{ryan:2000:paragen1,
  author =       "Conor Ryan and Laur Ivan",
  title =        "Paragen - The first results",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "338--348",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@Article{ryan:2000:IS,
  author =       "Conor Ryan",
  title =        "Genetic programming tools have the answers",
  journal =      "IEEE Intelligent Systems",
  year =         "2000",
  volume =       "15",
  number =       "3",
  pages =        "78--80",
  month =        may # "-" # jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1094-7167",
  URL =          "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf",
  size =         "3 pages",
  notes =        "part of hirsh:2000:GP",
}

@Article{ryan:2000:gp3,
  author =       "Conor Ryan",
  title =        "Book Review: Genetic Programming 3: Darwinian
                 Invention and Problem Solving",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "4",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISSN =         "1389-2576",
  notes =        "koza:gp3",
}

@InProceedings{ryan:2001:gecco,
  title =        "No Coercion and No Prohibition - {A} Position
                 Independent Encoding Scheme for Evolutionary
                 Algorithms",
  author =       "Conor Ryan and Michael O'Neill and Atif Azad",
  pages =        "187",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{ryan:2002:EuroGP,
  title =        "No Coercion and No Prohibition, {A} Position
                 Independent Encoding Scheme for Evolutionary
                 Algorithms---The {Chorus} System",
  author =       "Conor Ryan and Atif Azad and Alan Sheahan and Michael
                 O'Neill",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "131--141",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "We describe a new encoding system, Chorus, for grammar
                 based Evolutionary Algorithms. This scheme is coarsely
                 based on the manner in nature in which genes produce
                 proteins that regulate the metabolic pathways of the
                 cell. The phenotype is the behaviour of the cells
                 metabolism, which corresponds to the development of the
                 computer program in our case. In this procedure, the
                 actual protein encoded by a gene is the same regardless
                 of the position of the gene within the genome.

                 We show that the Chorus system has a very convenient
                 Regular Expression - type schema notation that can be
                 used to describe the presence of various phenotypes or
                 phenotypic traits. This schema notation is used to
                 demonstrate that massive areas of neutrality can exist
                 in the search landscape, and the system is also shown
                 to be able to dispense with large areas of the search
                 space that are unlikely to contain useful solutions.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{ryan:2002:EuroGPa,
  title =        "Genetic Algorithms Using Grammatical Evolution",
  author =       "Conor Ryan and Miguel Nicolau and Michael O'Neill",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "278--287",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "This paper describes the GAUGE system, Genetic
                 Algorithms Using Grammatical Evolution. GAUGE is a
                 position independent Genetic Algorithm that uses
                 Grammatical Evolution with an attribute grammar to
                 dictate what position a gene codes for. GAUGE suffers
                 from neither under-specification nor
                 over-specification, is guaranteed to produce
                 syntactically correct individuals, and does not require
                 any repair after the application of genetic operators.
                 GAUGE is applied to the standard onemax problem, with
                 results showing that its genotype to phenotype mapping
                 and position independence nature do not affect its
                 performance as a normal genetic algorithm. A new
                 problem is also presented, a deceptive version of the
                 Mastermind game, and we show that GAUGE possesses the
                 position independence characteristics it claims, and
                 outperforms several genetic algorithms, including the
                 competent genetic algorithm messy GA.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{ryan:2002:gecco:workshop,
  title =        "How to do Anything With Grammars",
  author =       "Conor Ryan and Michael O'Neill",
  pages =        "116--119",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{rylander:2000:OG,
  author =       "Bart Rylander",
  title =        "On {GP} complexity",
  booktitle =    "Graduate Student Workshop",
  year =         "2000",
  editor =       "Conor Ryan and Una-May O'Reilly and William B.
                 Langdon",
  pages =        "309--311",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{rylander:2001:EuroGP,
  author =       "Bart Rylander and Terry Soule and James Foster",
  title =        "Computational Complexity, Genetic Programming, and
                 Implications",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "348--360",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Computational
                 Complexity, Quantum Computing",
  ISBN =         "3-540-41899-7",
  size =         "13 pages",
  abstract =     "Recent theory work has shown that a Genetic Program
                 (GP) used to produce programs may have output that is
                 bounded above by the GP itself [l]. This paper presents
                 proofs that show that 1) a program that is the output
                 of a GP or any inductive process has complexity that
                 can be bounded by the Kolmogorov complexity of the
                 originating program; 2) this result does not hold if
                 the random number generator used in the evolution is a
                 true random source; and 3) an optimization problem
                 being solved with a GP will have a complexity that can
                 be bounded below by the growth rate of the minimum
                 length problem representation used for the
                 implementation. These results are then used to provide
                 guidance for GP implementation.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{rylander:2001:gecco,
  title =        "Quantum Evolutionary Programming",
  author =       "Bart Rylander and Terry Soule and James Foster and Jim
                 Alves-Foss",
  pages =        "1005--1011",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, DNA computing
                 or quantum computing or molecular computing, Computers,
                 Quantum Algorithms",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{ryu:1996:MASSON,
  author =       "Tae-Wan Ryu and Christoph F. Eick",
  title =        "{MASSON:} Discovering Commonalties in Collection of
                 Objects using Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "200--208",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.cs.uh.edu/~twryu/papers/gp96.ps",
  size =         "9 pages",
  abstract =     "For the current flood of data, automatic tools for
                 searching or analyzing data are necessary, especially
                 for complex databases. Accordingly, knowledge discovery
                 in databases is getting more and more attention. This
                 paper centers on the problem of discovering the common
                 characteristics that are shared by a set of objects
                 belonging to an object-oriented database. In our
                 approach, commonalities within a set of objects are
                 described by object-oriented queries that compute this
                 set of objects. The paper discusses the architecture of
                 a knowledge discovery system, called MASSON, which
                 employs genetic programming to find such queries, and
                 presents an example run of the system to illustrate how
                 the system works; we will show how interesting queries
                 that describe commonalities within a set of objects are
                 automatically generated, modified, evaluated, and
                 selected; we will also discuss how the search for the
                 {"}best{"} query is conducted by the MASSON system.
                 Specific problems such as the generation of constants
                 in queries, how to cope with type violations and other
                 constraints when creating object-oriented queries, and
                 query evaluation are discussed in some detail.",
  notes =        "GP-96",
}

@InProceedings{ryu:1996:dqeGP,
  author =       "Tae-Wan Ryu and Christoph F. Eick",
  title =        "Deriving Queries From Examples Using Genetic
                 Programming",
  booktitle =    "The Second International Conference on Knowledge
                 Discovery and Data Mining (KDD-96)",
  editor =       "Evangelos Simoudis and Jia Wei Han and Usama Fayyad",
  year =         "1996",
  month =        aug # " 2-4",
  keywords =     "Genetic Algorithms, genetic Programming, data mining,
                 MASSON",
  pages =        "303--306",
  address =      "Portland, Oregon, USA",
  publisher =    "AAAI Press",
  ISBN =         "1-57735-004-9",
  URL =          "http://www.cs.uh.edu/~twryu/papers/kdd96.ps",
  size =         "14 pages",
  abstract =     "This paper centers on the problem of extracting
                 intensional information for a set of objects from an
                 object-oriented database. In our approach, the
                 extracted intensional information for the given set of
                 objects are described by object- oriented queries that
                 compute this set of objects. The paper discusses the
                 architecture of a knowledge discovery system, called
                 MASSON, which employs genetic programming to find such
                 queries, moreover, we will show how interesting queries
                 that describe commonalities within a set of objects are
                 automatically generated, modified, evaluated, and
                 selected; we will also discuss how the search for the
                 {"}best{"} query is conducted by the MASSON system. We
                 also report on an experiment that evaluated the
                 knowledge discovery capability of MASSON.",
  notes =        "KDD-96
                 http://www.aaai.org:80/Press/Proceedings/KDD/1996/kdd-96.html",
}

@InProceedings{Saitou:2000:GECCOlb,
  author =       "Kazuhiro Saitou and Cem M. Baydar",
  title =        "A Genetic Programming Framework for Error Recovery in
                 Robotic Assembly Systems",
  pages =        "346--351",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Part of whitley:2000:GECCOlb",
}

@InProceedings{sakamoto:2001:isdegrngp,
  author =       "Erina Sakamoto and Hitoshi Iba",
  title =        "Inferring a System of Differential Equations for a
                 Gene Regulatory Network by using Genetic Programming",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "720--726",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, least mean
                 squares",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",
}

@InProceedings{MMRG_salhi1996a,
  author =       "Abdel Salhi and H. Glaser and D. {De Roure}",
  title =        "Model Generation Using Genetic Programming",
  booktitle =    "UK Parallel'96",
  year =         "1996",
  editor =       "R. Jesshope and A. V. Shafarenko",
  pages =        "92--109",
  address =      "Guildford",
  publisher =    "Springer",
  email =        "cjg@ecs.soton.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-76068-7",
  URL =          "http://www.bib.ecs.soton.ac.uk/records/900",
  abstract =     "In search and optimisation applications, model
                 building is largely manual. However, relationships
                 binding variables and making up constraints may be
                 automatically generated if a complete enough body of
                 data is available. At present, at the level of most
                 businesses such a body is not only available but
                 unexploited. In the following we shall rely on these
                 data which ultimately can be used in the constraints
                 description of the problem. The efficient
                 implementation of this process is also addressed.",
  notes =        "out of print, August 2002 ? not listed by Springer?",
}

@Article{SGD98,
  author =       "Abdel Salhi and H. Glaser and D. {De Roure}",
  title =        "Parallel implementation of a genetic-programming based
                 tool for symbolic regression",
  journal =      "Information Processing Letters",
  volume =       "66",
  number =       "6",
  pages =        "299--307",
  day =          "30",
  month =        jun,
  year =         "1998",
  CODEN =        "IFPLAT",
  ISSN =         "0020-0190",
  bibdate =      "Sat Nov 7 17:56:00 MST 1998",
  acknowledgement = ack-nhfb,
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 regression",
  abstract =     "We report on a parallel implementation of a tool for
                 symbolic regression, the algorithmic mechanism of which
                 is based on genetic programming, and communication is
                 handled using MPI. The implementation relies on a
                 random islands model (RIM), which combines both the
                 conventional islands model where migration of
                 individuals between islands occurs periodically and
                 niching where no migration takes place. The system was
                 designed so that the algorithm is synergistic with
                 parallel/distributed architectures, and works to make
                 use of processor time and minimum use of network
                 bandwidth without complicating the sequential algorithm
                 significantly. Results on an IBM SP2 are included.",
  notes =        "GP_SR",
}

@InProceedings{salim:1999:GARIPCCSAH,
  author =       "Vivian Salim",
  title =        "Genetic Algorithms in Road Investment Planning with
                 Computational Comparisons to Simulated Annealing and
                 Heuristics",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "808",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{salman:1999:LCFGA,
  author =       "Ayed A. Salman and Kishan Mehrotra and Chilukuri K.
                 Mohan",
  title =        "Linkage Crossover For Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "564--571",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{salomon:1998:egsp,
  author =       "Ralf Salomon",
  title =        "The Evolutionary-Gradient-Search Procedure",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "852--862",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Evolutionary Strategies",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{Salustowicz:97ecj,
  author =       "R. P. Salustowicz and J. Schmidhuber",
  title =        "Probabilistic Incremental Program Evolution",
  journal =      "Evolutionary Computation",
  volume =       "5",
  number =       "2",
  pages =        "123--141",
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming, probabilistic
                 Incremental Program Evolution, probabilistic
                 programming languages, stochastic program search,
                 Population-Based Incremental Learning, partially
                 observable environments",
  URL =          "ftp://ftp.idsia.ch/pub/rafal/PIPE.ps.gz",
  size =         "17 pages",
  abstract =     "Probabilistic Incremental Program Evolution (PIPE) is
                 a novel technique for automatic program synthesis. We
                 combine probability vector coding of program
                 instructions, Population-Based Incremental Learning,
                 and tree-coded programs like those used in some
                 variants of Genetic Programming (GP). PIPE iteratively
                 generates successive populations of functional programs
                 according to an adaptive probability distribution over
                 all possible programs. Each iteration it uses the best
                 program to refine the distribution. Thus, it
                 stochastically generates better and better programs.
                 Since distribution refinements depend only on the best
                 program of the current population, PIPE can evaluate
                 program populations efficiently when the goal is to
                 discover a program with minimal runtime. We compare
                 PIPE to GP on a function regression problem and the
                 6-bit parity problem. We also use PIPE to solve tasks
                 in partially observable mazes, where the best programs
                 have minimal runtime.",
  notes =        "Date: Tue, 20 Jan 1998 19:49:23 +0100 (MET)

                 PIPE_v1.0 SOFTWARE PACKAGE

                 Rafal Salustowicz

                 IDSIA, Switzerland

                 Probabilistic Incremental Program Evolution (PIPE) is a
                 novel technique for automatic program synthesis.
                 Details are described in R.P. Salustowicz and J.
                 Schmidhuber, Probabilistic Incremental Program
                 Evolution, Evolutionary Computation, 5(2):123-141,
                 1997. See ftp://ftp.idsia.ch/pub/rafal/PIPE.ps.gz

                 The PIPE_v1.0 software package allows for applying and
                 modifying the PIPE paradigm, and for benchmarking. URLs
                 are:

                 http://www.idsia.ch/~rafal/research.html
                 ftp://ftp.idsia.ch/pub/rafal/PIPE_v1.0.tar.gz

                 The software is written in C. It

                 - is easy to install (comes with an automatic
                 installation tool).

                 - is easy to use: setting up PIPE_V1.0 for different
                 problems requires a minimal amount of programming.
                 User-written, application- independent program parts
                 can easily be reused.

                 - is efficient: PIPE_V1.0 has been tuned to speed up
                 performance.

                 - is portable: comes with source code (optimized for
                 SunOS 5.5.1).

                 - is extensively documented(!) and contains three
                 example applications.

                 - supports statistical evaluations: it facilitates
                 running multiple experiments and collecting results in
                 output files.

                 - includes testing tool for testing generalization of
                 evolved programs.

                 - supports floating point and integer arithmetic.

                 - has extensive output features.

                 - For lil-gp users: Problems set up for lil-gp 1.0 can
                 be easily ported to PIPE_v1.0. The testing tool can
                 also be used to process programs evolved by lil-gp
                 1.0.

                 PIPE_v1.0 is licensed free of charge for non-profit or
                 internal
                 use.

                 **********************************************************************
                 * Rafal Salustowicz * * Istituto Dalle Molle di Studi
                 sull'Intelligenza Artificiale (IDSIA)* * Corso Elvezia
                 36 e-mail: rafal@idsia.ch * * 6900 Lugano
                 ============== * * Switzerland raf@cs.tu-berlin.de * *
                 Tel (IDSIA) : +41 91 91198-38 raf@psych.stanford.edu *
                 * Tel (office): +41 91 91198-32 * * Fax : +41 91
                 91198-39 WWW: http://www.idsia.ch/~rafal *
                 **********************************************************************",
}

@InProceedings{Salustowicz:97iconip,
  author =       "R. P. Sa\l{}ustowicz and M. A. Wiering and J.
                 Schmidhuber",
  title =        "Evolving Soccer Strategies",
  booktitle =    "Progress in Connectionist-based Information Systems:
                 Proceedings of the Fourth International Conference on
                 Neural Information Processing ICONIP'97",
  year =         "1997",
  editor =       "N. Kasabov and R. Kozma and K. Ko and R. O'Shea and G.
                 Coghill and T. Gedeon",
  publisher =    "Springer-Verlag",
  volume =       "1",
  pages =        "502--505",
  publisher_address = "Singapore",
  keywords =     "PIPE",
  URL =          "ftp://ftp.idsia.ch/pub/rafal/ICONIP_soccer.ps.gz",
  size =         "5 pages",
  abstract =     "We study multiagent learning in a simulated soccer
                 scenario. Players from the same team share a common
                 policy for mapping inputs to actions. They get rewarded
                 or punished collectively in case of goals. For varying
                 team sizes we compare the following learning
                 algorithms: TD-Q learning with linear neural networks
                 (TD-Q-LIN), with a neural gas network (TD-Q-NG),
                 Probabilistic Incremental Program Evolution (PIPE), and
                 a PIPE variant based on coevolution (CO-PIPE). TD-Q-LIN
                 and TD-Q-NG try to learn evaluation functions (EFs)
                 mapping input/action pairs to expected reward. PIPE and
                 CO-PIPE search policy space directly. They use adaptive
                 probability distributions to synthesize programs that
                 calculate action probabilities from current inputs. We
                 find that learning appropriate EFs is hard for both
                 EF-based approaches. Direct search in policy space
                 discovers more reliable policies and is faster.",
  notes =        "

                 ",
}

@InProceedings{Salustowicz:97icann,
  author =       "R. P. Sa\l{}ustowicz and M. A. Wiering and J.
                 Schmidhuber",
  title =        "On Learning Soccer Strategies",
  booktitle =    "Proceedings of the Seventh International Conference on
                 Artificial Neural Networks (ICANN'97)",
  editor =       "W. Gerstner and A. Germond and M. Hasler and J.-D.
                 Nicoud",
  year =         "1997",
  volume =       "1327",
  series =       "Lecture Notes in Computer Science",
  pages =        "769--774",
  publisher_address = "Berlin Heidelberg",
  publisher =    "Springer-Verlag",
  keywords =     "PIPE",
  URL =          "ftp://ftp.idsia.ch/pub/rafal/ICANN_soccer.ps.gz",
  size =         "7 pages",
  abstract =     "We use simulated soccer to study multiagent learning.
                 Each team's players (agents) share action set and
                 policy but may behave differently due to
                 position-dependent inputs. All agents making up a team
                 are rewarded or punished collectively in case of goals.
                 We conduct simulations with varying team sizes, and
                 compare two learning algorithms: TD-Q learning with
                 linear neural networks (TD-Q) and Probabilistic
                 Incremental Program Evolution (PIPE). TD-Q is based on
                 evaluation functions (EFs) mapping input/action pairs
                 to expected reward, while PIPE searches policy space
                 directly. PIPE uses an adaptive probability
                 distribution to synthesize programs that calculate
                 action probabilities from current inputs. Our results
                 show that TD-Q has difficulties to learn appropriate
                 shared EFs. PIPE, however, does not depend on EFs and
                 finds good policies faster and more reliably.",
  notes =        "

                 ",
}

@Article{Salustowicz:97mlj,
  author =       "R. P. Sa\l{}ustowicz and M. A. Wiering and J.
                 Schmidhuber",
  title =        "Learning Team Strategies: Soccer Case Studies",
  journal =      "Machine Learning",
  year =         "1998",
  note =         "To appear",
  keywords =     "PIPE",
  URL =          "ftp://ftp.idsia.ch/pub/rafal/soccer.ps.gz",
  abstract =     "We use simulated soccer to study multiagent learning.
                 Each team's players (agents) share action set and
                 policy, but may behave differently due to
                 position-dependent inputs. All agents making up a team
                 are rewarded or punished collectively in case of goals.
                 We conduct simulations with varying team sizes, and
                 compare several learning algorithms: TD-Q learning with
                 linear neural networks (TD-Q), Probabilistic
                 Incremental Program Evolution (PIPE), and a PIPE
                 version that learns by coevolution (CO-PIPE). TD-Q is
                 based on learning evaluation functions (EFs) mapping
                 input/action pairs to expected reward. PIPE and CO-PIPE
                 search policy space directly. They use adaptive
                 probability distributions to synthesize programs that
                 calculate action probabilities from current inputs. Our
                 results show that linear TD-Q encounters several
                 difficulties in learning appropriate shared EFs. PIPE
                 and CO-PIPE, however, do not depend on EFs and find
                 good policies faster and more reliably. This suggests
                 that in some multiagent learning scenarios direct
                 search in policy space can offer advantages over
                 EF-based approaches.",
  notes =        "

                 ",
}

@InProceedings{Salustowicz:97ecml,
  author =       "R. P. Salustowicz and J. Schmidhuber",
  title =        "Probabilistic Incremental Program Evolution:
                 Stochastic Search Through Program Space",
  booktitle =    "Machine Learning: ECML-97",
  editor =       "M. van Someren and G. Widmer",
  publisher =    "Springer-Verlag",
  pages =        "213--220",
  year =         "1997",
  volume =       "1224",
  series =       "Lecture Notes in Artificial Intelligence",
  publisher_address = "Berlin",
  keywords =     "genetic algorithms, genetic programming,
                 Population-Based Incremental Learning, Stochastic
                 Program Search",
  URL =          "ftp://ftp.idsia.ch/pub/rafal/ECML_PIPE.ps.gz",
  size =         "9 pages",
  abstract =     "Probabilistic Incremental Program Evolution (PIPE) is
                 a novel technique for automatic program synthesis. We
                 combine probability vector coding of program
                 instructions [Schmidhuber, 1997], Population-Based
                 Incremental Learning (PBIL) [Baluja and Caruana, 1995]
                 and tree-coding of programs used in variants of Genetic
                 Programming (GP) [Cramer, 1985; Koza, 1992]. PIPE uses
                 a stochastic selection method for successively
                 generating better and better programs according to an
                 adaptive ``probabilistic prototype tree''. No crossover
                 operator is used. We compare PIPE to Koza's GP variant
                 on a function regression problem and the 6-bit parity
                 problem.",
  notes =        "ECML-97",
}

@TechReport{Salustowicz:1998:hpipeTR,
  author =       "Rafal Salustowicz and Juergen Schmidhuber",
  title =        "{H-PIPE}: Facilitating Hierarchical Program Evolution
                 Through Skip Nodes",
  institution =  "IDSIA",
  year =         "1998",
  type =         "Technical Report",
  number =       "IDSIA-8-98",
  address =      "Switzerland",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.idsia.ch/pub/rafal/ICML98_H-PIPE.ps.gz",
  abstract =     "To evolve structured programs we introduce H-PIPE, a
                 hierarchical extension of Probabilistic Incremental
                 Program Evolution (PIPE). Structure is induced by
                 {"}hierarchical instructions{"} (HIs) limited to
                 top-level, structuring program parts. {"}Skip nodes{"}
                 (SNs) inspired by biology's introns (non-coding
                 segments) allow for switching program parts on and off.
                 In our experiments H-PIPE out- performs PIPE, and SNs
                 facilitate synthesis of certain structured programs but
                 not unstructured ones. We conclude that introns can be
                 particularly useful in the presence of structural
                 bias.

                 ",
  notes =        "genetic-programming@cs.stanford.edu Mon, 31 Aug 1998
                 13:51:03 +0200 (MET DST)

                 Short version: Evolving Structured Programs with
                 Hierarchical Instructions and Skip Nodes. In J.
                 Shavlik, ed., Machine Learning: Proceedings of the
                 Fifteenth International Conference (ICML'98), pages
                 488-496, Morgan Kaufmann Publishers, 1998.
                 salustowicz:1998:ICML",
  size =         "pages",
}

@TechReport{Salustowicz:1998:atdpipeTR,
  author =       "Rafal Salustowicz and Juergen Schmidhuber",
  title =        "Learning to predict through Probabilistic Incremental
                 Program Evolution and automatic task decomposition",
  institution =  "IDSIA",
  year =         "1998",
  type =         "Technical Report",
  number =       "IDSIA-11-98",
  address =      "Switzerland",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.idsia.ch/pub/rafal/TR-11-98-filter_pipe.ps.gz",
  abstract =     "Analog gradient-based recurrent neural nets can learn
                 complex prediction tasks. Most, however, tend to fail
                 in case of long minimal time lags between relevant
                 training events. On the other hand, discrete methods
                 such as search in a space of event-memori- zing
                 programs are not necessarily affected at all by long
                 time lags: we show that discrete {"}Probabilistic
                 Incremental Program Evolution{"} (PIPE) can solve
                 several long time lag tasks that have been successfully
                 solved by only one analog method ({"}Long Short- Term
                 Memory{"} - LSTM). In fact, sometimes PIPE even
                 outperforms LSTM. Existing discrete methods, however,
                 cannot easily deal with problems whose solutions
                 exhibit comparatively high algorithmic complexity. We
                 overcome this drawback by introducing filtering, a
                 novel, general, data-driven divide-and-conquer
                 technique for automatic task decomposition that is not
                 limited to a particular learning method. We compare
                 PIPE plus filtering to various analog recurrent net
                 methods.",
  notes =        "genetic-programming@cs.stanford.edu Thu, 17 Sep 1998
                 07:01:21 -0700 (PDT)",
  size =         "pages",
}

@InProceedings{salustowicz:1998:ICML,
  author =       "Rafal Salustowicz and Juergen Schmidhuber",
  title =        "Evolving Structured Programs with Hierarchical
                 Instructions and Skip Nodes",
  booktitle =    "Proceedings of the Fifteenth International Conference
                 on Machine Learning, ICML'98",
  year =         "1998",
  editor =       "Jude Shavlik",
  pages =        "488--496",
  address =      "Madison, Wisconsin, USA",
  month =        jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, Probabilistic
                 Incremental Program Evolution, Structured Programs,
                 Hierarchical Programs, Non-Coding Segments",
  ISBN =         "1-55860-556-8",
  size =         "pages",
  abstract =     "To evolve structured programs we introduce H-PIPE, a
                 hierarchical extension of Probabilistic Incremental
                 Program Evolution (PIPE - Salustowicz and Schmidhuber,
                 1997). Structure is induced by {"}hierarchical
                 instructions{"} (HIs) limited to top-level, structuring
                 program parts. {"}Skip nodes{"} (SNs) allow for
                 switching program parts on and off. They facilitate
                 synthesis of certain structured programs. In our
                 experiments H-PIPE outperforms PIPE: structural bias
                 can speed up program synthesis.",
  notes =        "ICML'98 http://www.cs.wisc.edu/icml98/ See also
                 Salustowicz:1998:hpipeTR",
}

@InProceedings{salustowicz:1999:SLTPATD,
  author =       "Rafal P. Salustowicz and Jurgen Schmidhuber",
  title =        "Sequence Learning Through {PIPE} and Automatic Task
                 Decomposition",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1184--1191",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{samuelsson:2001:debgsaa,
  author =       "Fredrik Samuelsson and Peter Nordin",
  title =        "Distributed Evolution of Behaviour for a Group of
                 Social Autonomous Agents",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "366--371",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, lego
                 mindstorm robot",
  URL =          "http://www.dtek.chalmers.se/~d4sama/Kurser/Exjobb/gecco.pdf",
  notes =        "GECCO-2001LB Abstract of Masters thesis?",
}

@Article{sanchez:2000:TEC,
  author =       "Luciano Sanchez",
  title =        "Interval-valued {GA}-{P} algorithms",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2000",
  volume =       "4",
  number =       "1",
  pages =        "64--72",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, symbolic
                 regression, point estimate, confidence interval, rural
                 spanish electrical energy distribution",
  ISSN =         "1089-778X",
  URL =          "http://ieeexplore.ieee.org/iel5/4235/18295/00843495.pdf",
  size =         "9 pages",
  abstract =     "When genetic programming (GP) methods are applied to
                 solve symbolic regression problems, we obtain a point
                 estimate of a variable, but it is not easy to calculate
                 an associated confidence interval. We designed an
                 interval arithmetic-based model that solves this
                 problem. Our model extends a hybrid technique, the GA-P
                 method, that combines genetic algorithms and genetic
                 programming. Models based on interval GA-P can devise
                 an interval model from examples and provide the
                 algebraic expression that best approximates the data.
                 The method is useful for generating a confidence
                 interval for the output of a model, and also for
                 obtaining a robust point estimate from data which we
                 know to contain outliers. The algorithm was applied to
                 a real problem related to electrical energy
                 distribution. Classical methods were applied first, and
                 then the interval GA-P. The results of both studies are
                 used to compare interval GA-P with GP, GA-P, classical
                 regression methods, neural networks, and fuzzy
                 models.",
}

@Article{Sanchez:2001:IS,
  author =       "Luciano Sanchez and Ines Couso and J. A. Corrales",
  title =        "Combining operators with search to evolve fuzzy rule
                 based classifiers",
  journal =      "Information Sciences",
  volume =       "136",
  pages =        "175--191",
  year =         "2001",
  number =       "1-4",
  keywords =     "genetic algorithms, genetic programming, Fuzzy
                 classification, Simulated annealing",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-43DDW06-B/1/dc4e8685f90cc493d7946e93370ac22f",
  size =         "19 pages",
  abstract =     "The genotype-phenotype encoding of fuzzy rule bases in
                 GA, along with their corresponding crossover and
                 mutation operators, can be used by other search
                 schemes, improving the behavior of these last ones. As
                 a practical consequence of this, a simulated
                 annealing-based method for inducting both parameters
                 and structure of a fuzzy classifier has been developed.
                 The adjacency operator in SA has been replaced with a
                 macromutation taken from tree-shaped genotype GAs. We
                 will show that results of SA search are similar to
                 those of GP in both the efficiency of the learned
                 classifiers and in its linguistic interpretability,
                 while the memory consumption of the learning process is
                 lower.",
}

@InCollection{sanchez:2002:AEAIPMCD,
  author =       "Javier Nicolas Sanchez",
  title =        "An Evolutionary Approach to the Induction of Process
                 Model from Continuous Data",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "213--222",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InCollection{sandberg:2000:ENNS,
  author =       "Magnus Sandberg",
  title =        "Evolving Neural Network Structures",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "335--342",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InCollection{sander:1995:ECDLGP,
  author =       "Hanno Sander",
  title =        "Evolution of Communication and Division of Labor via
                 Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "239--248",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InCollection{santhanam:1998:AGPTR,
  author =       "Kumaran Santhanam",
  title =        "A Genetically Programmed Tone Recognizer",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "157--166",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{santini:2001:EuroGP,
  author =       "Massimo Santini and Andrea Tettamanzi",
  title =        "Genetic Programming for Financial Time Series
                 Prediction",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "361--370",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Time Series
                 prediction, Financial markets, Multi-expression
                 individuals, Genetic operators, Crossover",
  ISBN =         "3-540-41899-7",
  size =         "10 pages",
  abstract =     "This paper describes an application of genetic
                 programming to forecasting financial markets that
                 allowed the authors to rank first in a competition
                 organized within the CEC2000 on {"}Dow Jones
                 Prediction{"}. The approach is substantially driven by
                 the rules of that competition, and is characterized by
                 individuals being made up of multiple GP expressions
                 and specific genetic operators.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{sarafopoulos:1999:agaIFSSTGP,
  author =       "Anargyros Sarafopoulos",
  title =        "Automatic Generation of Affine {IFS} and Strongly
                 Typed Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "149--160",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, STGP",
  ISBN =         "3-540-65899-8",
  abstract =     "Iterated Functions Systems IFS are recursive systems
                 that have applications in modeling, animation and,
                 Fractal Image Compression.... demonstrated with
                 examples its application to the inverse problem for
                 IFS.

                 ",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@InProceedings{sarafopoulos:2001:EuroGP,
  author =       "Anargyros Sarafopoulos",
  title =        "Evolution of Affine Transformations and Iterated
                 Function Systems using Hierarchical Evolution
                 Strategy",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "176--191",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Strongly
                 Typed GP, STGP, Evolution Strategies, Iterated Function
                 Systems",
  ISBN =         "3-540-41899-7",
  size =         "16 pages",
  abstract =     "Often optimization problems involve the discovery of
                 many scalar coefficients. Although genetic programming
                 (GP) has been applied to the optimization and discovery
                 of functions with an arbitrary number of scalar
                 coefficients, recent results indicate that a method for
                 fine-tuning GP scalar terminals can assist the
                 discovery of solutions. In this paper we demonstrate an
                 approach where genetic programming and evolution
                 strategies (ES) are seamlessly combined. We apply our
                 GP/ES hybrid, which we name Hierarchical Evolution
                 Strategy, to the problem of evolving affine
                 transformations and iterated function systems (IFS). We
                 compare the results of our approach with GP and notice
                 an improvement in performance in terms of discovering
                 better solutions and speed.",
  notes =        "EuroGP'2001, part of miller:2001:gp. Best student
                 paper",
}

@InProceedings{sarma:1999:TBSDEANE,
  author =       "Jayshree Sarma and Kenneth De Jong",
  title =        "The Behavior of Spatially Distributed Evolutionary
                 Algorithms in Non-Stationary Environments",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "572--578",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{sawai:1999:PDPPHMM,
  author =       "Hidefumi Sawai and Susumu Adachi",
  title =        "Parallel Distributed Processing of a Parameter-free
                 Hierarchical Migration Methods",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "579--586",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{saxon:1999:XMP,
  author =       "Shaun Saxon and Alwyn Barry",
  title =        "{XCS} and the Monk's Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "809",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{scapel:2000:GP,
  author =       "Nicolas Scapel",
  title =        "Genetic Painter",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "343--350",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{schleiffer:1999:AFNBGFED,
  author =       "Ralf Schleiffer and Hans-Jurgen Sebastian",
  title =        "A Fuzzy Neighborhood Based {GA} in Fuzzy Engineering
                 Design",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1797",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{schloman:2001:gecco,
  title =        "Genetic Programming Evolves a Human-Competitive Player
                 for a Complex, On-line, Interactive, Multi-Player Game
                 of Strategy",
  author =       "John Schloman and Ben Blackford",
  pages =        "188",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@Misc{schmidhuber:1986:prolog,
  author =       "Jurgen Schmidhuber",
  title =        "Der genetische Algorithmus: Eine Implemetierung in
                 Prolog",
  year =         "1986",
  keywords =     "genetic algorithms, genetic programming, Prolog",
  size =         "21 pages",
  notes =        "In German The author states {"}We wrote it while still
                 being undergraduates. The paper does not meet the
                 scientific stanards you would expect from, say, a
                 journal publication.{"} and continues {"}to my
                 knowledge, the paper represents the first publication
                 describing an instance of ``Genetic Programming'' --
                 the application of genetic algorithms to the evolution
                 of variable length computer programs.{"} However says
                 {"}I'd be surprised if there really were no other
                 relevant publications by authors predating ours. If you
                 are aware of any please let me know!{"}",
}

@MastersThesis{schmidhuber:1987:srl,
  author =       "Jurgen Schmidhuber",
  title =        "Evolutionary Principles in Self-Referential Learning.
                 On Learning now to Learn: The Meta-Meta-Meta...-Hook",
  school =       "Technische Universitat Munchen, Germany",
  year =         "1987",
  type =         "Diploma Thesis",
  month =        "14 " # may,
  keywords =     "genetic algorithms, genetic programming
                 self-reference, introsepection, learning, meta,
                 evolution, associative nets, neuronal nets, genetical
                 algorithm, bucket brigade, SALM, PSALM, EURISKO,
                 fractals",
  size =         "62 pages",
}

@InProceedings{schmidhuber:1995:inc,
  author =       "Jurgen Schmidhuber",
  title =        "Beyond {``}Genetic Programming{''}: Incremental
                 Self-Improvement",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "42--49",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.idsia.ch/%7Ejuergen/",
  size =         "8 pages",
  abstract =     "Presents method aiming to encourage reinforcement
                 learning to improve the way it learns",
  notes =        "part of rosca:1995:ml",
}

@TechReport{schmidhuber:1996:spm,
  author =       "Juergen Schmidhuber and Jieyu Zhao and Marco Wiering",
  title =        "Simple Principles of Metalearning",
  institution =  "IDSIA, Lugano, Switzerland",
  year =         "1996",
  type =         "Technical Report",
  number =       "IDSIA-69-96",
  address =      "Corso Elvezia 36, CH-6900, Switzerland",
  month =        jun # " 27",
  keywords =     "metalearning",
  URL =          "ftp://ftp.idsia.ch//pub/juergen/meta.ps.gz",
  abstract =     "The goal of metalearning is to generate useful shifts
                 of inductive bias by adapting the current learning
                 strategy in a {"}useful{"} way. Our learner leads a
                 single life during which actions are continually
                 executed according to the system's internal state and
                 current policy (a modifiable, probabilistic algorithm
                 mapping environmental inputs and internal states to
                 outputs and new internal states). An action is
                 considered a learning algorithm if it can modify the
                 policy. Effects of learning processes on later learning
                 processes are measured using reward/time ratios.
                 Occasional backtracking enforces success histories of
                 still valid policy modifications corresponding to
                 histories of lifelong reward accelerations. The
                 principle allows for plugging in a wide variety of
                 learning algorithms. In particular, it allows for
                 embedding the learner's policy modification strategy
                 within the policy itself (self-reference). To
                 demonstrate the principle's feasibility in cases where
                 traditional reinforcement learning fails, we test it in
                 complex, non-Markovian, changing environments
                 ({"}POMDPs{"}). One of the tasks involves more than
                 10^13 states, two learners that both cooperate and
                 compete, and strongly delayed reinforcement signals
                 (initially separated by more than 300,000 time
                 steps).",
  notes =        "Details to GP list on Wed, 24 Jul 1996 13:57:22
                 +0200",
  size =         "23 pages",
}

@InCollection{schmidhuber:1996:isinal,
  author =       "Juergen Schmidhuber",
  title =        "A General Method for Incremental Self-Improvement and
                 Multi-agent Learning in Unrestricted Environments",
  booktitle =    "Evolutionary Computation: Theory and Applications",
  publisher =    "Scientific Publishing Company",
  year =         "1996",
  editor =       "X. Yao",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.idsia.ch//pub/juergen/ec96.ps.gz",
  notes =        "Details to GP list on Wed, 24 Jul 1996 13:57:22 +0200
                 and www home page Based on Environment-independent
                 reinforcement acceleration, technical note IDSIA-59-95,
                 June 1995 (write-up of invited talk at Hongkong Univ.
                 ST, 70 K, can be questioned via NetQ ), and on On
                 learning how to learn learning strategies, TR
                 FKI-198-94, TUM, November, 1994; revised January, 1995
                 (81 K).",
  size =         "30 pages",
}

@InProceedings{zhao:1996:isiltmarl,
  author =       "Jieyu Zhao and Juergen Schmidhuber",
  title =        "Incremental Self-Improvement for Life-Time Multi-Agent
                 Reinforcement Learning",
  booktitle =    "Proceedings of the Fourth International Conference on
                 Simulation of Adaptive Behavior: From animals to
                 animats 4",
  year =         "1996",
  editor =       "Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer
                 and Jordan Pollack and Stewart W. Wilson",
  pages =        "516--525",
  address =      "Cape Code, USA",
  publisher_address = "Cambridge, MA, USA",
  month =        "9-13 " # sep,
  publisher =    "MIT Press",
  URL =          "ftp://ftp.idsia.ch//pub/jieyu/sab96.ps.gz",
  ISBN =         "0-262-63178-4",
  size =         "10 pages",
  notes =        "SAB-96 Details to GP list on Wed, 24 Jul 1996 13:57:22
                 +0200

                 A spin-off paper of the TR above
                 (schmidhuber:1996:spm?) It includes another experiment:
                 a multi-agent system consisting of 3 co-evolving,
                 IS-based animats chasing each other learns interesting,
                 stochastic predator and prey strategies.",
}

@InProceedings{wiering:1996:pomdps,
  author =       "Marco Wiering and Juergen Schmidhuber",
  title =        "Solving {POMDP}s with Levin search and {EIRA}",
  booktitle =    "Machine Learning: Procceedings of 13th International
                 Conference",
  year =         "1996",
  address =      "Bari, Italy",
  URL =          "ftp://ftp.idsia.ch//pub//pub/marco/ml_levin_eira.ps.gz",
  size =         "9 pages",
  notes =        "Details to GP list on Wed, 24 Jul 1996 13:57:22
                 +0200

                 To appear in Proc. ICML`96, 86 K, 252 K uncompressed.
                 Another spin-off paper of the TR
                 (schmidhuber:1996:spm?) above. It uses ``Levin's
                 universal search through program space (LS)''. LS is
                 theoretically `optimal' for a wide variety of search
                 problems including many partially observable Markov
                 decision problems (POMDPs). Experiments show that LS
                 can solve partially observable mazes (`POMS') involving
                 many more states and obstacles than those solved by
                 various previous authors. An adaptive extension of LS
                 (ALS) is introduced. ALS uses experience to increase
                 probabilities of instructions occurring in successful
                 programs found by LS. To deal with cases where ALS does
                 not lead to long term performance improvement, we use
                 the above-mentioned, novel paradigm (EIRA) to guarantee
                 lifelong histories of reward accelerations. We show:
                 (a) ALS can dramatically reduce the search time
                 consumed by successive calls of LS. (b) Additional
                 significant speedups can be obtained by combining ALS
                 with EIRA.",
}

@TechReport{schmidhuber:2002:TR12,
  author =       "Juergen Schmidhuber",
  title =        "Optimal Ordered Problem Solver",
  institution =  "IDSIA",
  year =         "2002",
  number =       "IDSIA-12-02",
  month =        "31 " # jul,
  keywords =     "genetic algorithms, genetic programming, OOPS,
                 bias-optimality, incremental optimal universal search,
                 metasearching, metalearning, self-improvement",
  URL =          "ftp://ftp.idsia.ch/pub/juergen/oops.ps.gz",
  abstract =     "We extend principles of non-incremental universal
                 search to build a novel, optimally fast, incremental
                 learner that is able to improve itself through
                 experience. The Optimal Ordered Problem Solver (OOPS)
                 searches for a universal algorithm that solves each
                 task in a sequence of tasks. It continually organises
                 and exploits previously found solutions to earlier
                 tasks, efficiently searching not only the space of
                 domain-specific algorithms, but also the space of
                 search algorithms.

                 The initial bias is embodied by a task-dependent
                 probability distribution on possible program prefixes
                 (pieces of code that may continue). Prefixes are
                 self-delimiting and executed in online fashion while
                 being generated. They compute the probabilities of
                 their own possible continuations. Let p^n denote a
                 found prefix solving the first n tasks. It may exploit
                 previous solutions p^i (i<n) stored in non-modifiable
                 memory by calling them as subprograms, or by copying
                 them into modifiable memory and editing the copies
                 before executing them. We provide equal resources for
                 two searches that run in parallel until p^{n+1} is
                 discovered and stored. The first search is exhaustive;
                 it systematically tests all possible prefixes on all
                 tasks up to n+1. The second search is much more
                 focused; it only searches for prefixes that start with
                 p^n, and only tests them on task n+1, which is safe,
                 because we already know that such prefixes solve all
                 tasks up to n. Both searches are depth-first and
                 bias-optimal: the branches of the search trees are
                 program prefixes, and backtracking is triggered once
                 the sum of the run times of the current prefix on all
                 current tasks exceeds the prefix probability multiplied
                 by the total search time so far.

                 In illustrative experiments, our self-improver becomes
                 the first general system that learns to solve all n
                 disk Towers of Hanoi tasks (solution size 2^n-1) for n
                 up to 30, profiting from previously solved, simpler
                 tasks involving samples of a simple context free
                 language.",
  notes =        "GP is a heuristic method with little or no theoretical
                 justification (http://www.idsia.ch/~juergen/gp.html);
                 most existing GP implementations do not even allow for
                 loops and recursion. The following method, however,
                 performs optimal incremental search in general program
                 space.
                 http://www.idsia.ch/~juergen/oops.html

                 arXiv:cs.AI/0207097 v1;",
  size =         "pages",
}

@InProceedings{schmiedle:2001:gecco,
  title =        "Priorities in Multi-Objective Optimization for Genetic
                 Programming",
  author =       "Frank Schmiedle and Nicole Drechsler and Daniel Grosse
                 and Rolf Drechsler",
  pages =        "129--136",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming,
                 multi-objective optimization, BDD minimization,
                 priorities in optimization",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{Schmiedle:2001:FD,
  author =       "Frank Schmiedle and Daniel Grosse and Rolf Drechsler
                 and Bernd Becker",
  title =        "Too Much Knowledge Hurts: Acceleration of Genetic
                 Programs for Learning Heuristics",
  booktitle =    "Computational Intelligence : Theory and Applications",
  year =         "2001",
  editor =       "Bernd Reusch",
  volume =       "2206",
  series =       "LNCS",
  pages =        "479--491",
  address =      "Dortmund, Germany",
  month =        "1-3 " # oct,
  organization = "7th Fuzzy Days",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-42732-5",
  size =         "pages",
  abstract =     "Among many other applications, evolutionary methods
                 have been used to develop heuristics for several
                 optimization problems in VLSI CAD in recent years.
                 Although learning is performed according to a set of
                 training benchmarks, it is most important to generate
                 heuristics that have a good generalization behaviour
                 and hence are well suited to be applied to unknown
                 examples. Besides large runtimes for learning, the
                 major drawback of these approaches is that they are
                 very sensitive to a variety of parameters for the
                 learning process.

                 In this paper, we study the impact of different
                 parameters, like e.g. stopping conditions, on the
                 quality of the results for learning heuristics for BDD
                 minimization. If learning takes too long, the developed
                 heuristics become too specific for the set of training
                 examples and in that case results of application to
                 unknown problem instances deteriorate. It will be
                 demonstrated here that runtime can be saved while even
                 improving the generalization behaviour of the
                 heuristics.",
  notes =        "http://ls1-www.cs.uni-dortmund.de/fd7/",
}

@Article{schmiedle:2002:GPEM,
  author =       "Frank Schmiedle and Nicole Drechsler and Daniel Grosse
                 and Rolf Drechsler",
  title =        "Heuristic Learning Based on Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "4",
  pages =        "363--388",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, heuristic
                 learning, multi-objective optimization, BDD
                 minimization, variable re-ordering",
  ISSN =         "1389-2576",
  abstract =     "In this paper we present an approach to learning
                 heuristics based on Genetic Programming (GP) which can
                 be applied to problems in the VLSI CAD area. GP is used
                 to develop a heuristic that is applied to the problem
                 instance instead of directly solving the problem by
                 application of GP. The GP-based heuristic learning
                 method is applied to one concrete field from the area
                 of VLSI CAD, i.e. minimisation of Binary Decision
                 Diagrams (BDDs). Experimental results are given in
                 order to demonstrate that the GP-based method leads to
                 high quality results that outperform previous methods
                 while the run-times of the resulting heuristics do not
                 increase. Furthermore, we show that by clever
                 adjustment of parameters, further improvements such as
                 the saving of about 50% of the run-time for the
                 learning phase can be achieved.",
  notes =        "Article ID: 5103874",
}

@MastersThesis{Schmutter:ms02,
  author =       "Peter Schmutter",
  title =        "Object-Oriented Ontogenetic Programming: Breeding
                 Computer Programs that Work Like Multicellular
                 Creatures",
  school =       "University of Dortmund, Germany",
  year =         "2002",
  type =         "Diploma thesis",
  month =        jun,
  email =        "email@schmutter.de",
  keywords =     "Object-Oriented Ontogenetic Programming, Genetic
                 Programming, Multicellular Programming,
                 Swarm-Programming, Evolutionof Distributed
                 Intelligence, Gene Regulation, Embryology, Amorphous
                 Computing, Multiagent Systems",
  URL =          "http://www.ooop.org/publications/thesis/",
  size =         "100 pages",
  abstract =     "As the research field called Genetic Programming has
                 shown during the last decade, it is possible not only
                 to write computer programs by hand but also to let the
                 computer itself develop programs that solve given
                 problems. This is achieved by simulating natural
                 evolution on the computer for {"}breeding{"} programs
                 that are well adapted to a specific problem
                 environment. The use of mechanisms found in nature can
                 lead to solutions to complex problems that by far
                 outperform any man-made approaches. The reasons are
                 that complex problems often are difficult to solve
                 analytically and many other possible approaches are not
                 accessible to the human way of thinking. The use of the
                 mechanisms of evolution based on genetic variation and
                 {"}survival of the fittest{"} is only one example.
                 Another example are Artificial Neural Networks that
                 imitate clusters of nervous cells and their
                 interactions for solving difficult problems (inspired
                 among others by the human brain).

                 The here presented work explores a different and new
                 approach to adopting problem solving methods found in
                 nature. It uses the natural cell control mechanism
                 called Gene Regulation that according to modern
                 molecular genetics is the basis of the cooperation
                 between and differentiation into all the different
                 cells in living creatures. The most astonishing example
                 of self-organization between simple units that
                 cooperate to solve complex problems is not the
                 interaction between nervous cells on the basis of
                 mutual electrical activation through explicit and
                 directed connections. It is the interaction between all
                 kinds of cells in a living creature which is based on
                 the diffusion of messages in the form of produced
                 substances. This interaction is much more powerful and
                 flexible than the neural interaction because of many
                 reasons. The main reason is, that a cell in this
                 context is not only a simple unit which can have
                 different levels of activation, but it is a complex
                 system with many behavioural possibilities. The
                 communication between the cells not only bases on
                 different activation intensities but on many different
                 message types which (also depending their intensity)
                 can have very sophisticated effects on the behaviour of
                 a cell.

                 This new programming and control paradigm has been
                 combined with genetic programming for breeding
                 {"}multicellular{"} programs (which probably is the
                 only feasible way of producing them). The system that
                 implements this combination can not only be used to
                 create programs with a new modular structure which has
                 several advantages. It also is a great tool for
                 developing systems of cooperating autonomous units like
                 Amorphous Computers and Multiagent Systems.",
}

@Proceedings{PPSN2000,
  title =        "Parallel Problem Solving from Nature - {PPSN} {VI} 6th
                 International Conference",
  year =         "2000",
  editor =       "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
                 Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
                 Merelo and Hans-Paul Schwefel",
  volume =       "1917",
  series =       "LNCS",
  address =      "Paris, France",
  month =        "16-20 " # sep,
  publisher =    "Springer Verlag",
  keywords =     "analysis and theory of elevolutionary algorithms,
                 genetic algorithms, genetic programming, scheduling,
                 representations and operators Co-evolution, constraint
                 handling techniques, noisy and non-stationary
                 environments, evolvable hardware and hardware
                 implementions of EAs, combinatorial optimisation,
                 machine learning and classifier systems, new algorithms
                 and mesaphors, multiobjective optimisation, EA
                 Software",
  ISBN =         "3-540-41056-2",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-41056-2",
  size =         "914 pages",
  notes =        "PPSN VI The Sixth International Conference on Parallel
                 Problem Solving from Nature Paris, Sept. 16-20 2000
                 http://www.inria.fr/ppsn2000",
}

@InProceedings{Schoenauer:2001:EUROGEN,
  author =       "Marc Schoenauer and Michele Sebag",
  title =        "Using Domain Knowledge in Evolutionary System
                 Identification",
  booktitle =    "Evolutionary Methods for Design, Optimization and
                 Control with Applications to Industrial Problems",
  year =         "2001",
  editor =       "K. C. Giannakoglou and D. Tsahalis and J. Periaux and
                 K. Papailiou and T. C. Fogarty",
  month =        "19-21 " # sep,
  address =      "Athens",
  keywords =     "genetic algorithms, genetic programming, inverse
                 problems",
  abstract =     "Two example of Evolutionary System Identification are
                 presented to highlight the importance of incorporating
                 Domain Knowledge: the discovery of an analytical
                 indentation law in Structural Mechanics using
                 constrained Genetic Programming, and the identification
                 of the repartition of underground velocities in Seismic
                 Prospection. Critical issues for successful ESI are
                 discussed in the light of these results.",
  notes =        "EUROGEN'2001
                 http://eurogen2001.ltt.mech.ntua.gr/eurogen/ invited
                 speaker

                 To be publihed with CIMNE, Barcelona, 2002 ????",
}

@InProceedings{scholoman:2001:gpehpcoimgs,
  author =       "John Scholoman and Benjamin Blackford",
  title =        "Genetic Programming Evolves a Human-Competitive Player
                 for a Complex, On-line, Interactive, Multi-Player Game
                 of Strategy",
  booktitle =    "Graduate Student Workshop",
  year =         "2001",
  editor =       "Conor Ryan",
  pages =        "441--444",
  address =      "San Francisco, California, USA",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming, Quake2",
  notes =        "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}

@Article{Schreittmiller:2002:fogp,
  author =       "Robert Schreittmiller",
  title =        "Langdon, {W}.{B}., Poli, {R}., Foundations of Genetic
                 Programming",
  journal =      "GSCI Digest",
  year =         "2002",
  volume =       "2",
  number =       "4",
  month =        "28 " # mar,
  email =        "scicomp@uni-erlangen.de",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.scicomp.uni-erlangen.de/letter/v02n04/b6",
  notes =        "GSCI is the newsletter of German Scientific Computing.
                 langdon:fogp",
}

@InProceedings{schulenburg:1999:A,
  author =       "Sonia Schulenburg and Peter Ross",
  title =        "An evolutionary approach to modelling the behaviours
                 of financial traders",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "245--253",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{schuschny:1997:soglei,
  author =       "Andres R. Schuschny and Roberto P. J. Perazzo and
                 Daniel Heymann",
  title =        "Self-organization Through Global and Local Exchange of
                 Information: {A} Schematic Model of Bank Runs",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "213--218",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670

                 agents",
}

@InProceedings{schwartz:1996:aim,
  author =       "Carey Schwartz and Charles Keyes and E. van
                 Bronkhorst",
  title =        "Application and Evaluation of Genetic Programming for
                 Aimpoint Selection",
  booktitle =    "Adaptive Computing: Mathematical and Physical Methods
                 for Complex Environments",
  year =         "1996",
  editor =       "H. John Caulfield and Su-Shing Chen",
  volume =       "2824",
  pages =        "191--200",
  address =      "Denver",
  month =        "4--5 " # aug,
  organisation = "SPIE",
  keywords =     "genetic algorithms, genetic programming, ANN, ID3,
                 C4.5",
  URL =          "http://www.spie.org/web/meetings/programs/dv96adv_2824.html",
  notes =        "[2824-30] Naval Air Warfare Centre, Chinalake, CA,
                 USA

                 Chip Keyes.

                 Compares three layer feedforward ANN with
                 backpropergation, C4.5 (a derivative of ID3) and GP on
                 a X-Y learning of 192 examples with 20 inputs on each.
                 No requirement for out-of-sample generalisation. Some
                 ANN and C4.5 were able to learn them all but {"}the
                 C4.5 solutions appear incapable of generalizing{"} (in
                 the presence of noise) and {"}The GP approach is
                 unsatisfactory for this application{"}. [p199]",
}

@InProceedings{searson:1998:cpcdGP,
  author =       "Dominic Searson and Mark Willis and Gary Montague",
  title =        "Chemical Process Controller Design Using Genetic
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "359--364",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@TechReport{sebag:1995:imGP1,
  author =       "Michele Sebag and Habibou Maitournam and Marc
                 Schoenauer",
  title =        "Identification of Mechanical Behaviour by Genetic
                 Programming Part {I}: {R}heological Formulation",
  institution =  "Ecole Polytechnique",
  year =         "1995",
  address =      "91128 Palaiseau, France",
  keywords =     "genetic algorithms, genetic programming, solid state
                 mechanical models, hybrid GP and local paramter
                 optimisation",
  abstract =     "Goal is to build general visco-elastic-plastic 1-D
                 models using GP and dynamic models. Splits deep
                 evolution (ie crossover) from surface evolution
                 (parameter adjustment using random process like
                 Evolution Strategies). Each deep evolution followed by
                 surface evolution.

                 Increasing the population size (80, 100 to 120) does
                 not neccessarily lead to better results.",
  notes =        "See also part 2 sebag:1995:imGP2

                 ",
  size =         "3 pages",
}

@TechReport{sebag:1995:imGP2,
  author =       "Marc Schoenauer and Bertrand Lamy and Francois Jouve",
  title =        "Identification of Mechanical Behaviour by Genetic
                 Programming Part {II}: {E}nergy Formulation",
  institution =  "Ecole Polytechnique",
  year =         "1995",
  address =      "91128 Palaiseau, France",
  keywords =     "genetic algorithms, genetic programming, solid state
                 mechanical models, hybrid GP and local paramter
                 optimisation",
  abstract =     "Goal is to build hyper-elastic 3-D models using statc
                 experiments. Focuses on discovering local energy
                 function using GP.

                 Splits deep evolution (ie crossover and mutation) from
                 surface evolution (parameter adjustment using random
                 process like Evolution Strategies). Uses generational
                 GPQuick. Crossover 0.3, mutation 0.5 and copy 0.2
                 Prepared to run to 1000 generations. Pop size
                 500.

                 Fitness based on closeness of match both of GP function
                 and also its derivative. (tentativly this seems to
                 increase both accuracy and robustness) However results
                 indicate GP is overfitting (due to single fitness
                 case?)",
  notes =        "See also part 2 sebag:1995:imGP2

                 ",
  size =         "3 pages",
}

@InCollection{schoenauer:1996:aigp2,
  author =       "Marc Schoenauer and Michele Sebag and Francois Jouve
                 and Bertrand Lamy and Habibou Maitournam",
  title =        "Evolutionary Identification of Macro-Mechanical
                 Models",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "467--488",
  chapter =      "23",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming, structural
                 mechanics",
  ISBN =         "0-262-01158-1",
  abstract =     "This chapter illustrates the potential of genetic
                 programming (GP) in the field of macro-mechanical
                 modeling, addressing the problem of identification of a
                 mechanical model for a material. Two kinds of models
                 are considered. One-dimensional dynamic models are
                 represented via symbolic formulations termed {\em
                 rheological models}, which are directly evolved by GP.
                 Three-dimensional static models of hyperelastic
                 materials are expressed in terms of strain energy
                 functions. A model is rated based on the distance
                 between the behavior predicted by the model, and the
                 actual behavior of the material given by a set of
                 mechanical experiments. The choice of GP is motivated
                 by strong arguments, relying on the tree-structure of
                 rheological models in the first case, and on the need
                 for first and second order derivatives in the second
                 case. Key issues are the exploration of viable
                 individuals only, and the use of Gaussian mutations to
                 optimize numerical constants.",
}

@InProceedings{SebSch97,
  author =       "M. Sebag and M. Schoenauer and H. Maitournam",
  title =        "Parametric and non-parametric identification of
                 macro-mechanical models",
  booktitle =    "Genetic Algorithms and Evolution Strategies in
                 Engineering and Computer Sciences",
  year =         "1997",
  editor =       "D. Quagliarella and J. Periaux and C. Poloni and G.
                 Winter",
  pages =        "327--340",
  publisher =    "John Wiley",
  organisation = "INGENET",
  ISBN =         "0-471-97710-1",
  URL =          "http://www.amazon.com/exec/obidos/ASIN/0471977101/qid%3D977328943/sr%3D1-1/002-5868922-2651240",
  keywords =     "genetic algorithms, genetic programming, strutural
                 mechanics",
  abstract =     "This paper presents evolutionary identification of a
                 particular form of behavioral laws for
                 elasto-visco-plastic materials termed rheological
                 models. First, rheologicalmodels with known graph of
                 connections are used, and the identification amounts to
                 identifying real-valued parameters. But the more
                 challenging task of identifying also thetopology of the
                 rheological model can be tackled using Genetic
                 Programming, after turning those models into
                 parse-trees. Both approaches are compared and discussed
                 ona simple artificial example.",
  notes =        "EUROGEN 1997",
}

@InProceedings{sebald:1998:mpef,
  author =       "A. V. Sebald and K. Chellapilla",
  title =        "On Making Problems Eolutionary Friendly",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "271--290",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "Evolutionary Programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
                 Comes as Part 1 and Part 2",
}

@InCollection{segal:1994:birds,
  author =       "Julie Segal",
  title =        "Concurrent Evolution of Territory Defining Behavior in
                 Birds Using Genetic Programming",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "120--129",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "2 branches

                 This volume contains 20 papers written and submitted by
                 students describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{Segapeli:1997:DGP,
  author =       "J-L Segapeli and Escazut C. and P. Collard",
  title =        "{DGP}: How to Improve Genetic Programming with
                 Doubles",
  booktitle =    "ICANNGA97",
  year =         "1997",
  address =      "University of East Anglia, Norwich, UK",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}

@InProceedings{Segovia:1997:GPahNNlr,
  author =       "Javier Segovia and Pedro Isasi",
  title =        "Genetic Programming For Designing Ad Hoc Neural
                 Network Learning Rules",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "303",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{Segovia:2000:GECCO,
  author =       "Sara Pozzi and Javier Segovia",
  title =        "Evaluations of Genetic Programming and Neural Networks
                 Techniques for Nuclear Material Identification",
  pages =        "590--596",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InCollection{seibulescu:1994:ismp,
  author =       "Alexandru Seibulescu",
  title =        "Instruction Scheduling on Multiprocessors Using a
                 Genetic Algorithm",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "130--139",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-187263-3",
  notes =        "{"}Representation: Binary string. Each bit respresents
                 the processor on which the corresponding node of the
                 graph is to be scheduled.{"} p135

                 This volume contains 20 papers written and submitted by
                 students describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@MastersThesis{self:thesis,
  author =       "Steven Self",
  title =        "On the Origin of Effective Procedures by Means of
                 Artificial Selection",
  school =       "Birkbeck College, University of London",
  address =      "UK",
  month =        Sep,
  year =         "1992",
  size =         "60 pages",
  keywords =     "genetic algorithms",
  notes =        "Applies GA to task of evolving simple Turing
                 Machines",
}

@InProceedings{semeraro:1998:earta,
  author =       "Greg P. Semeraro",
  title =        "Evolutionary Approach To Real-Time Analysis",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "592",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InCollection{sen:1998:DDASTUGP,
  author =       "Pradeep Sen",
  title =        "Designing Digital Adder Structures Through the Use of
                 Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1998",
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "167--176",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-212568-8",
  notes =        "part of koza:1998:GAGPs",
}

@InProceedings{senin:1999:ODMOGA,
  author =       "Nicola Senin and David R. Wallace and Nick Borland",
  title =        "Object-based Design Modeling and Optimization with
                 Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1715--1722",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{seok:2000:EH,
  author =       "Ho-Sik Seok and Kwang-Ju Lee and Byoung-Tak Zhang and
                 Dong-Wook Lee and Kwee-Bo Sim",
  title =        "Genetic Programming of Process Decomposition
                 Strategies for Evolvable Hardware",
  booktitle =    "Proceedings of the Second NASA / DoD Workshop on
                 Evolvable Hardware",
  year =         "2000",
  pages =        "43--52",
  address =      "Palo Alto, California",
  publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC,
                 20036-1992, USA",
  month =        "13-15 " # jul,
  organization = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-0762-X",
  URL =          "http://www.computer.org/proceedings/eh/0762/07620025abs.htm",
  URL =          "http://scai.snu.ac.kr/~hsseok/Seok.ps",
  abstract =     "Evolvable hardware is able to offer considerably
                 higher performance than general-purpose processors and
                 significantly more flexibility than ASICs. In order to
                 take the advantages of general-purpose processors and
                 ASICs, dividing a complex process into subprocesses is
                 essential. In this paper, we propose an evolutionary
                 method called context switching that splits a task into
                 a set of subtasks whose complexity is manageable on the
                 given hardware. The method is based on genetic
                 programming. Due to its expressive power, genetic
                 program can represent flexible strategies for
                 decomposing complex tasks. The effectiveness of context
                 switching is demonstrated on the design of adaptive
                 controllers for a team of autonomous mobile robots.",
  notes =        "EH-2000 XC6216. VCC HOT board. Two Khepera, one object
                 some obstacles. cannot map environment. context
                 switching allows process to be bigger than hardware.
                 Uses fitness switching of zhang:1998:fs:ecgbGP

                 ",
}

@InProceedings{seok:2000:arob,
  author =       "Ho-Sik Seok and Kwang-Ju Lee and Byoung-Tak Zhang",
  title =        "An On-Line Learning Method for Object-Locating Robots
                 using Genetic Programming on Evolvable Hardware",
  booktitle =    "International Symposium on Artificial Life and
                 Robotics",
  year =         "2000",
  editor =       "Masanori Sugisaka",
  pages =        "321--324",
  address =      "Oita, Japan",
  month =        "26-28 " # jan,
  organisation = "International Society for Artificial Life and Robotics
                 (ISAROB)",
  keywords =     "genetic algorithms, genetic programming, FPGA",
  URL =          "http://scai.snu.ac.kr/~hsseok/arob.ps",
  size =         "4 pages",
  notes =        "AROB-00",
}

@InProceedings{seok:2001:CEC,
  author =       "Ho-Sik Seok and Byoung-Tak Zhang",
  title =        "Evolutionary Calibration of Sensors using Genetic
                 Programming on Evolvable Hardware",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "630--634",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, sensor
                 calibration, evolvable hardware, mobile robot,
                 Khepera",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 GP tree converted to bit string down loaded onto XC6216
                 FPGA",
}

@InProceedings{seredynski:1999:dsdpcGPa,
  author =       "Franciszek Seredynski and Jacek Koronacki and Cezary
                 Z. Janikow",
  title =        "Distributed Scheduling with Decomposed Optimization
                 Criterion:Genetic Programming Approach",
  booktitle =    "Parallel and Distributed Processing",
  year =         "1999",
  editor =       "Jose Rolim et al.",
  volume =       "1586",
  series =       "Lecture Notes in Computer Science",
  pages =        "192--200",
  address =      "San Juan, Puerto Rico, USA",
  month =        "12-16 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65831-9",
  notes =        "11 IPPS/SPDP'99 Workshops Held in Conjunction with the
                 13th International Parallel Processing Symposium and
                 10th Symposium on Parallel and Distributed
                 Processing,

                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-65831-9

                 multi-agent systems, coevolution, game theory, loosely
                 coupled genetic algorithms LCGA

                 ",
}

@InProceedings{seredynski:1999:DCASA,
  author =       "Franciszek Seredynski and Cezary Z. Janikow",
  title =        "Designing Cellular Automata-based Scheduling
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "587--594",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{Seredyski:2001:FGCS,
  author =       "F. Seredyski and J. Koronacki and C. Z. Janikow",
  title =        "Distributed multiprocessor scheduling with decomposed
                 optimization criterion",
  journal =      "Future Generation Computer Systems",
  volume =       "17",
  pages =        "387--396",
  year =         "2001",
  number =       "4",
  keywords =     "genetic algorithms, genetic programming,
                 Multiprocessor scheduling, Multi-agent systems,
                 Decomposition of optimization criterion",
  URL =          "http://www.sciencedirect.com/science/article/B6V06-4234BR7-6/1/9ac251ca310222336e096ca3ecf27e22",
  abstract =     "n this paper, a new approach to scheduling of parallel
                 and distributed algorithms for multiprocessor systems
                 is proposed. Its main innovation lies in evolving a
                 decomposition of the global optimization criteria. For
                 this purpose, agents {"}local decision making units{"}
                 are associated with individual tasks of the program
                 graph. Thus, the program can be interpreted as a
                 multi-agent system. A game-theoretic model of
                 interaction between agents is applied. Agents take part
                 in an iterated game to find directions of migration in
                 the system graph, with the objective of minimizing the
                 total execution time of the program in a given
                 multiprocessor topology. Competitive coevolutionary
                 genetic algorithm, termed loosely coupled genetic
                 algorithm, is used to implement the multi-agent system.
                 The scheduling algorithm works with a global
                 optimization function, what limits its efficiency. To
                 make the algorithm truly distributed, decomposition of
                 the global optimization criterion into local criteria
                 is proposed. This decomposition is evolved with genetic
                 programming. Results of successive experimental study
                 of the proposed algorithm are presented.",
}

@InProceedings{seront:1995:crGP,
  author =       "Gregory Seront",
  title =        "External Concepts Reuse in Genetic Programming",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "94--98",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP, {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@MastersThesis{setzkorn:masters,
  author =       "Christian Setzkorn",
  title =        "Investigation into the Application of Artificial
                 Intelligence Methods to the Analysis of Medical Data",
  school =       "Computer Science Department, University of Liverpool",
  year =         "2000",
  address =      "Peach Street, Liverpool L69 7ZF",
  month =        jan,
  email =        "C.Setzkorn@csc.liv.ac.uk, CSetzkorn@gmx.net",
  keywords =     "genetic algorithms, genetic programming, data mining",
  URL =          "http://www.csc.liv.ac.uk/~chris/Thesis_Setzkorn_Online.zip",
  size =         "219 pages",
  abstract =     "Two methods from the field of artificial intelligence
                 were implemented and employed on a medical data set, in
                 order to perform data mining. The data set consisted of
                 cases from patients who suffered recurring miscarriage,
                 and the aim was to investigate whether the implemented
                 methods were able to identify previously unknown
                 factors associated with recurrent miscarriage. The
                 first approach used a specific type of artificial
                 neural network - Kohonen's self-organizing map for
                 performing clustering within data sets. By using new
                 cluster detection methods and the visualisation
                 possibilities of the employed programming language
                 Java, and its graphical user interface components
                 Swing, it allows interactively the visualisation of
                 relationships within a data set. The second, relatively
                 unique approach, infers rules from a data set by using
                 the paradigm of genetic programming. The rules consist
                 of an IF-part (antecedent) and a THEN-part
                 (consequent). The system has to be supplied with the
                 consequent and works out antecedents, which describe
                 the sub data set indicated by the consequent within the
                 supplied data set. The antecedents produced take the
                 form of a tree where Boolean operations AND, OR and NOT
                 represent nodes, and Boolean expressions represent the
                 leaves. Boolean expressions can be built from all types
                 of data including free-text and real numbers. This
                 system was also implemented with Java and offers in
                 addition the possibility of knowledge extraction from
                 clusters built by the self-organizing map approach.",
  notes =        "My master thesis concerns (apart of other things) rule
                 inference from a medical data set by using GP (Data
                 Mining). The rules consist of an IF-part (antecedent)
                 and a THEN-part (consequent). The system has to be
                 supplied with the consequent and works out antecedents,
                 which describe the sub data set indicated by the
                 consequent within the supplied data set. The
                 antecedents produced take the form of a tree where
                 Boolean operations AND, OR and NOT represent nodes, and
                 Boolean expressions represent the leaves. Boolean
                 expressions can be built from all types of data
                 including free-text and real numbers (AGE<=35,
                 DiseaseXYZ = yes, bloo_valueX = abnormal values). This
                 system was implemented with Java and offers in addition
                 the possibility of knowledge extraction from clusters
                 built by a self-organizing map approach (also
                 implemented during this thesis).",
}

@Article{shaaban:2001:GPEM,
  author =       "N. Shaaban and S. Hasegawa and A. Suzuki and H.
                 Takahashi",
  title =        "The Use of Genetic Algorithms for the Improvement of
                 Energy Characteristics of {CdZnTe} Semiconductor
                 Detectors",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "3",
  pages =        "289--299",
  month =        sep,
  email =        "noha@qs.t.u-tokyo.ac.jp",
  keywords =     "genetic algorithms, semiconductor detectors, charge
                 loss, energy spectrum, EGS4 software",
  ISSN =         "1389-2576",
  abstract =     "A new charge loss correction method using genetic
                 algorithms (GA) has been proposed to improve gamma ray
                 energy spectrum characteristics of CdZnTe detectors.
                 The correction method is based on the analysis of
                 signal waveform shapes taking into account the
                 contribution of multiple interaction processes to pulse
                 shape generation. A GA recognizes the charge deposition
                 places for each signal and provides the related
                 corrective factors of the pulse heights; the corrected
                 pulse height spectrum was obtained by summing up the
                 corrected pulse heights for each signal. An enhanced
                 energy spectrum characteristic was obtained after the
                 correction process for 662 keV photons. This method is
                 simple and useful for pulse shape analysis; the results
                 demonstrate promise for the successful application of
                 GAs for digital signal processing data analysis.",
}

@Article{shackleford:2001:gam,
  author =       "Barry Shackleford and Greg Snider and Richard J.
                 Carter and Etsuko Okushi and Mitsuhiro Yasuda and
                 Katsuhiko Seo and Hiroto Yasuura",
  title =        "A High-Performance, Pipelined, {FPGA}-Based Genetic
                 Algorithm Machine",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "1",
  pages =        "33--60",
  month =        mar,
  keywords =     "genetic algorithms, evolvable hardware, genetic
                 algorithm processor, reconfigurable-computing, FPGA",
  ISSN =         "1389-2576",
  abstract =     "Accelerating a genetic algorithm (GA) by implementing
                 it in a reconfigurable field programmable gate array
                 (FPGA) is described. The implemented GA features:
                 random parent selection, which conserves selection
                 circuitry; a steady-state memory model, which conserves
                 chip area; survival of fitter child chromosomes over
                 their less-fit parent chromosomes, which promotes
                 evolution. A net child chromosome generation rate of
                 one per clock cycle is obtained by pipelining the
                 parent selection, crossover, mutation, and fitness
                 evaluation functions. Complex fitness functions can be
                 further pipelined to maintain a high-speed clock cycle.
                 Fitness functions with a pipeline initiation interval
                 of greater than one can be plurally implemented to
                 maintain a net evaluated-chromosome throughput of one
                 per clock cycle. Two prototypes are described: The
                 first prototype (c. 1996 technology) is a multiple-FPGA
                 chip implementation, running at a 1 MHz clock rate,
                 that solves a 94-row times 520-column set covering
                 problem 2,200 times faster than a 100 MHz workstation
                 running the same algorithm in C. The second prototype
                 (Xilinx XVC300) is a single-FPGA chip implementation,
                 running at a 66 MHZ clock rate, that solves a
                 36-residue protein folding problem in a 2-d lattice 320
                 times faster than a 366 MHz Pentium II. The current
                 largest FPGA (Xilinx XCV3200E) has circuitry available
                 for the implementation of 30 fitness function units
                 which would yield an acceleration of 9,600 times for
                 the 36-residue protein folding problem.",
}

@Article{shami:1997:ftrr,
  author =       "S. H. Shami and I. M. A. Kirkwood and M. C. Sinclair",
  title =        "Evolving Simple Fault-tolerant Routing Rules using
                 Genetic Programming",
  journal =      "Electronics Letters",
  year =         "1997",
  volume =       "33",
  number =       "17",
  pages =        "1440--1441",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming,
                 telecommunications networks, routing",
  abstract =     "A novel approach to solving network routing and
                 restoration problems using the genetic programming (GP)
                 paradigm is presented, in which a single robust and
                 fault-tolerant program is evolved which determines the
                 near-shortest paths through a network subject to link
                 failures.",
  email =        "mcs@essex.ac.uk",
  notes =        "Letter. Multiple populations uni-direction ring and
                 injection both with 3 subpopulations. Dijkstra fitness.
                 lilgp.

                 {"}GP has some limited potential, for small networks,
                 to evolve near-optimal fault-tolerant routing rules
                 which are robust enough to be able to solve a high
                 proportion of multiple link failures. Overall, though,
                 this approach lacks adequate performance even for
                 modest-sized networks.{"}",
}

@InProceedings{shanahan:1999:CIFCGFMGP,
  author =       "James G. Shanahan and James F. Baldwin and Trevor P.
                 Martin",
  title =        "Constructive Induction of Fuzzy Cartesian Granule
                 Feature Models using Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1237",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{shanahan:1999:CIFCGFMGPA,
  author =       "James G. Shanahan and James F. Baldwin and Trevor P.
                 Martin",
  title =        "Constructive Induction of Fuzzy Cartesian Granule
                 Feature Models using Genetic Programming with
                 Applications",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "218--226",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, learning",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{Sharif:1997:femgGA,
  author =       "Amir Sharif and Robert Ettinger",
  title =        "Finite Element Mesh Generation using Genetic
                 Algorithms",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "219--223",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{sharif:1998:sgpmoe,
  author =       "Amir M. Sharif and Anthony N. Barrett",
  title =        "Seeding a genetic population for mesh optimisation and
                 evaluation",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{sharman:1993:gessm,
  author =       "Ken C. Sharman and Anna I. Esparcia-Alcazar",
  title =        "Genetic Evolution of Symbolic Signal Models",
  booktitle =    "Proceedings of the Second International Conference on
                 Natural Algorithms in Signal Processing, NASP'93",
  year =         "1993",
  address =      "Essex University, UK",
  month =        "15-16 " # nov,
  organisation = "IEE",
  email =        "ken@music.gla.ac.uk, anna@music.gla.ac.uk",
  keywords =     "genetic programming, simulated annealing, adaptive
                 signal processing",
  URL =          "http://www.iti.upv.es/~anna/papers/natalg93.ps",
  abstract =     "This paper reports on a novel method of signal
                 modelling that employs a variable model structure as
                 opposed to the fixed model structure used in
                 conventional methods. The functional form of the model
                 along with any required numerical parameters are
                 simultaneously estimated from the signal sequence to be
                 modelled. This is accomplished by defining the model
                 functional and its parameters in terms of structured
                 lists of symbols, and using an estimation algorithm
                 that can infer symbol lists from the given data.
                 Motivated by the recent work in cellular coding and
                 evolutionary computation, we use Genetic Programming
                 (GP) to evolve high quality model structures. This is
                 based on a coding of the model in terms of an
                 expression tree in polish form which can then be
                 manipulated and optimised using standard Genetic
                 algorithm (GA) techniques. In conjunction with the
                 model structure evolution, we use Simulated Annealing
                 to optimise the numerical parameters of the model and a
                 set of production rules to minimise the model order.
                 The paper discusses how these three processes can be
                 combined to yield a powerful general purpose modelling
                 system",
  notes =        "Recursion Section 2.2.5",
}

@InProceedings{sharman:1995:espa,
  author =       "Ken C. Sharman and Anna I. {Esparcia Alcazar} and Yun
                 Li",
  title =        "Evolving Signal Processing Algorithms by Genetic
                 Programming",
  booktitle =    "First International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1995",
  editor =       "A. M. S. Zalzala",
  volume =       "414",
  pages =        "473--480",
  address =      "Sheffield, UK",
  publisher_address = "London, UK",
  month =        "12-14 " # sep,
  publisher =    "IEE",
  keywords =     "genetic algorithms, genetic programming, simulated
                 annealing, adaptive signal processing, neural networks,
                 memory",
  ISBN =         "0-85296-650-4",
  URL =          "http://www.iti.upv.es/~anna/papers/galesi95.ps",
  abstract =     "We introduce a novel Genetic Programming (GP)
                 technique to evolve both the structure and parameters
                 of adaptive Digital Signal Processing (DSP) algorithms.
                 This is accomplished by defining a set of node
                 functions and terminals to implement the basic
                 operations commonly used in a large class of DSP
                 algorithms. In addition, we show how Simulated
                 Annealing may be employed to assist the GP in
                 optimising the numerical parameters of expression
                 trees. The concepts are illustrated by using GP to
                 evolve high performance algorithms for detecting binary
                 data sequences at the output of a noisy, non-linear
                 communications channel.",
  notes =        "12--14 September 1995, Halifax Hall, University of
                 Sheffield, UK see also
                 http://www.iee.org.uk/LSboard/Conf/program/galprog.htm

                 Use simulated annealing to assist GP optimise numerical
                 componets of expression trees.

                 DSP uses push and pop (any point in stack stkN) in
                 function set

                 Previous outputs (Yn) used as inputs - {"}time
                 recursion{"}. Single sample delay node Z. {"}infinitley
                 many different tree structures for a particular system
                 function{"}

                 {"}Time varying fitness function{"} Sigmoidal transfer
                 function with beta evolable.

                 Pop 250, tournament 10, max gene 100, mutation, 2 ADFs,
                 Evolved filter has zero bit error rate!

                 {"}Perliminary results...GP significantly out performed
                 existing systems.{"}

                 GP computationaly expensive",
}

@InProceedings{sharpe:1998:bNFLts,
  author =       "Oliver Sharpe",
  title =        "Beyond {NFL}: {A} few tentative steps",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "593--600",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  URL =          "http://www.cogs.susx.ac.uk/users/olivers/BNFL_Camera_Ready.ps",
  size =         "8 pages",
  notes =        "SGA-98 Hill climbing, mutaton and uniform crossover
                 compared on three abstract landscapes.",
}

@InProceedings{sharpe:1998:cfdc,
  author =       "Oliver Sharpe",
  title =        "Concerns with Fitness Distance Correlations",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "601",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  URL =          "http://www.cogs.susx.ac.uk/users/olivers/poster.ps",
  notes =        "SGA-98",
}

@InProceedings{sharpe:1999:CBND,
  author =       "Oliver Sharpe",
  title =        "Continuing Beyond {NFL}: Dissecting real world
                 problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "595--602",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{sharpe:1999:PSA,
  author =       "Oliver Sharpe",
  title =        "Persistence, Search and Autopoiesis",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1374--1381",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{shelton:1995:GAAML,
  author =       "Christian R. Shelton",
  title =        "Genetic Algorithms Applied to Machine Language",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "249--258",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{Sherrah:1997:afeGP,
  author =       "Jamie Sherrah",
  title =        "Automatic Feature Extraction using Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "298",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@InProceedings{Sherrah:1997:epafxsc,
  author =       "Jamie R. Sherrah and Robert E. Bogner and Abdesselam
                 Bouzerdoum",
  title =        "The Evolutionary Pre-Processor: Automatic Feature
                 Extraction for Supervised Classification using Genetic
                 Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "304--312",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@PhdThesis{sherrah:1998:thesis,
  author =       "Jamie Sherrah",
  title =        "Automatic Feature Extraction for Pattern Recognition",
  school =       "University of Adelaide, South Australia",
  year =         "1998",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dcs.qmw.ac.uk/~jamie/research.html",
  size =         "pages",
  abstract =     "A typical pattern recognition system consists of two
                 stages: the pre-processing stage to extract features
                 from the data, and the classification stage to assign
                 the feature vector to one of several classes. While
                 many general classifiers exist and are well-understood,
                 the pre-processing stage is usually ad-hoc and designed
                 by hand. Although the accuracy of the classifier is
                 heavily dependent on the choice of features, there is
                 little more guidance in the process of manual feature
                 extraction than intuition, experience and
                 trial-and-error.

                 To achieve automatic and near-optimal pre-processor
                 design, a framework is required for the
                 problem-independent extraction of features. Within such
                 a framework, the concept of an optimal pre-processor
                 can be formulated. The framework must allow
                 pre-processors which are universally applicable and
                 realisable using finite resources. Those frameworks
                 already in existence, such as principal-component
                 analysis and multi-layer perceptrons, are either unable
                 to cope with arbitrary non-linearity or unable to be
                 implemented using finite resources because they employ
                 one type of constituent function and have a fixed
                 structure.

                 In this thesis, a framework for automatic feature
                 extraction is proposed, called the {"}generalised
                 pre-processor{"}. This is an arbitrarily-interconnected
                 feed-forward network with arbitrary non-linear
                 functions at the nodes. The use of different
                 constituent functions and irregular inter-connection
                 strategies allows for the economic realisation of a
                 pre-processor in more situations than the more uniform
                 universal approximators, such as the multi-layer
                 perceptron. A software system called the
                 {"}Evolutionary Pre-Processor{"} is presented which
                 performs a search over the space of generalised
                 pre-processors. The system is used for supervised
                 classification, and must be provided with a data set of
                 measurement vectors and associated class labels. Based
                 on genetic programming, the evolutionary pre-processor
                 begins with a population of randomly-generated
                 pre-processors. The fitness of each pre-processor is
                 based on the estimated misclassification cost of a
                 classifier trained on the pre-processed data. Through
                 fitness-proportionate reproduction and recombination,
                 the ability of the pre-processors to separate the data
                 increases with generations.

                 The evolutionary pre-processor has been tested on 15
                 real and synthetic public-domain data sets. Neural
                 networks, decision trees and five simple statistical
                 classification techniques were applied to the same
                 problems, and the results compared. The results show
                 that the evolutionary pre-processor maintains good
                 classification and generalisation performance, and is
                 more accurate on average than the decision tree method.
                 The neural network achieved the lowest classification
                 errors on average, but was surpassed by the
                 evolutionary pre-processor on some synthetic problems.
                 Both the evolutionary pre-processor and the decision
                 tree produce solutions which can be understood and
                 interpreted by the user. These results must be
                 considered with care, however, as they fluctuate with
                 different random seeds and partitioning of the data.
                 The investigations of this thesis have revealed that a
                 search over pre-processors is feasible. The synthesis
                 of pre-processors from a variety of non-linear, and
                 even discontinuous functions occasionally provides
                 better discrimination than existing methods of
                 classification, but for most problems gradient-descent
                 methods are adequate. The evolutionary pre-processor
                 has advantages for knowledge discovery due to the
                 versatility with which appropriate functions can be
                 combined, but is limited due to the high variability in
                 results. It should be used in conjunction with other
                 methods of knowledge discovery for reliable results.
                 The evolved pre-processors and simple classifiers used
                 by EPrep result in relatively accurate classification
                 systems that can be implemented more economically than
                 other methods.",
}

@InProceedings{LeyuanShi:1998:hGA,
  author =       "Leyuan Shi and Sigurdur Olafsson",
  title =        "A New Hybrid Genetic Algorithm",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Article{shih:2001:ISJ,
  author =       "Timothy K. Shih",
  title =        "Mobile agent evolution computing",
  journal =      "Information Sciences",
  year =         "2001",
  volume =       "137",
  number =       "1-4",
  pages =        "53--73",
  email =        "tshih@cs.tku.edu.tw",
  keywords =     "Internet multimedia, Web services, Mobile network
                 architectures",
  ISSN =         "0020-0255",
  URL =          "http://www.elsevier.com/gej-ng/10/23/143/90/25/29/abstract.html",
  abstract =     "The ecosystem is an evolutionary result of natural
                 laws. Food web (or food chain) embeds a set of
                 computation rules of natural balance. Based on the
                 concepts of food web, one of the laws that we may learn
                 from the natural besides neural networks and genetic
                 algorithms, we propose a theoretical computation model
                 for mobile-agent evolution on the Internet. We define
                 an agent niche overlap graph and agent evolution
                 states. We also propose a set of algorithms, which is
                 used in our multimedia search programs, to simulate
                 agent evolution. Agents are cloned to live on a remote
                 host station based on three different strategies: the
                 brute force strategy, the semi-brute force strategy,
                 and the selective strategy. Evaluations of different
                 strategies are discussed. Guidelines of writing
                 mobile-agent programs are proposed. The technique can
                 be used in distributed information retrieval which
                 allows the computation load to be added to servers, but
                 significantly reduces the traffic of network
                 communication. In the literature of software agents, it
                 is hard to find other similar models. The results of
                 this research only address a small portion of the ice
                 field. We hope that this problem would be further
                 studied in the societies of network communications,
                 multimedia information retrieval, and intelligent
                 systems on the Internet.",
  notes =        "Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}

@InProceedings{shimada:1999:FRCCCGONF,
  author =       "Sotaro Shimada and Yuichiro Anzai",
  title =        "Fast and Robust Convergence of Chained Classifiers by
                 Generating Operons through Niche Formation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "810",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{shimodaira:1999:ADCOGADER,
  author =       "Hisashi Shimodaira",
  title =        "A Diversity Control Oriented Genetic Algorithm
                 ({DCGA}): Development and Experimental Results",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "603--611",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Shimooka:1998:geGPcmip,
  author =       "Hiroaki Shimooka and Yoshiji Fujimoto",
  title =        "Generating Equations with Genetic Programming for
                 Control of a Movable Inverted Pendulum",
  booktitle =    "Second Asia-Pacific Conference on Simulated Evolution
                 and Learning",
  year =         "1998",
  editor =       "Charles Newton",
  address =      "Australian Defence Force Academy, Canberra,
                 Australia",
  month =        "24-27 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "SEAL'98 Possible publication in springer-verlag LNAI
                 series SEAL98#AP11",
}

@InProceedings{Shimooka:2000:GECCO,
  author =       "Hiroaki Shimooka and Yoshiji Fujimoto",
  title =        "Generating Robust Control Equations with Genetic
                 Programming for Control of a Rolling Inverted
                 Pendulum",
  pages =        "491--495",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{shiraishi:2002:gecco:lbp,
  title =        "The Basic Study of Artificial Ecosystem Models Using
                 Network-Type Assembly-Like Language",
  author =       "Yuhki Shiraishi and Kotaro Hirasawa and Jinglu Hu and
                 Junichi Murata",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "412--418",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming, alife",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp",
}

@InProceedings{shirasaka:1998:adbdtGP,
  author =       "Mitsuyoshi Shirasaka and Qiangfu Zhao and Omar Hammami
                 and Kenichi Kuroda and Kazuyuki Saito",
  title =        "Automatic Design of Binary Decision Trees Based on
                 Genetic Programming",
  booktitle =    "Second Asia-Pacific Conference on Simulated Evolution
                 and Learning",
  year =         "1998",
  editor =       "Charles Newton",
  address =      "Australian Defence Force Academy, Canberra,
                 Australia",
  month =        "24-27 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "SEAL'98 Possible publication in springer-verlag LNAI
                 series, SEAL98#036",
}

@InProceedings{shyu+foster:2002:gecco:workshop,
  title =        "Evolutionary Approach for Inferring Phylogenetic
                 Trees",
  author =       "Conrad Shyu and James.A. Foster",
  pages =        "45--47",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{shyu:2002:gecco:workshop,
  title =        "Evolutionary Approach for Inferring Phylogenetic
                 Trees",
  author =       "Conrad Shyu and James A. Foster",
  pages =        "300--303",
  booktitle =    "Graduate Student Workshop",
  editor =       "Sean Luke and Conor Ryan and Una-May O'Reilly",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop

                 see also shyu+foster:2002:GECCO:workshop",
}

@InCollection{kinnear:siegel,
  title =        "Competitively Evolving Decision Trees Against Fixed
                 Training Cases for Natural Language Processing",
  author =       "Eric V. Siegel",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "19",
  pages =        "409--423",
  size =         "15 pages",
  keywords =     "genetic algorithms, genetic programming,
                 co-evolution",
  abstract =     "Competitive fitness functions can generate performance
                 superior to absolute fitness functions [Angeline and
                 Pollack 1993], [Hillis 1992]. This chapter describes a
                 method by which competition can be implemented when
                 training over a fixed (static) set of examples. Since
                 new training cases cannot be generated by mutation or
                 crossover, the probabilistic frequencies by which
                 individual training cases are selected competitively
                 adapt. We evolve decision trees for the problem of word
                 sense disambiguation. The decision trees contain
                 embedded bit strings; bit string crossover is
                 intermingled with subtree-swapping. To approach the
                 problem of overlearning, we have implemented a fitness
                 penalty function specialized for decision trees which
                 is dependent on the partition of the set of training
                 cases implied by a decision tree.",
  notes =        "Not a GP but uses a mixture of strings and trees as an
                 interpretable data structure for making a single choice
                 from two alternatives. Gives training cases a fitness
                 and choices by tournament the most difficult tests.
                 arbitrary restriction on tree to prevent learning test
                 cases rather than general principles. See also
                 siegel2",
}

@InProceedings{siegel2,
  key =          "Siegel",
  author =       "E. V. Siegel and K. R. McKeown",
  title =        "Emergent linguistic rules from inducing decision
                 trees: disambiguating discourse clue words",
  year =         "1994",
  booktitle =    "Proceedings of the Twelfth National Conference on
                 Artificial Intelligence",
  month =        jul,
  publisher =    "AAAI Press",
  address =      "Menlo Park, CA, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.columbia.edu/~evs/papers/aaai94.ps",
  notes =        "

                 report on the same work in which decision tree choice
                 point feature sets are bitstrings, but overall tree is
                 a tree. Subtree swapping and onepoint bitstring
                 crossovers are both used. See also kinnear:siegel",
  abstract =     "We apply decision tree induction to the problem of
                 discourse clue word sense disambiguation. The automatic
                 partitioning of the training set which is intrinsic to
                 decision tree induction gives rise to linguistically
                 viable rules.",
}

@TechReport{siegel:1995:aaai-fgp,
  author =       "Eric V. Siegel and John R. Koza",
  title =        "Working notes {AAAI}-95 Fall Symposium Series Genetic
                 Programming",
  institution =  "The American Association for Artificial Intelligence",
  year =         "1995",
  type =         "Technical Report",
  number =       "FS-95-01",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  note =         "Held at MIT, Cambridge, MA, USA, 10--12 November
                 1995",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.columbia.edu/~evs/gpsym95/working.html",
  url_2 =        "http://www.aaai.org/Press/Reports/Symposia/Fall/fs-95-01.html",
  ISBN =         "0-929280-92-x",
  notes =        "Collection of 19 papers, {\em Telephone:} 415-328-3123
                 {\em Fax:} 415-321-4457 {\em email} info@aaai.org {\em
                 URL:} http://www.aaai.org/ AAAI Fall 1995 Symposia held
                 in MIT, Cambridge, MA, USA",
  size =         "140 pages",
}

@Misc{siegel:1995:aaai-fgpWWW,
  key =          "Eric V. Siegel",
  editor =       "Eric V. Siegel",
  title =        "Collective Brainstorming at the {AAAI} Symposium on
                 Genetic Programming",
  howpublished = "www page",
  year =         "1996",
  month =        Jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.columbia.edu/~evs/gpsym95.html",
  abstract =     "This is an archive of the collective brainstorming
                 undertaken by the creative participants of the AAAI
                 Symposium on Genetic Programming at MIT nov 10-12
                 1995",
  notes =        "proceedings are siegel:1995:aaai-fgp",
}

@InProceedings{siegel:1996:gsacsGA,
  author =       "Eric V. Siegel and Kathleen R. McKeown",
  title =        "Gathering statistics to aspectually classify sentences
                 with a genetic algorithm",
  booktitle =    "Proceedings of the Second International Conference on
                 New Methods in Language Processing",
  year =         "1996",
  address =      "Bilkent University, Ankara, Turkey",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.columbia.edu/~evs/papers/nemlap.ps",
  abstract =     "This paper presents a method for large corpus analysis
                 to semantically classify an entire clause. In
                 particular, we use cooccurrence statistics among
                 similar clauses to determine the aspectual class of an
                 input clause. The process examines linguistic features
                 of clauses that are relevant to aspectual
                 classification. A genetic algorithm determines what
                 combinations of linguistic features to use for this
                 task.",
}

@InCollection{siegel:1996:aigp2,
  author =       "Eric V. Siegel and Alexander D. Chaffee",
  title =        "Genetically Optimizing the Speed of Programs Evolved
                 to Play Tetris",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "279--298",
  chapter =      "14",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "Many new domains for genetic programming require
                 evolved programs to be executed for longer amounts of
                 time. For these applications it is likely that some
                 test cases optimally require more computation cycles
                 than others. Therefore, programs must dynamically
                 allocate cycles among test cases in order to use
                 computation time efficiently. To elicit the strategic
                 allocation of computation time, we impose an {\it
                 aggregate computation time ceiling} that applies over a
                 series of fitness cases. This exerts time pressure on
                 evolved programs, with the effect that resulting
                 programs dynamically allocate computation time,
                 opportunistically spending less time per test case when
                 possible, with minimal damage to domain performance.
                 This technique is in principle extensible to resources
                 other than computation time such as memory or fuel. We
                 introduce the game Tetris as a test problem for this
                 technique.",
}

@InProceedings{siegel:1997:lmclicv,
  author =       "Eric V. Siegel",
  title =        "Learning methods for combining linguistic indicators
                 to classify verbs",
  booktitle =    "Proceedings of the Second Conference on Empirical
                 Methods in Natural Language Processing",
  year =         "1997",
  address =      "Providence, RI, USA",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.columbia.edu/~evs/papers/stativity.ps",
  abstract =     "Fourteen linguistically-motivated numerical indicators
                 are evaluated for their ability to categorize verbs as
                 either states or events. The values for each indicator
                 are computed automatically across a corpus of text. To
                 improve classification performance, machine learning
                 techniques are employed to combine multiple indicators.
                 Three machine learning methods are compared for this
                 task: decision tree induction, a genetic algorithm, and
                 log-linear regression.",
}

@PhdThesis{siegel:thesis,
  author =       "Eric Siegel",
  title =        "Linguistic Indicators for Language Understanding:
                 Using Machine Learning Methods to Combine Corpus-Based
                 Indicators for Aspectual Classification of Clauses.",
  school =       "Computer Science Department. Columbia University",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.columbia.edu/~evs/papers/thesis.ps",
  size =         "pages",
  abstract =     "Linguistics as a field has provided enormous insights
                 that describe how the thoughts behind language are
                 reflected by the structure of sentences. For example,
                 one writes a paper in one week, but rides a bicycle for
                 one hour. This illustrates how prepositions (in and
                 for) correspond to the type of event. Specifically, in
                 modifies a completed process, while for modifies an
                 ongoing process. The area explored by this thesis is,
                 how can we best put our understanding of linguistics to
                 use in order to tap into the vast knowledge encoded in
                 texts?

                 The ability to distinguish stative clauses, e.g., ``She
                 resembles her mother,'' from event clauses, e.g., ``She
                 ran down the street,'' is a fundamental component of
                 natural language understanding. These two high-level
                 categories correspond to primitive distinctions in many
                 domains, including, for example, the distinctions
                 between diagnosis and procedure in the medical domain.
                 Stativity is the first of three high-level distinctions
                 that compose the aspectual class of a clause. These
                 distinctions in meaning have been well motivated by
                 work in linguistics and natural language
                 understanding.

                 Aspectual classification is a necessary component for
                 applications that perform certain natural language
                 interpretation, natural language generation,
                 summarization, information retrieval, and machine
                 translation tasks. This is because each of these
                 applications requires the ability to reason about
                 time.

                 In this thesis, I develop a system to perform aspectual
                 classification with linguistically-based, numerical
                 indicators. These linguistic indicators make use of an
                 array of aspectual markers, each of which has an
                 associated constraint on aspectual class. For example,
                 only clauses that describe an event can appear with the
                 progressive marker, e.g., ``I was eating breakfast.''
                 Therefore, the category of a verb or phrase is
                 reflected by a numerical indicator that measures how
                 often it occurs in the progressive. The values for such
                 linguistic indicators are computed automatically across
                 corpora of text. We develop and evaluate fourteen
                 indicators over unrestricted sets of verbs occurring
                 across two corpora. Our analysis reveals a predictive
                 value for several indicators that have not previously
                 been conjectured to correlate with aspect in the
                 linguistics literature.

                 Then, machine learning is used to combine multiple
                 indicators in order to improve classification
                 performance. The models automatically derived by
                 learning are manually examined, revealing several
                 linguistic insights regarding the indicators and their
                 interactions. Three machine learning techniques are
                 compared for this task: decision tree induction, a
                 genetic algorithm, and log-linear regression.

                 We conclude that linguistic indicators successfully
                 exploit linguistic insights to provide a much-needed
                 method for aspectual classification. Future work will
                 extend this approach to other semantic distinctions in
                 natural language.",
}

@InProceedings{silva:1999:ecaaGPn,
  author =       "Arlindo Silva and Ana Neves and Ernesto Costa",
  title =        "Evolving Controllers for Autonomous Agents Using
                 Genetically Programmed Networks",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "255--269",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP

                 Santa Fe ant",
}

@InProceedings{silva:1999:GPNEMM,
  author =       "Arlindo Silva and Ana Neves and Ernesto Costa",
  title =        "Genetically Programming Networks to Evolve Memory
                 Mechanisms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1448",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, artificial
                 life, adaptive behavior and agents, poster papers",
  ISBN =         "1-55860-611-4",
  abstract =     "ant, tartarus, multi-tree individuals",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{silva:1999:BAMAAGPN,
  author =       "Arlindo Silva and Ana Neves and Ernesto Costa",
  title =        "Building Agents with Memory: An Approach using
                 Genetically Programmed Networks",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "3",
  pages =        "1824--1840",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, route and
                 network planning",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{SilvaPPSN2000,
  author =       "A. Silva and A. Neves and E. Costa",
  title =        "Polymorphy and Hybridization in Genetically Programmed
                 Networks",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VI 6th
                 International Conference",
  editor =       "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
                 Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
                 Merelo and Hans-Paul Schwefel",
  year =         "2000",
  publisher =    "Springer Verlag",
  address =      "Paris, France",
  month =        "16-20 " # sep,
  note =         "LNCS 1917",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{simoes:1999:TCAES,
  author =       "Anabela Borges Simoes and Ernesto Costa",
  title =        "Transposition versus Crossover: An Empirical Study",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "612--619",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@TechReport{Sims91,
  author =       "K. Sims",
  title =        "Artificial Evolution for Computer Graphics",
  institution =  "Thinking Machines Corporation",
  number =       "TR-185",
  year =         "1991",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Published in ACM, Computer Graphics, Vol. 25, No. 4
                 (July 1991) see also sims91a",
}

@Article{sims91a,
  author =       "Karl Sims",
  title =        "Artificial evolution for computer graphics",
  pages =        "319--328",
  journal =      "ACM Computer Graphics",
  volume =       "25",
  number =       "4",
  year =         "1991",
  month =        jul,
  editor =       "Thomas W. Sederberg",
  conference =   "held in Las Vegas, Nevada; 28 July - 2 August 1991",
  note =         "SIGGRAPH '91 Proceedings",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "see also Sims91 Evolving 3Dee Plant Structures.
                 Discusses using fixed GA chromosome then goes to using
                 s-expressions. 7 Types of mutation. Technique used to
                 create sims:panspermia",
}

@InCollection{sims:panspermia,
  author =       "K. Sims",
  title =        "panspermia",
  booktitle =    "Artificial Life II Video Proceedings",
  publisher =    "Addison-Wesley",
  year =         "1991",
  editor =       "Christopher G. Langton",
  address =      "Santa Fe Institute, New Mexico, USA",
  month =        feb # " 1990",
  notes =        "

                 Also in Siggraph Video Review 1990?

                 ",
  keywords =     "genetic algorithms",
}

@InProceedings{Sims:1994:ieds,
  author =       "Karl Sims",
  title =        "Interactive evolution of dynamical systems",
  booktitle =    "Toward a Practice of Autonomous Systems: Proceedings
                 of the First European Conference on Artificial Life",
  year =         "1992",
  editor =       "Francisco J. Varela and Paul Bourgine",
  pages =        "171--178",
  address =      "Paris, France",
  month =        "11-13 " # dec,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming, cellular
                 automata, parallel running, connection machine",
  notes =        "ECAL-91 Interactive evolition of artistic images.
                 Discusses drawback of using GA to define CA state
                 transistion tables. Second half, discrete CA states are
                 replaced by one or more continious variables, whose
                 initial value and rate of change are controlled by
                 differential equations (which may depend upon the cells
                 state and that of its neighbours). Both initial value
                 and rate of change are given by a lisp s-expressions. 5
                 different types of mutation. {"}estimates of
                 computation times are made, and slow expressions are
                 automatically eliminated before bing used.{"} Crossover
                 not clear: may be different from Koza, exchaning only a
                 single node between expressions rather than subtrees.
                 Also when mated both the s-expression controlling the
                 initial state and the rate of change are crossed over.
                 Some runs use complex (ie i,j) rather than real
                 numbers. Run on connection machine, one virtual
                 processor per cell. 256*256 arrays processed at
                 interactive rates. Mutations and crossovers performed
                 in a front end machine. Genotypes evolved
                 (interactively) {"}in timescales such as 10
                 minutes{"}",
}

@InProceedings{Sims:1992:ieepm,
  author =       "K. Sims",
  title =        "Interactive evolution of equations for procedural
                 models",
  booktitle =    "Proceedings of IMAGINA conference, Monte Carlo,
                 January 29-31, 1992",
  year =         "1992",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "see Sims:1993:ieepm",
}

@Article{Sims:1993:ieepm,
  author =       "K. Sims",
  title =        "Interactive evolution of equations for procedural
                 models",
  journal =      "The Visual Computer",
  year =         "1993",
  volume =       "9",
  pages =        "466--476",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Publisher Springer-Verlag. like Sims:1994:ieds Add 3rd
                 Z variable to each cell to give volume textures or time
                 to give animation sequences. Volumes also produced
                 using primatives that give 3dee coordinates. Differnt
                 ways of visualising 3dee are described. Library of
                 previously evolved s-expressions may be (interactively)
                 re-used. {"}allows the user and computer to work
                 together interactively in a new way to produce results
                 that niether could easily produce alone.{"} {"}A
                 version of this paper was previously published{"}... as
                 Sims:1992:ieepm

                 7 different types of mutation described {"}It is
                 preferable to adjust the mutation frequencies such that
                 a decrease in complexity is slightly more probable than
                 an increase. This prevents the expressions drifting
                 towards large and slow forms without necessarily
                 improving the results.{"} [page 469]",
}

@Misc{Sims:1993:ei,
  author =       "K. Sims",
  title =        "Evolving Images",
  howpublished = "Lecture",
  year =         "1993",
  month =        mar,
  note =         "Lecture presented at Centre George Pompidou, Paris on
                 March 4, 1993. Notebook. Number 5.",
  size =         "pages",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{sinclair:1997:essa,
  author =       "M. C. Sinclair and S. H. Shami",
  title =        "Evolving Simple Software Agents: Comparing Genetic
                 Algorithm and Genetic Programming Performance",
  booktitle =    "Second International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1997",
  address =      "University of Strathclyde, Glasgow, UK",
  publisher_address = "London, UK",
  publisher =    "IEE",
  email =        "mcs@essex.ac.uk",
  keywords =     "genetic algorithms, genetic programming, software
                 agents",
  abstract =     "This paper investigates the relative efficiency of
                 genetic algorithms and genetic programming in evolving
                 simple software agents. The problem domain consists of
                 an autonomous food-gathering agent placed on a square
                 grid of hundred cells with food units spread evenly
                 over the grid. Initial results show that evolving the
                 agent using GP requires less effort than with GA.
                 Nevertheless, further investigation revealed some
                 interesting aspects.",
  notes =        "http://www.iee.org.uk/Conf/GALESIA/conf.htm",
}

@InProceedings{sinclair:1999:OMNTDNEGP,
  author =       "Mark C. Sinclair",
  title =        "Optical Mesh Network Topology Design using Node-Pair
                 Encoding Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1192--1197",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{singh:1995:AECPSATSP,
  author =       "Sanjeev Singh",
  title =        "An Empirical Comparison of 3 Population-Based Search
                 Algorithms for the Traveling Salesman Problem",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "259--268",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@Article{singleton:byte,
  author =       "Andy Singleton",
  title =        "Genetic Programming with {C}++",
  journal =      "BYTE",
  year =         "1994",
  pages =        "171--176",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.byte.com/art/9402/sec10/art1.htm",
  size =         "4 pages",
  notes =        "Source code available for DOS...page 5!",
}

@Unpublished{icga93-gp:singleton,
  author =       "Andrew Singleton",
  title =        "Meta {GA}, Desktop Supercomputing and
                 Object-Orientated {GP}",
  note =         "Notes from Genetic Programming Workshop at ICGA-93

                 ",
  year =         "1993",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/ICGA-93-GP-Abstracts.ps.Z",
  size =         "1 pages",
}

@InCollection{singleton:2000:TSHEMDJO,
  author =       "Todd Singleton",
  title =        "The Scavenger Hunt: Evolving the Mobility of
                 Distributed Java Objects",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "351--359",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{Sipper:1997:POE,
  author =       "Moshe Sipper and Eduardo Sanchez and Daniel Mange and
                 Marco Tomassini and Andres Perez-Uribe and Andre
                 Stauffer",
  title =        "The {POE} Model of Bio-Inspired Hardware Systems: {A}
                 Short Introduction",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Evolvable Hardware",
  pages =        "510",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@Article{Sipper:2001:sciam,
  author =       "Moshe Sipper and James A. Reggia",
  title =        "Go forth and Replicate",
  journal =      "Scientific American",
  year =         "2001",
  volume =       "265",
  number =       "2",
  pages =        "27--35",
  keywords =     "Alife, cellular automata",
}

@InProceedings{sivathasan:1998:ECGcsANN,
  author =       "S. Sivathasan and W. Balachandran and F. Cecelja",
  title =        "{ECG} Classifier System Using Artificial Neural
                 Networks",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{Sivrikaya-Serifoglu:1996:uobx,
  author =       "Funda Sivrikaya-Serifoglu and Gunduz Ulusoy",
  title =        "A New Uniform Order-Based Crossover Operator for
                 Multi-Component Combinatorial Optimization Problems",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "160--166",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, MCUOX",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{Slavov:1997:fliGP,
  author =       "Vanyo Slavov and Nikolay I. Nikolaev",
  title =        "Fitness Landscapes and Inductive Genetic Programming",
  booktitle =    "ICANNGA97",
  year =         "1997",
  address =      "University of East Anglia, Norwich, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.aubg.bg/faculty/cs/nikolaev/papers/icannga97.ps.gz",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html

                 GPDT investigates fitness landscape using mutation as
                 genetic operator and calculates fitness distance
                 correlation, fitness autocorrelation and fitness
                 correlation length for random walks. This analysis
                 prompts improved choices for system components such as
                 mutation rate.",
}

@InProceedings{Slavov:1997:iGPsfl,
  author =       "Vanio Slavov and Nikolay I. Nikolaev",
  title =        "Inductive Genetic Programming and Superposition of
                 Fitness Landscapes",
  booktitle =    "Genetic Algorithms: Proceedings of the Seventh
                 International Conference",
  year =         "1997",
  editor =       "Thomas Back",
  pages =        "97--104",
  address =      "Michigan State University, East Lansing, MI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "19-23 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-487-1",
  URL =          "http://www.aubg.bg/faculty/cs/nikolaev/papers/icga97.ps.gz",
  size =         "8 pages",
  abstract =     "This paper presents an approach to improving the
                 performance of evolutionary algorithms. The
                 evolutionary search effort is distributed among
                 cooperating subpopulations that correspond to the
                 substructures of the fitness landscape. The idea is to
                 create such subpopulations that flow easily on the
                 simple substructures of the complex fitness landscape
                 structure. We claim that the search on a complex
                 fitness landscape is facilitated if properly integrated
                 with search on its simple components. This evolutionary
                 structured search is applied for solving hard inductive
                 learning tasks. The performance observed while inducing
                 regular grammars from sets of boolean strings
                 demonstrated that the approach mitigates the search
                 difficulties.",
  notes =        "ICGA-97",
}

@InCollection{smilak:1999:FUVPPGP,
  author =       "Kevin C. Smilak",
  title =        "Finding the Ultimate Video Poker Player using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "209--217",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InCollection{smith:1994:npga:mdc,
  author =       "Brian Smith",
  title =        "A New Paradigm for Genetic Algorithms:
                 Multidimensional Chromosomes",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "140--149",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@Article{JamesSmith:2002:GPEM,
  author =       "James Smith",
  title =        "On Appropriate Adaptation Levels for the Learning of
                 Gene Linkage",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "2",
  pages =        "129--155",
  month =        jun,
  keywords =     "gene linkage, recombination, adaptive,
                 self-adaptation",
  ISSN =         "1389-2576",
  abstract =     "A number of algorithms have been proposed aimed at
                 tackling the problem of learning Gene Linkage within
                 the context of genetic optimisation, that is to say,
                 the problem of learning which groups of co-adapted
                 genes should be inherited together during the
                 recombination process. These may be seen within a wider
                 context as a search for appropriate relations which
                 delineate the search space and guide heuristic
                 optimisation, or, alternatively, as a part of a
                 comprehensive body of work into Adaptive Evolutionary
                 Algorithms.

                 In this paper, we consider the learning of Gene Linkage
                 as an emergent property of adaptive recombination
                 operators. This is in contrast to the behaviour
                 observed with fixed recombination strategies in which
                 there is no correspondence between the sets of genes
                 which are inherited together between generations, other
                 than that caused by distributional bias. A discrete
                 mathematical model of Gene Linkage is introduced, and
                 the common families of recombination operators, along
                 with some well known linkage-learning algorithms, are
                 modelled within this framework. This model naturally
                 leads to the specification of a recombination operator
                 that explicitly operates on sets of linked
                 genes.

                 Variants of that algorithm, are then used to examine
                 one of the important concepts from the study of
                 adaptivity in Evolutionary Algorithms, namely that of
                 the level (population, individual, or component) at
                 which learning takes place. This is an aspect of
                 adaptation which has received considerable attention
                 when applied to mutation operators, but which has been
                 paid little attention in the context of adaptive
                 recombination operators and linkage learning. It is
                 shown that even with the problem restricted to learning
                 adjacent linkage, the population based variants are not
                 capable of correctly identifying building blocks. This
                 is in contrast to component level adaptation which
                 outperforms conventional operators whose bias is ideal
                 for the problems considered.",
  notes =        "Special issue on Gene Expression Kargupta:2002:GPEM",
}

@InProceedings{smith:cbifgr,
  author =       "Peter Smith",
  title =        "Conjugation -- {A} Bacterially Inspired Form of
                 Genetic Recombination",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "167--176",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{PWHSmith:1998:iraf,
  author =       "Peter W. H. Smith",
  title =        "Introns and The Replication Accuracy Force",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Article{PWHSmith:1998:cgediass,
  author =       "Peter W. H. Smith and Kim Harries",
  title =        "Code Growth, Explicitly Defined Introns, and
                 Alternative Selection Schemes",
  journal =      "Evolutionary Computation",
  year =         "1998",
  volume =       "6",
  number =       "4",
  pages =        "339--360",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming, introns,
                 bloat, parsimony, fitness selection, linear encoding",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=6&issue=4",
  abstract =     "Previous work on introns and code growth in genetic
                 programming is expanded on and tested experimentally.
                 Explicitly defined introns are introduced to tree-based
                 representations as an aid to measuring and evaluating
                 intron behavior. Although it is shown that introns do
                 create code growth, they are not its only cause.
                 Removing introns merely decreases the growth rate; it
                 does not eliminate it. By systematically negating
                 various forms of intron behavior, a deeper
                 understanding of the causes of code growth is obtained,
                 leading to the development of a system that keeps
                 unnecessary bloat to a minimum. Alternative selection
                 schemes and recombination operators are examined and
                 improvements demonstrated over the standard selection
                 methods in terms of both performance and parsimony.",
  notes =        "Special Issue: Variable-Length Representation and
                 Noncoding Segments for Evolutionary Algorithms Edited
                 by Annie S. Wu and Wolfgang Banzhaf Early version
                 available as harries:1998:cgediass",
}

@InProceedings{PWHSmith:2000:ccgGP,
  author =       "P. W. H. Smith",
  title =        "Controlling Code Growth in Genetic Programming",
  booktitle =    "Advances in Soft Computing",
  year =         "2000",
  editor =       "Robert John and Ralph Birkenhead",
  pages =        "166--171",
  address =      "De Montfort University, Leicester, UK",
  publisher =    "Physica-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-7908-1257-9",
  URL =          "http://www.springer-ny.com/detail.tpl?ISBN=3790812579",
  notes =        "Published in 2000",
}

@InProceedings{smith:1997:rGP2pevltmdv,
  author =       "Tim Smith and M. Bartley and T. C. Fogarty",
  title =        "Microprocessor Design Verification by Two-Phase
                 Evolution of Variable Length Tests",
  booktitle =    "Proceedings of the 1997 {IEEE} International
                 Conference on Evolutionary Computation",
  year =         "1997",
  pages =        "453--458",
  address =      "Indianapolis",
  publisher_address = "Piscataway, NJ, USA",
  month =        "13-16 " # apr,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ICEC-97",
}

@InProceedings{smith:1999:AAGPTISO,
  author =       "Charles E. Smith",
  title =        "An Application of Genetic Programming To Investment
                 System Optimization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1798",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{someya:1999:AGAPTAFDP,
  author =       "Hiroshi Someya and Masayuki Yamamura",
  title =        "A Genetic Algorithm without Parameters Tuning and its
                 Application on the Floorplan Design Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "620--627",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{somorjai:1995:ccd,
  author =       "Ray L. Somorjai and Alexander E. Nikulin and Nic Pizzi
                 and Dick Jackson and Gordon Scarth and Brion Dolenko
                 and Heather Gordon and Peter Russell and Cynthia L.
                 Lean amd Leigh Delbridge and Carolyn E. Mountford and
                 Ian C. P. Smith",
  title =        "Computerized Consensus Diagnosis: {A} Classification
                 Strategy for the Robust Analysis of {MR} spectra. {I}.
                 Application to {1H} Spectra of Thyroid Neoplasms",
  journal =      "Magnetic Resonance Medicine",
  year =         "1995",
  volume =       "33",
  number =       "2",
  pages =        "257--263",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, computerised
                 consensus diagnosis, robust classification, thyroid
                 neoplasms, proton magnetic resonance spectrum, LDA,
                 ANN, GEPPETTO",
  ISSN =         "0740-3194",
  URL =          "http://www.interscience.wiley.com/jpages/0740-3194/",
  URL =          "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=7707918&dopt=Abstract",
  abstract =     "We introduce and apply a new classification strategy
                 we call computerized consensus diagnosis (CCD). Its
                 purpose is to provide robust, reliable classification
                 of biomedical data. The strategy involves the
                 cross-validated training of several classifiers of
                 diverse conceptual and methodological origin on the
                 same data, and appropriately combining their outcomes.
                 The strategy is tested on proton magnetic resonance
                 spectra of human thyroid biopsies, which are
                 successfully allocated to normal or carcinoma classes.
                 We used Linear Discriminant Analysis, a Neural
                 Net-based method, and Genetic Programming as
                 independent classifiers on two spectral regions, and
                 chose the median of the six classification outcomes as
                 the consensus. This procedure yielded 100% specificity
                 and 100% sensitivity on the training sets, and 100%
                 specificity and 98% sensitivity on samples of known
                 malignancy in the test sets. We discuss the necessary
                 steps any classification approach must take to
                 guarantee reliability, and stress the importance of
                 fuzziness and undecidability in robust
                 classification.",
  notes =        "PMID: 7707918 [PubMed - indexed for MEDLINE] consensus
                 means taking 2 of 3 vote from the three different
                 classifiers (GP, LDA and NN).",
}

@InProceedings{song:1999:ABIENNCA,
  author =       "Geum-Beom Song and Sung-Bae Cho",
  title =        "Adaptive Behavior of Incrementally Evolved Neural
                 Networks based on Cellular Automata",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1449",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{song:2002:tcgbgp,
  author =       "Andy Song and Vic Ciesielski and Hugh Williams",
  title =        "Texture Classifiers Generated by Genetic Programming",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "243--248",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{soule:1996:cgGP,
  author =       "Terence Soule and James A. Foster and John Dickinson",
  title =        "Code Growth in Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "215--223",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{soule:1996:GPamc,
  author =       "Terence Soule and James A. Foster and John Dickinson",
  title =        "Using Genetic Programming to Approximate Maximum
                 Clique",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "400--405",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{Soule:1997:csdfGP,
  author =       "Terence Soule and James A. Foster",
  title =        "Code Size and Depth Flows in Genetic Programming",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "313--320",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@Unpublished{soule:1997:smccg,
  author =       "Terence Soule and James A. Foster",
  title =        "Support for Multiple Causes of Code Growth in {GP}",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 variable size representation",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "3 pages",
}

@InProceedings{soule:1998:rbias,
  author =       "Terence Soule and James A. Foster",
  title =        "Removal Bias: a New Cause of Code Growth in Tree Based
                 Evolutionary Programming",
  booktitle =    "1998 IEEE International Conference on Evolutionary
                 Computation",
  year =         "1998",
  pages =        "781--186",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  organisation = "IEEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, parsimony,
                 removal bias, Code growth, variable length
                 representations",
  file =         "c136.pdf",
  size =         "6 pages",
  abstract =     "This paper presents a new cause of code growth, termed
                 removal bias. We show that growth due to removal bias
                 can be expected to occur whenever operations which
                 remove and replace a variable sized section of code,
                 e.g. crossover or subtree mutation, are used in an
                 evolutionary paradigm. Two forms of non-destructive
                 crossover are used to examine the causes of code
                 growth. Results support the protective value of
                 inviable code and removal bias as two distinct causes
                 of code growth. Both causes of code growth are shown to
                 exist in at least two different problems.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

@PhdThesis{soule:thesis,
  author =       "Terence Soule",
  title =        "Code Growth in Genetic Programming",
  school =       "University of Idaho",
  year =         "1998",
  address =      "Moscow, Idaho, USA",
  month =        "15 " # may,
  keywords =     "genetic algorithms, genetic programming, bloat",
  URL =          "ftp://ftp.cs.uidaho.edu/pub/foster/papers/soule-thesis.ps.gz",
  size =         "67 pages",
}

@Article{soule:1998:gp97,
  author =       "Terence Soule",
  title =        "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference on Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "1997",
  volume =       "1",
  number =       "4",
  pages =        "294--295",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  size =         "1.5 pages",
  notes =        "Review of koza:gp97 and mentions koza:gp97lb briefly",
}

@Article{soule:1998:ecgpppGP,
  author =       "Terence Soule and James A. Foster",
  title =        "Effects of Code Growth and Parsimony Pressure on
                 Populations in Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "1998",
  volume =       "6",
  number =       "4",
  pages =        "293--309",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming, Code growth,
                 code bloat, parsimony, introns",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=6&issue=4",
  abstract =     "Parsimony pressure, the explicit penalization of
                 larger programs, has been increasingly used as a means
                 of controlling code growth in genetic programming.
                 However, in many cases parsimony pressure degrades the
                 performance of the genetic program. In this paper we
                 show that poor average results with parsimony pressure
                 are a result of {"}failed{"} populations that
                 overshadow the results of populations that incorporate
                 parsimony pressure successfully. Additionally, we show
                 that the effect of parsimony pressure can be measured
                 by calculating the relationship between program size
                 and performance within the population. This measure can
                 be used as a partial indicator of success or failure
                 for individual populations.",
  notes =        "Special Issue: Variable-Length Representation and
                 Noncoding Segments for Evolutionary Algorithms Edited
                 by Annie S. Wu and Wolfgang Banzhaf",
}

@InProceedings{soule:1999:VTA,
  author =       "Terence Soule",
  title =        "Voting Teams: {A} cooperative approach to non-typical
                 problems using genetic programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "916--922",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, evolution
                 strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  abstract =     "even-7-parity XOR replaces NOR, population 500, 5
                 trees per individual, crossover like ADFs, size
                 secondary component of fitness, team's answer is
                 majority vote of five trees with in it. Robust to
                 crossover?

                 Performance of tree poor near random but whole team
                 good (NB don't solve even-7-parity). Teams (slightly)
                 smaller than one tree approach.",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{Soule:2000:GECCO,
  author =       "Terence Soule",
  title =        "Heterogeneity and Specialization in Evolving Teams",
  pages =        "778--785",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{soule2:2001:gecco,
  title =        "Function Sets in Genetic Programming",
  author =       "Terence Soule and Robert B. Heckendorn",
  pages =        "190",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster,
                 Representations, Function Sets",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{soule:2002:EuroGP,
  title =        "Exons and Code Growth in Genetic Programming",
  author =       "Terence Soule",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "142--151",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "The phenomenon of code growth is well documented in
                 genetic programming. Several well supported theories
                 exist to explain code growth, each of which focuses on
                 introns, sections of code that do not contribute to
                 fitness. However, several researchers have pointed out
                 that these theories, and code growth itself, does not
                 seem to depend upon the presence of introns. In this
                 paper we show for the first time that code growth can
                 occur, albeit quite slowly, even with exons that have a
                 significant impact on fitness.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@Article{soule:2002:GPEM,
  author =       "Terence Soule and Robert B. Heckendorn",
  title =        "An Analysis of the Causes of Code Growth in Genetic
                 Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "3",
  pages =        "283--309",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, code growth,
                 code bloat, crossover",
  ISSN =         "1389-2576",
  abstract =     "This research examines the cause of code growth
                 (bloat) in genetic programming (GP). Currently there
                 are three hypothesised causes of code growth in GP:
                 protection, drift, and removal bias. We show that
                 single node mutations increase code growth in evolving
                 programs. This is strong evidence that the protective
                 hypothesis is correct. We also show a negative
                 correlation between the size of the branch removed
                 during crossover and the resulting change in fitness,
                 but a much weaker correlation for added branches. These
                 results support the removal bias hypothesis, but seem
                 to refute the drift hypothesis. Our results also
                 suggest that there are serious disadvantages to the
                 tree structured programs commonly evolved with GP,
                 because the nodes near the root are effectively fixed
                 in the very early generations.",
  notes =        "Article ID: 5091792",
}

@InProceedings{soute:2001:ugpflf,
  author =       "I. A. C. Soute and M. J. G. {van de Molengraft} and G.
                 Z. Angelis",
  title =        "Using Genetic Programming to Find Lyapunov Functions",
  booktitle =    "Graduate Student Workshop",
  year =         "2001",
  editor =       "Conor Ryan",
  pages =        "449--452",
  address =      "San Francisco, California, USA",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}

@Article{souza:1999:LRA,
  author =       "Paulo A. {de Souza Jr.}",
  title =        "Automation in {M}össbauer Spectroscopy Data Analysis",
  journal =      "Laboratory Robotics and Automation",
  year =         "1999",
  volume =       "11",
  number =       "1",
  pages =        "3--23",
  email =        "souza@iacgu7.chemie.uni-mainz.de",
  keywords =     "genetic algorithms, neural networks, fuzzy logic,
                 Mössbauer spectroscopy",
  ISSN =         "08957533",
  URL =          "www.interscience.wiley.com",
  abstract =     "The present article reviews the main progress in
                 automation of Mössbauer spectroscopy data analysis by
                 using genetic algorithms, fuzzy logic, and artificial
                 neural networks. Several tests were carried out, and
                 the results are presented and discussed.",
}

@Article{souza:1998:HI,
  author =       "Paulo A. {de Souza Jr.}",
  title =        "Advances in {M}össbauer Data Analysis",
  journal =      "Hyperfine Interactions",
  year =         "1998",
  volume =       "113",
  pages =        "383--390",
  email =        "souza@iacgu7.chemie.uni-mainz.de",
  keywords =     "genetic algorithms, data analysis, fitting routine,
                 Mössbauer spectroscopy",
  ISSN =         "0304-3843",
  URL =          "http://www.baltzer.nl/oasis.htm/328096",
  abstract =     "In the present report we propose the autmoation of
                 least square fitting of Mössbauer spectra, the
                 identification of the substance, its crystal structure
                 and the access to the references with the help of a
                 genetic algorithm, fuzzy logic, and the artificial
                 neural network associated with a databank of Mössbauer
                 parameters and references. This system could be useful
                 for specialists and non-specialists, in industry as
                 well as in research laboratories.",
}

@InProceedings{souza:1997:MCPL,
  author =       "Paulo A. {de Souza Jr.}",
  title =        "Air Pollution Monitoring using Genetic Algorithm,
                 Fuzzy Logic and Neural Networks",
  booktitle =    "IFAC/IFIP Conference on Management and Control of
                 Production and Logistics",
  year =         "1997",
  pages =        "763--766",
  organization = "IFAC/IFIP",
  publisher =    "IFAC",
  note =         "?DOUBLE CHECK?",
  email =        "souza@iacgu7.chemie.uni-mainz.de",
  URL =          "http://www.elsevier.nl/inca/publications/store/6/0/0/4/8/1/index.htt",
  keywords =     "genetic algorithms, air pollution, Mössbauer
                 spectroscopy, fuzzy logic, neural networks",
  ISBN =         "0-08-043036-8",
  abstract =     "The present study envolves the automation of air
                 pollution monitoring using genetic algorithm, fuzzy
                 logic, neural networks of data from nuclear techniques
                 analysis of industrial waste and suspended particles in
                 air int he metropolitan region of Vitória, Espírito
                 Santo state, Brazil.",
  notes =        "MCPL'97 Mossbauer",
}

@Article{souza:1998:HIb,
  author =       "Paulo A. {de Souza Jr.} and R. Garg and Vijayendra
                 Kumar Garg",
  title =        "Automation of the analysis of {M}össbauer spectra",
  journal =      "Hyperfine Interactions",
  year =         "1998",
  volume =       "112",
  pages =        "275--278",
  email =        "souza@iacgu7.chemie.uni-mainz.de",
  keywords =     "genetic algorithms, neural networks, fuzzy logic,
                 Mössbauer spectroscopy",
  ISSN =         "0304-3843",
  abstract =     "In the present report we propose the autmoation of
                 least square fitting of Mössbauer spectra, the
                 identification of the substance, its crystal structure
                 and the access to the references with the help of a
                 genetic algorithm, fuzzy logic, and the artificial
                 neural network associated with a databank of Mössbauer
                 parameters and references. This system could be useful
                 for specialists and non-specialists, in industry as
                 well as in research laboratories.",
}

@InProceedings{spalanzani:1999:EAOSDP,
  author =       "A. Spalanzani and S. A. Selouani and H. Kabre",
  title =        "Evolutionary Algorithms for Optimizing Speech Data
                 Projection",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1799",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{spears:1991:xover,
  author =       "William M. Spears and Vic Anand",
  title =        "A Study of Crossover Operators in Genetic
                 Programming",
  booktitle =    "Proceedings of the Sixth International Symposium on
                 Methodologies for Intelligent Systems ISMIS 91",
  year =         "1991",
  editor =       "Z. W. Ras and M. Zemankova",
  pages =        "409--418",
  publisher_address = "Berlin, Germany",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms",
  URL =          "http://www.aic.nrl.navy.mil:80/~spears/papers/ismis91.ps.Z",
  notes =        "Charlotte, NC, USA. 16-19 October 1991

                 Apparently NOT about (Koza style) GP",
}

@InProceedings{Spector:1994:GPAI,
  author =       "L. Spector",
  title =        "Genetic programming and {AI} planning systems",
  booktitle =    "Proceedings of Twelfth National Conference on
                 Artificial Intelligence",
  year =         "1994",
  address =      "Seattle, Washington, USA",
  publisher =    "AAAI Press/MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic programming (GP) is an automatic programming
                 technique that has recently been applied to a wide
                 range of problems including blocks-world planning. This
                 paper describes a series of illustrative experiments in
                 which GP techniques are applied to traditional
                 blocks-world planning problems. We discuss genetic
                 planning in the context of traditional AI planning
                 systems, and comment on the costs and benefits to be
                 expected from further work.

                 ",
}

@InProceedings{Spector:1994:ccaga,
  author =       "Lee Spector and Adam Alpern",
  title =        "Criticism, culture, and the automatic generation of
                 artworks",
  booktitle =    "Proceedings of Twelfth National Conference on
                 Artificial Intelligence",
  year =         "1994",
  pages =        "3--8",
  address =      "Seattle, Washington, USA",
  publisher =    "AAAI Press/MIT Press",
  keywords =     "genetic algorithms, genetic programming, bebop, jazz,
                 Charlie Parker, case-base",
  size =         "6 pages",
  notes =        "Combines case-base of existing {"}highly valued{"}
                 bebop jazz melodies with GP to produce new music. The
                 music is the response part of a call/response pair, ie
                 a novel improvised response to existing music. Is able
                 to evalulate the fitness of GP solutions using an
                 automatic critic, but the authors are not pleased with
                 the final solution even though the automatic critic
                 is.

                 url in text no longer works 9-Jun-95

                 ",
}

@InProceedings{Spector:1995:irdms,
  author =       "Lee Spector and Adam Alpern",
  title =        "Induction and Recapitulation of Deep Musical
                 Structure",
  booktitle =    "Proceedings of International Joint Conference on
                 Artificial Intelligence, IJCAI'95 Workshop on Music and
                 AI",
  year =         "1995",
  address =      "Montreal, Quebec, Canada",
  month =        "20-25 " # aug,
  organisation = "IJCAII,AAAI,CSCSI",
  keywords =     "genetic algorithms, genetic programming, Music, Jazz,
                 Charlie Parker",
  size =         "8 pages",
  notes =        "

                 Induce musical structure in ANN from body of musical
                 works, use resulting ANN as critic used to provide GP
                 fitness function. Uses 96 indexed memory cells plus
                 various block copy primitives and interation. Yeilds
                 {"}anytime{"} GP. As in Spector:1994:ccaga high fitness
                 GP produced music does not appeal artistically to the
                 author.",
}

@InProceedings{spector:1995:ADM,
  author =       "Lee Spector",
  title =        "Evolving Control Structures with Automatically Defined
                 Macros",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "99--105",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP, {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InCollection{spector:1996:aigp2,
  author =       "Lee Spector",
  title =        "Simultaneous Evolution of Programs and their Control
                 Structures",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "137--154",
  chapter =      "7",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "This chapter shows how a genetic programming system
                 can be used to simultaneously evolve programs and their
                 control structures. Koza has previously shown that the
                 performance of a genetic programming system can
                 sometimes be improved by allowing for the simultaneous
                 evolution of a main program and a collection of
                 automatically defined functions (ADFs). This chapter
                 shows how related techniques can be used to
                 simultaneously evolve a main program and a collection
                 of automatically defined macros (ADMs). Examples are
                 provided to show how the use of ADMs can lead to the
                 production of useful new control structures during
                 evolution, and data is presented to show that ADMs
                 sometimes provide a greater benefit than do ADFs. The
                 chapter includes a discussion of characteristics of
                 problems that may benefit most from the use of ADMs or
                 from architectures that include both ADFs and ADMs. It
                 is suggested that ADMs are likely to be useful for
                 evolving intelligent action systems for complex
                 environments, and data is presented to show that this
                 is the case for one such application. The chapter
                 concludes with a discussion of several directions for
                 further research.",
}

@InProceedings{spector:1996:ctiGP,
  author =       "Lee Spector and Sean Luke",
  title =        "Cultural Transmission of Information in Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "209--214",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.cs.gmu.edu/~sean/papers/culture-gp96.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/culture-gp96.ps.gz",
  size =         "9 pages",
  abstract =     "This paper shows how the performance of a genetic
                 programming system can be improved through the addition
                 of mechanisms for non-genetic transmission of
                 information between individuals (culture). Teller has
                 previously shown how genetic programming systems can be
                 enhanced through the addition of memory mechanisms for
                 individual programs [Teller 1994]; in this paper we
                 show how Teller's memory mechanism can be changed to
                 allow for communication between individuals within and
                 across generations. We show the effects of indexed
                 memory and culture on the performance of a genetic
                 programming system on a symbolic regression problem, on
                 Koza's Lawnmower problem, and on Wumpus world agent
                 problems. We show that culture can reduce the
                 computational effort required to solve all of these
                 problems. We conclude with a discussion of possible
                 improvements.",
  notes =        "GP-96 Discussion of this paper appeared as part of the
                 Scientific American (Oct. 96) column {"}Computing:
                 Programming with Primordial Ooze{"} (p. 50).",
}

@InProceedings{spector:1996:ogp,
  author =       "Lee Spector and Kilian Stoffel",
  title =        "Ontogenetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "394--399",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{spector:1996:GP,
  author =       "Lee Spector and Sean Luke",
  title =        "Culture Enhances the Evolvability of Cognition",
  booktitle =    "Cognitive Science (CogSci) 1996 Conference
                 Proceedings",
  year =         "1996",
  keywords =     "Genetic Programming, Genetic Algorithms",
  URL =          "http://www.cs.gmu.edu/~sean/papers/culture-cogsci.pdf",
  url2 =         "http://www.cs.gmu.edu/~sean/papers/culture-cogsci.ps.gz",
  abstract =     "This paper discusses the role of culture in the
                 evolution of cognitive systems. We define {"}culture{"}
                 as any information transmitted between individuals and
                 between generations by non-genetic means. Experiments
                 are presented that use genetic programming systems that
                 include special mechanisms for cultural transmission of
                 information. These systems evolve computer programs
                 that perform cognitive tasks including mathematical
                 function mapping and action selection in a virtual
                 world. The data show that the presence of
                 culture-supporting mechanisms can have a clear
                 beneficial impact on the evolvability of correct
                 programs. The implications that these results may have
                 for cognitive science are briefly discussed.",
  notes =        "

                 ",
}

@InProceedings{spector:1996:agap,
  author =       "Lee Spector and Kilian Stoffel",
  title =        "Automatic Generation of Adaptive Programs",
  booktitle =    "Proceedings of the Fourth International Conference on
                 Simulation of Adaptive Behavior: From animals to
                 animats 4",
  year =         "1996",
  editor =       "Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer
                 and Jordan Pollack and Stewart W. Wilson",
  pages =        "476--483",
  address =      "Cape Code, USA",
  publisher_address = "Cambridge, MA, USA",
  month =        "9-13 " # sep,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming, memory",
  ISBN =         "0-262-63178-4",
  notes =        "SAB-96 ontogenetic programming. Self changing programs
                 compared with other forms of GP, including GP+indexed
                 memory, on predicting repeating patern 010001 and
                 solving wumpus world.",
}

@InProceedings{spector:1998:GPqc,
  author =       "Lee Spector and Howard Barnum and Herbert J.
                 Bernstein",
  title =        "Genetic Programming for Quantum Computers",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "365--373",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Book{book:1999:aigp3,
  editor =       "Lee Spector and W. B. Langdon and Una-May O'Reilly and
                 Peter J. Angeline",
  title =        "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  URL =          "http://www.cs.bham.ac.uk/~wbl/aigp3",
  notes =        "AiGP3",
  size =         "488 pages",
}

@InCollection{spector:1999:aigp3,
  author =       "Lee Spector and Howard Barnum and Herbert J. Bernstein
                 and Nikhil Swamy",
  title =        "Quantum Computing Applications of Genetic
                 Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "7",
  pages =        "135--160",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InCollection{intro:1999:aigp3,
  author =       "Lee Spector and W. B. Langdon and Una-May O'Reilly and
                 Peter J. Angeline",
  title =        "An Introduction to the Third Volume",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "1",
  pages =        "1--12",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{spector:1999:FBQAAGP,
  author =       "Lee Spector and Howard Barnum and Herbert J. Bernstein
                 and Nikhil Swami",
  title =        "Finding a Better-than-Classical Quantum {AND}/{OR}
                 Algorithm using Genetic Programming",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "3",
  pages =        "2239--2246",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, quantum
                 computing",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@Article{spector:2000:IS,
  author =       "Lee Spector",
  title =        "Evolution of arbitary computational processes",
  journal =      "IEEE Intelligent Systems",
  year =         "2000",
  volume =       "15",
  number =       "3",
  pages =        "80--83",
  month =        may # "-" # jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1094-7167",
  URL =          "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf",
  size =         "3 pages",
  notes =        "part of hirsh:2000:GP",
}

@Proceedings{spector:2001:GECCO,
  title =        "Proceedings of the Genetic and Evolutionary
                 Computation Conference, {GECCO}-2001",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic programming, genetic algorithms, ant colony
                 optimization, evolvable hardware, artificial life,
                 adaptive behavior, agents, classifier systems, DNA
                 computing, quantum computing, molecular computing,
                 evolution strategies, evolutionary design of engineered
                 structures, evolutionary programming, evolutionary
                 robotics, evolutionary scheduling and routing,
                 evolvable hardware, methodology, pedagogy, philosophy,
                 real world applications",
  ISBN =         "1-55860-774-9",
  URL =          "http://www.isgec.org/GECCO-2001",
  URL =          "http://www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-774-9",
  size =         "1461 pages",
  notes =        "GECCO-2001. A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)",
}

@InProceedings{spector2:2001:gecco,
  title =        "Autoconstructive Evolution: Push, Push{GP}, and
                 Pushpop",
  author =       "Lee Spector",
  pages =        "137--146",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming,
                 self-replication, stack-based genetic programming,
                 ontogenetic programming, adaptive evolutionary
                 computation, recursion, modularity",
  ISBN =         "1-55860-774-9",
  URL =          "http://hampshire.edu/lspector/pubs/ace.pdf",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO

                 Slides at the 2001 Genetic and Evolutionary Computation
                 Conference (GECCO-2001).
                 http://hampshire.edu/lspector/ACE-GECCO.pdf",
}

@InProceedings{spector:2001:vqacpapsa,
  author =       "Lee Spector and Ryan Moore and Alan Robinson",
  title =        "Virtual Quidditch: {A} Challenge Problem for
                 Automatically Programmed Software Agents",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "384--389",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, Quidditch
                 harry potter j.k.rowling, faster than real time
                 simulator",
  URL =          "http://hampshire.edu/lspector/pubs/quidditch-cite.pdf",
  notes =        "GECCO-2001LB. public domain _when_ completed",
}

@Misc{spector:2001:p45,
  author =       "Lee Spector and Alan Robinson",
  title =        "Genetic programming and Autoconstructive Evolution
                 with the {Push} Programming Language",
  note =         "Draft",
  notes =        "No longer on Lee's home page",
}

@Article{spector:2002:GPEM,
  author =       "Lee Spector and Alan Robinson",
  title =        "Genetic Programming and Autoconstructive Evolution
                 with the Push Programming Language",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "1",
  pages =        "7--40",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, artificial
                 life, modularity, programming languages,
                 self-adaptation",
  ISSN =         "1389-2576",
  URL =          "http://hampshire.edu/lspector/pubs/push-gpem-final.pdf",
  abstract =     "Push is a programming language designed for the
                 expression of evolving programs within an evolutionary
                 computation system. This article describes Push and
                 illustrates some of the opportunities that it presents
                 for evolutionary computation. Two evolutionary
                 computation systems, PushGP and Pushpop, are described
                 in detail. PushGP is a genetic programming system that
                 evolves Push programs to solve computational problems.
                 Pushpop, an ?autoconstructive evolution? system, also
                 evolves Push programs but does so while simultaneously
                 evolving its own evolutionary mechanisms.",
  notes =        "Article ID: 395988",
}

@Article{spector:2002:GPEMr,
  author =       "Lee Spector",
  title =        "Book Review: The Quest for the Quantum Computer",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "4",
  pages =        "391--393",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, quantum
                 computing",
  ISSN =         "1389-2576",
  notes =        "Article ID: 5103877",
}

@InProceedings{spector:2002:gecco:workshop,
  title =        "Multi-type, Self-adaptive Genetic Programming as an
                 Agent Creation Tool",
  author =       "Lee Spector and Alan Robinson",
  pages =        "73--80",
  booktitle =    "{GECCO 2002}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming, pushGP",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{icga93:spencer,
  author =       "Graham F. Spencer",
  title =        "Automatic Generation of Programs for Crawling and
                 Walking",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  pages =        "654",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  size =         "1 pages",
  notes =        "See also biblio of GP2 Simulated six legged insect. GP
                 set-leg function with side effects. Worked!",
}

@InCollection{kinnear:spencer,
  author =       "Graham F. Spencer",
  title =        "Automatic generation of programs for Crawling and
                 Walking",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "15",
  pages =        "335--353",
  size =         "19 pages",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Simulated six legged insect. GP set-leg function with
                 side effects. Worked! 25%constant perturbation
                 .9--1.1",
}

@InCollection{spitz:1994:esrmsGP,
  author =       "Steven Spitz",
  title =        "Evolving Stopping Rule Mating Strategies using Genetic
                 Programming",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "163--171",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-182105-2",
  notes =        "When to decide to stop waiting for Mr. Wright.
                 Decision made by GP.

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InCollection{spitz:1994:dgpopce,
  author =       "Steven Spitz",
  title =        "Distributed Genetic Programming for On-line Prediction
                 in Changing Environments",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "150--159",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InCollection{spivak:2002:DOCRAGP,
  author =       "Polina K. Spivak",
  title =        "Discovery of Optical Character Recognition Algorithms
                 using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "223--232",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp 16 characters 10x10 GIF images,
                 96 fitness cases, parsimony pressure as part of
                 fitness",
}

@InProceedings{Spohn:1997:csec,
  author =       "Bryan G. Spohn and Philip H. Crowley",
  title =        "Complexity of Strategies and the Evolution of
                 Cooperation",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "genetic algorithms, classifier systems",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InCollection{srinivasan:2002:DBTPGP,
  author =       "Praveen Srinivasan",
  title =        "Development of Block-Stacking Teleo-Reactive Programs
                 using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "233--242",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{stanhope:1996:ivm-r,
  author =       "Stephen A. Stanhope and Jason M. Daida",
  title =        "An Individually Variable Mutation-Rate Strategy for
                 Genetic Algorithms",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "177--185",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{stanhope:1997:ivm-r,
  author =       "S. Stanhope and J. Daida",
  title =        "An Individually Variable Mutation-Rate Strategy for
                 Genetic Algorithms",
  booktitle =    "Evolutionary Programming VI: Proceedings of the Sixth
                 Annual Conference on Evolutionary Programming",
  year =         "1997",
  editor =       "Peter J. Angeline and Robert G. Reynolds and John R.
                 McDonnell and Russ Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  address =      "Indianapolis, Indiana, USA",
  publisher_address = "Berlin",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://ftp.eecs.umich.edu/people/daida/papers/EP97mutation.pdf",
  notes =        "EP-97",
}

@InProceedings{Stanhope:1997:GPtciSAR,
  author =       "Stephen A. Stanhope and Jason M. Daida",
  title =        "Genetic Programming for Target Classification and
                 Identification in Synthetic Aperture Radar Imagery",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "224--230",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{stanhope:1998:GPatcrSARi,
  author =       "Stephen A. Stanhope and Jason M. Daida",
  title =        "Genetic Programming for Automatic Target
                 Classification and Recognition in Synthetic Aperture
                 Radar Imagery",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and A.
                 E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  pages =        "735--744",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  notes =        "EP-98.
                 http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7",
}

@InProceedings{stauffer:2001:eh,
  author =       "Andre Stauffer and Daniel Mange and Gianluca Tempesti
                 and Christof Teuscher",
  title =        "BioWatch: {A} Giant Electronic Bio-Inspired Watch",
  booktitle =    "The Third NASA/DoD workshop on Evolvable Hardware",
  year =         "2001",
  editor =       "Didier Keymeulen and Adrian Stoica and Jason Lohn and
                 Ricardo S. Zebulum",
  pages =        "185--192",
  address =      "Long Beach, California",
  publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC,
                 20036-1992, USA",
  month =        "12-14 " # jul,
  organisation = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, cellular automata",
  ISBN =         "0-7695-1180-5",
  notes =        "EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/
                 see also Sipper:2001:sciam",
}

@InCollection{stefanini:1994:gcbaCA,
  author =       "Tim Stefanini",
  title =        "The Genetic Coding of Behavioral Attributes in
                 Cellular Automata",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "172--180",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "farmers

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@Unpublished{icga93-gp:stefanski,
  author =       "Pawel A. Stefanski",
  title =        "Genetic Programming Using Abstract Syntax Trees",
  note =         "Notes from Genetic Programming Workshop at ICGA-93",
  year =         "1993",
  notes =        "{"}We argue, that in order to evolve truely complex
                 programs, the genetic operators must be grounded in the
                 basic software engineering knowledge, and directly obey
                 not only syntactic but also (as much as possible)
                 semantic constraints.{"}",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/ICGA-93-GP-Abstracts.ps.Z",
  keywords =     "genetic algorithms, genetic programming",
  size =         "1 pages",
}

@InProceedings{steinberg:1999:OSTP,
  author =       "Louis Steinberg and Khaled Rasheed",
  title =        "Optimization by Searching a Tree of Populations",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1723--1730",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{stephens:1999:EMRGM,
  author =       "C. R. Stephens",
  title =        "Effect of Mutation and Recombination on the
                 Genotype-Phenotype Map",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1382--1389",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{stephens:2000:efit,
  author =       "C. R. Stephens and J. Mora Vargas",
  title =        "Effective Fitness as an Alternative Paradigm for
                 Evolutionary Computation {I}: General Formalism",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "4",
  pages =        "363--378",
  month =        oct,
  keywords =     "genetic algorithms, effective fitness, fitness
                 landscape, evolution equations, genotype-phenotype map,
                 induced symmetry breaking",
  ISSN =         "1389-2576",
  abstract =     "In evolutionary computation the concept of a fitness
                 landscape has played an important role, evolution
                 itself being portrayed as a hill-climbing process on a
                 rugged landscape. In this article we review the recent
                 development of an alternative paradigm for evolution on
                 a fitness landscape-effective fitness. It is shown that
                 in general, in the presence of other genetic operators
                 such as mutation and recombination, hill-climbing is
                 the exception rather than the rule; a discrepancy that
                 has its origin in the different ways in which the
                 concept of fitness appears-as a measure of the number
                 of fit offspring, or as a measure of the probability to
                 reach reproductive age. Effective fitness models the
                 former not the latter and gives an intuitive way to
                 understand population dynamics as flows on an effective
                 fitness landscape when genetic operators other than
                 reproductive selection play an important role.
                 Additionally, we will show that when the
                 genotype-phenotype map is degenerate, i.e. there exists
                 a synonym symmetry, it can be used to quantify the
                 degree of symmetry breaking of the map, thus allowing
                 for a quantitative explanation of phenomena such as
                 self-adaptation, bloat and evolutionary robustness.",
  notes =        "continued in stephens:2001:efit",
}

@Article{stephens:2001:efit,
  author =       "C. R. Stephens and J. Mora Vargas",
  title =        "Effective Fitness as an Alternative Paradigm for
                 Evolutionary Computation {II}: Examples and
                 Applications",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "1",
  pages =        "7--32",
  month =        mar,
  keywords =     "genetic algorithms, effective fitness, fitness
                 landscape, evolution equations, genotype-phenotype map,
                 induced symmetry breaking",
  ISSN =         "1389-2576",
  abstract =     "In paper I stephens:2000:efit of this series we
                 reviewed the recent development of an alternative
                 paradigm for evolution on a fitness landscape effective
                 fitness which offers an intuitive way to understand
                 population dynamics as flows on an effective fitness
                 landscape when genetic operators other than
                 reproductive selection play an important role. In this
                 article we demonstrate the utility of the concept using
                 several simple analytical models and some more complex
                 models that we simulate numerically. In particular, we
                 show that effective fitness offers a qualitative and
                 quantitative framework within which the phenomenon of
                 induced symmetry breaking of the genotype-phenotype map
                 may be understood. As explicit examples we consider:
                 the violation of the building block hypothesis in
                 non-epistatic landscapes; self-adaptation of genetic
                 algorithms in time-dependent fitness landscapes and the
                 appearance of evolutionary robustness as an emergent
                 property in the evolution of language. In all cases we
                 demonstrate that effective fitness offers a framework
                 within which these diverse phenomena can be understood
                 and in principle quantitatively studied.",
}

@InProceedings{sterling:1998:bccc:hpppp,
  author =       "Thomas Sterling",
  title =        "Beowulf-class Clustered Computing: Harnessing the
                 Power of Parallelism in a Pile of {PC}s",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "883",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  note =         "Invited talk",
  publisher =    "Morgan Kaufmann",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{stewart:1999:NV,
  author =       "Richard Stewart and Paul L. Rosin",
  title =        "Neural network construction using Voronoi
                 dissections",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "811",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{Stiffelman:1997:edrGPGA,
  author =       "Oscar Stiffelman",
  title =        "The Evolution of Data Representation Through Genetic
                 Programming and Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "197--206",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "it is shown bitstrings representing english text can
                 be encoded using this technique",
  notes =        "part of koza:1997:GAGPs combines Genesis wit DGPC",
}

@InProceedings{stilger:1996:GPdbqo,
  author =       "Michael Stillger and Myra Spiliopoulou",
  title =        "Genetic Programming in Database Query Optimization",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "388--393",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96 distributed databases, optimizing joins, sql",
}

@InProceedings{stoffel:1996:hpsbGP,
  author =       "Kilian Stoffel and Lee Spector",
  title =        "High-Performance, Parallel, Stack-Based Genetic
                 Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "224--229",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{stoica:1999:ECCSDHPC,
  author =       "Adrian Stoica and Carlos-Salazar Lazaro and Didier
                 Keymeulen and Ken Hayworth",
  title =        "Evolution of {CMOS} Circuits in Simulations and
                 Directly in Hardware on a Programmable Chip",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1198--1203",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Proceedings{stoica:1999:eh,
  title =        "The First {NASA}/Do{D} Workshop on Evolvable
                 Hardware",
  year =         "1999",
  editor =       "Adrian Stoica and Jason Lohn and Didier Keymeulen",
  address =      "Pasadena, California",
  publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC,
                 20036-1992, USA",
  month =        "19-21 " # jul,
  organisation = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, evolvable hardware",
  URL =          "http://cism.jpl.nasa.gov/ehw/events/nasa_eh/",
}

@Article{stoica:1999:GP3,
  author =       "Adrian Stoica",
  title =        "Genetic Programming {III}: Darwinian Invention and
                 Problem Solving",
  journal =      "IEEE Transaactions on Evolutionary Computation",
  year =         "1999",
  volume =       "3",
  number =       "3",
  pages =        "251--253",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  size =         "2 pages",
  notes =        "Review of koza:gp3",
}

@InProceedings{stolzmann:1998:acs,
  author =       "Wolfgang Stolzmann",
  title =        "Anticipatory Classifier Systems",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "658--664",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{streeter:2001:gecco,
  title =        "Automated Discovery of Numerical Approximation
                 Formulae Via Genetic Programming",
  author =       "Matthew Streeter and Lee A. Becker",
  pages =        "147--154",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming,
                 approximations, symbolic, regression, Pareto
                 optimality",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO See also
                 streeter:2001:TBSW",
}

@InProceedings{streeter:2001:tbsw,
  author =       "Matthew Streeter and Lee A. Becker",
  title =        "Toward a Better Sine Wave",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "398--404",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001LB. Follow up to streeter:2001:GECCO",
}

@InProceedings{streeter:2002:EuroGP,
  title =        "Routine Duplication of Post-2000 Patented Inventions
                 by Means of Genetic Programming",
  author =       "Matthew J. Streeter and Martin A. Keane and John R.
                 Koza",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  publisher =    "Springer-Verlag",
  volume =       "2278",
  series =       "LNCS",
  pages =        "26--36",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "Previous work has demonstrated that genetic
                 programming can automatically create analog electrical
                 circuits, controllers, and other devices that duplicate
                 the functionality and, in some cases, partially or
                 completely duplicate the exact structure of inventions
                 that were patented between 1917 and 1962. This paper
                 reports on a project in which we browsed patents of
                 analog circuits issued after January 1, 2000 on the
                 premise that recently issued patents represent current
                 research that is considered to be of practical and
                 scientific importance. The paper describes how we used
                 genetic programming to automatically create circuits
                 that duplicate the functionality or structure of five
                 post -2000 patented inventions. This work employed four
                 new techniques (motivated by the theory of genetic
                 algorithms and genetic programming) that we believe
                 increased the efficiency of the runs. When an automated
                 method duplicates a previously patented human -designed
                 invention, it can be argued that the automated method
                 satisfies a Patent -Office-based variation of the
                 Turing test.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{streeter:2002:gecco,
  author =       "Matthew J. Streeter and Martin A. Keane and John R.
                 Koza",
  title =        "Iterative Refinement Of Computational Circuits Using
                 Genetic Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "877--884",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, circuits,
                 computational circuits, error correction, iterative
                 refinement, numerical approximation",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{Keane:2002:eh,
  author =       "M. A. Keane and J. R. Koza and M. J. Streeter",
  editor =       "Adrian Stoica and Jason Lohn and Rich Katz and Didier
                 Keymeulen and Ricardo Salem Zebulum",
  month =        "15-18 " # jul,
  year =         "2002",
  title =        "Automatic Synthesis Using Genetic Programming of an
                 Improved General-Purpose Controller for Industrially
                 Representative Plants",
  booktitle =    "The 2002 {NASA/DoD} Conference on Evolvable Hardware",
  pages =        "113--122",
  publisher =    "IEEE Computer Society",
  address =      "Alexandria, Virginia",
  organization = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  keywords =     "genetic algorithms, genetic programming",
  publisher_address = "10662 Los Vaqueros Circle, P.O. Box 3014, Los
                 Alamitos, CA, 90720-1314, USA",
  email =        "makeane@ix.netcom.com",
  ISBN =         "0-7695-1718-8",
  notes =        "EH2002 http://cism.jpl.nasa.gov/ehw/events/nasaeh02/",
}

@InProceedings{strother:1998:spscomDNAc,
  author =       "Todd Strother and Anthony G. Frutos and Qinghua Liu
                 and Liman Wang and Susan D. Gillmor and Anne Condon and
                 Robert M. Corn and Max G. Lagally and Lloyd M. Smith",
  title =        "A Split and Pool Approach to the Synthesis of
                 Combinational Oligonucleotide Mixtures for {DNA}
                 Computing",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In recent years, the use of DNA to encode information
                 for computing applications has been explored. A recent
                 paper described a model in which information is encoded
                 in sets of {"}words,{"} in which each member of the set
                 differs by at least 4 bases from any other member of
                 the set, facilitating efficient hybridization
                 discrimination. The structure of these words is such
                 that the combinatorial sets needed for DNA computing
                 applications may not be directly synthesized. In this
                 paper we describe an approach to the synthesis of the
                 requisite combinatorial sets using a {"}split and
                 pool{"} approach. Preliminary results are presented
                 demonstrating the fidelity of the synthesis under these
                 conditions.",
  notes =        "GP-98LB, GP-98PhD Student Workshop",
}

@InProceedings{sturgill:1999:ECGPE,
  author =       "David Sturgill and Gautam Pant",
  title =        "Evaluation Criteria for Genetically-Tuned
                 Problem-Solving Experts",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1390--1397",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{suen:1999:PIIPGA,
  author =       "Lawrence Chun Kiat Suen",
  title =        "Performance Improvement through Isolated Population
                 Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "218--225",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{sugai:1999:SEHP,
  author =       "Koji Sugai",
  title =        "Stochastic Evolution on the Hierarchical Population",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "628--634",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{Sugiyama:1995:ersoeg,
  author =       "Tomonori Sugiyama and Takashi Kido and Masakazu
                 Nakanishi",
  title =        "Evolving Robot Strategy for Open ended Game",
  booktitle =    "Progress in Evolutionary Computation",
  publisher =    "Springer-Verlag",
  year =         "1995",
  editor =       "X. Yao",
  volume =       "956",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "225--235",
  address =      "Heidelberg, Germany",
  keywords =     "genetic algorithms, genetic programming,
                 entertainment, Games",
  notes =        "Robot battle game X-Window Robot Battle XRB. Is is it
                 a GP? Robot's game strategy given by genes that are
                 evoleved using a GP plus tree based crossover.

                 ",
}

@InCollection{sukalski:1999:DATTSUGP,
  author =       "Mitch Sukalski",
  title =        "Developing Adaptive {TCP} Transmission Strategies
                 Using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "226--235",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@MastersThesis{Suleman:1997:masters,
  author =       "Hussein Suleman",
  title =        "Genetic Programming in Mathematica",
  school =       "Computer Science Department, University of Durban
                 Westville",
  year =         "1997",
  address =      "Kwazulu-Natal, South Africa",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://yoda.cs.udw.ac.za/~hussein/software/gpinmath.zip",
  size =         "pages",
  notes =        "gpinmath.zip in MS-Word 7 format",
}

@InCollection{suri:2000:AGAFLPHG,
  author =       "Siddharth Suri",
  title =        "A Genetic Algorithm for Finding a Long Path in a
                 Hamiltonian Graph",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "360--370",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{Surkan:2002:eh,
  author =       "Alvin J. Surkan and Amiran Khuskivadze",
  editor =       "Adrian Stoica and Jason Lohn and Rich Katz and Didier
                 Keymeulen and Ricardo Salem Zebulum",
  month =        "12-14 " # jul,
  year =         "2001",
  title =        "Evolution of Quantum Algorithms for Computer of
                 Reversible Operators",
  booktitle =    "The 2002 {NASA/DoD} Conference on Evolvable Hardware",
  pages =        "186--187",
  publisher =    "IEEE Computer Society",
  address =      "Long Beach, California",
  organization = "Jet Propulsion Laboratory, California Institute of
                 Technology",
  keywords =     "genetic algorithms, genetic programming, program
                 synthesis, evolutionary design and discovery of
                 evolvable hardware, circuit models, qubits, CCNOT
                 Toffoli quantum gates, quantum computation",
  publisher_address = "10662 Los Vaqueros Circle, P.O. Box 3014, Los
                 Alamitos, CA, 90720-1314, USA",
  email =        "surkan@cse.unl.edu",
  ISBN =         "0-7695-1180-5",
  size =         "2 pages",
  notes =        "EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/",
}

@InProceedings{Suzuki:1996:femvnc,
  author =       "Hideaki Suzuki",
  title =        "Functional Emergence with Multiple von Neumann
                 Computers",
  booktitle =    "Artificial Life V: Proceedings of the Fifth
                 International Workshop on the Synthesis and Simulation
                 of Living Systems",
  year =         "1996",
  editor =       "C. Langton and K. Shimohara",
  pages =        "108--115",
  publisher_address = "Cambridge, MA, USA",
  month =        "16-18 " # may,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "ALife V
                 http://www.hip.atr.co.jp/~alife/program/node2.html#SECTION00011000000000000000

                 ",
}

@Article{suzuki:1997:mvnc,
  author =       "Hideaki Suzuki",
  title =        "Multiple von Neumann Computers: An Evolutionary
                 Approach to Functional Emergence",
  journal =      "Artificial Life",
  year =         "1997",
  volume =       "3",
  pages =        "121--142",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Artificial Life Journal

                 ",
}

@InProceedings{suzuki:1999:PTIC,
  author =       "Yasuhiro Suzuki and Hiroshi Tanaka",
  title =        "Practical and Theoretical Investigation of a
                 Collective work",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1450",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{svangard:2002:estsugp,
  author =       "Nils Svangard and Stefan Lloyd and Peter Nordin and
                 Clas Wihlborg",
  title =        "Evolving Short-Term Trading Strategies Using Genetic
                 Programming",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "2006--2010",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We have used a linear Genetic Programming system with
                 a multitude of different quotes on financial securities
                 as input in order to evolve an intraday trading
                 strategy for an individual stock, attempting to
                 outperform a simple buy and hold strategy over the same
                 period of time.",
}

@InProceedings{Svingen:1997:GPppai,
  author =       "Borge Svingen",
  title =        "{GP}++ An Introduction",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "231--239",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670

                 Description of C++ library to support GP",
}

@InProceedings{svingen:1997:uGPdc,
  author =       "Borge Svingen",
  title =        "Using Genetic Programming for Document
                 Classification",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "240--245",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{svingen:1998:lrlGP,
  author =       "Borge Svingen",
  title =        "Learning Regular Languages Using Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "374--376",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{swift:1999:MFGVFUGA,
  author =       "Stephen Swift and Xiaohui Liu",
  title =        "Modelling and Forecasting of Glaucomatous Visual
                 Fields Using Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1731--1737",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{sykes:2000:UGAGDSPSP,
  author =       "Michael Sykes",
  title =        "Using a Genetic Algorithm to Generate Decoy Sets for
                 Protein Structure Prediction",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "371--379",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{icga93:tackett,
  author =       "Walter Alden Tackett",
  title =        "Genetic Programming for Feature Discovery and Image
                 Discrimination",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  editor =       "Stephanie Forrest",
  publisher =    "Morgan Kaufmann",
  pages =        "303--309",
  size =         "7 pages",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/GP.feature.discovery.ps.Z",
  notes =        "Contrasts ANN, Binary Tree Classifier and GP on same
                 test data. GP a bit better but 8 times faster in
                 execution. GP(lisp) takes far more CPU to learn.
                 Version in C (GP/C) 25 times faster than GP(lisp)",
}

@InProceedings{Tackett93,
  author =       "Walter Alden Tackett",
  title =        "Genetic Generation of {``}Dendritic{''} Trees for
                 Image Classification",
  booktitle =    "Proceedings of WCNN93",
  publisher =    "IEEE Press",
  pages =        "IV 646--649",
  year =         "1993",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming,
                 connectionism, cogann",
  abstract =     "ABSTRACT Genetic Programming (GP) is an adaptive
                 method for generating executable programs from labeled
                 training data. It differs from the conventional methods
                 of Genetic Algorithms because it manipulates tree
                 structures of arbitrary size and shape rather than
                 fixed length binary strings. We apply GP to the
                 development of a processing tree with a dendritic, or
                 neuron-like structure: measurements from a set of input
                 nodes are weighted and combined through linear and
                 nonlinear operations to form an output response. Unlike
                 conventional neural methods, no constraints are placed
                 upon size, shape, or order of processing withing the
                 network. This network is used to classify feature
                 vectors extracted from IR imagery into target/nontarget
                 catagories using a database of 2000 training samples.
                 Performance is tested against a separate database of
                 7000 samples. For purposes of comparison, the same
                 training and test sets are used to train two other
                 adaptive classifier systems, the binary tree classifier
                 and the Backpropagation neural network. The GP network
                 acheives higher performance with reduced computational
                 requirements.",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/GP.feature.discovery.ps.Z",
}

@InCollection{kinnear:tackett,
  author =       "Walter Alden Tackett and Aviram Carmi",
  institution =  "HMSC",
  title =        "The Donut Problem: Scalability and Generalization in
                 Genetic Programming",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  pages =        "143--176",
  chapter =      "7",
  keywords =     "genetic algorithms, genetic programming",
  size =         "34 pages",
  notes =        "see also
                 ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/.message
                 ICGA93.Donut.ps.Z - Preliminary version of Avi and
                 Walter's ICGA93 paper",
}

@PhdThesis{Tackett:1994:thesis,
  author =       "Walter Alden Tackett",
  title =        "Recombination, Selection, and the Genetic Construction
                 of Computer Programs",
  school =       "University of Southern California, Department of
                 Electrical Engineering Systems",
  year =         "1994",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  abstract =     "Computational intelligence seeks as a basic goal to
                 create artificial systems which mimic aspects of
                 biological adaptation, behavior, perception, and
                 reasoning. Toward that goal, genetic program induction
                 - {"}Genetic Programming{"} - has succeeded in
                 automating an activity traditionally considered to be
                 the realm of creative human endeavor. It has been
                 applied successfully to the creation of computer
                 programs which solve a diverse set of model problems.
                 This naturally leads to questions such as:

                 * Why does it work? * How does it fundamentally differ
                 from existing methods?

                 * What can it do that existing methods cannot?

                 The research described here seeks to answer those
                 questions through investigations on several fronts.
                 Analysis is performed which shows that Genetic
                 Programming has a great deal in common with heuristic
                 search, long studied in the field of Artificial
                 Intelligence. It introduces a novel aspect to that
                 method in the form of the recombination operator which
                 generates successors by combining parts of favorable
                 strategies. On another track, we show that Genetic
                 Programming is a powerful tool which is suitable for
                 real-world problems. This done first by applying it to
                 an extremely difficult induction problem and measuring
                 performance against other state-of-the-art methods. We
                 continue by formulating a model induction problem which
                 not only captures the pathologies of the real world,
                 but also parameterizes them so that variation in
                 performance can be measured as a function of
                 confounding factors. At the same time, we study how the
                 properties of search can be varied through the effects
                 of the selection operator. Combining the lessons of the
                 search analysis with known properties of biological
                 systems leads to the formulation of a new recombination
                 operator which is shown to improve induction
                 performance. In support of the analysis of selection
                 and recombination, we define problems in which
                 structure is precisely controlled. These allow fine
                 discrimination of search performance which help to
                 validate analytic predictions. Finally, we address a
                 truly unique aspect of Genetic Programming, namely the
                 exploitation of symbolic procedural knowledge in order
                 to provide {"}explanations{"} from genetic programs.",
  url_2 =        "ftp://ftp.santafe.edu/pub/OLD/Users/tackett/phd",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/watphd.tar.Z",
  notes =        "Also available as Available as Technical Report CENG
                 94-13, Dept. of Electrical Engineering Systems,
                 University of Southern California, April 1994.",
}

@InProceedings{Tackett:1994:broodGP,
  author =       "W. A. Tackett and A. Carmi",
  title =        "The unique implications of brood selection for genetic
                 programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Tackett:1995:grgsscp,
  author =       "Walter Alden Tackett",
  title =        "Greedy Recombination and Genetic Search on the Space
                 of Computer Programs",
  booktitle =    "Foundations of Genetic Algorithms 3",
  year =         "1995",
  editor =       "L. Darrell Whitley and Michael D. Vose",
  pages =        "271--297",
  publisher_address = "San Francisco, CA, USA",
  address =      "Estes Park, Colorado, USA",
  month =        "31 " # jul # "--2 " # aug # " 1994",
  organisation = "International Society for Genetic Algorithms",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-356-5",
  notes =        "FOGA-3

                 Royal Road, Deceptive Royal Road, Soft brood selection,
                 comparision with evolution strategies (u,l)-ES, NOP_1,
                 NOP_2, NOP_4 (intron like). {"}Performance (of SSGA)
                 depends critically on how individuals are selected for
                 replacement (ref De Jong FOGA-2){"} page 288.
                 {"}greatest fitness variation is always achieved using
                 the tournament selection method{"} p290. Claims to
                 explain results in kinnear:kinnear

                 ",
}

@Article{Tackett:1995:mGP,
  author =       "Walter Alden Tackett",
  title =        "Mining the genetic program",
  journal =      "IEEE Expert",
  year =         "1995",
  volume =       "10",
  number =       "3",
  pages =        "28--38",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "IEEE Expert Special Track on Evolutionary Programming
                 (P. J. Angeline editor) angeline:1995:er",
}

@InProceedings{takadama:1999:HDGLAO,
  author =       "Keiki Takadama and Takao Terano and Katsunori
                 Shimohara",
  title =        "How to Design Good Learning Agents in Organization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1398--1405",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{takahashi:1999:AECTAGDS,
  author =       "Eiichi Takahashi and Masahiro Murakawa and Kenji Toda
                 and Tetsuya Higuchi",
  title =        "An Evolvable-hardware-based Clock Timing Architecture
                 towards GigaHz Digital Systems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1204--1210",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{takahashi:1999:pfhGA,
  author =       "Osamu Takahashi and Hajime Kita and Shigenobu
                 Kobayashi",
  title =        "Protein Folding by a Hierarchical Genetic Algorithm",
  booktitle =    "fourth AROB",
  year =         "1999",
  month =        "19-22 " # jan,
  keywords =     "genetic algorithms",
  notes =        "Evolves branching chromosome using crossover

                 ",
}

@MastersThesis{talko:mastersthesis,
  author =       "Bret Talko",
  title =        "A Rule-Based Approach for Constructing Neural Networks
                 Using Genetic Programming",
  school =       "University of Melbourne",
  year =         "1999",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "msc_thesis.ps.gz",
  size =         "166 pages",
  abstract =     "This thesis presents a novel use of Genetic
                 Programming (GP) to evolve recurrent, weightless neural
                 networks. The approach taken uses neural network
                 construction rules as the data structures that undergo
                 adaptation by the GP algorithm. These rules can be used
                 to construct a neural network by adding neurons and
                 connections to an initial basic network configuration.
                 In addition to evolving the architectures of networks,
                 the system evolves the formulae for the activation
                 function of each neuron in the networks and the number
                 of processing cycles for the networks.

                 The system has been applied to a number of Boolean
                 functions and it is shown that solution networks were
                 able to be found for each. Some variations in the
                 system design were investigated on the Boolean
                 functions to identify possible improvements that could
                 be made to the system which would result in better
                 performance. One variation to the system design which
                 resulted in a significantly large increase in the
                 system performance was made by changing the
                 construction rules that are used by the system.

                 A number of characteristics of the produced networks
                 were noted. Among them is the generation of network
                 construction rules that are similar to each other. A
                 system variation was made which succeeded in making the
                 rules more diverse but does not generally result in
                 better performance. Another characteristics of the
                 networks is that their construction rules often contain
                 unused and redundant rules.

                 The construction rules were designed to allow efficient
                 specification of networks which contain multiple
                 instances of the same sub-network. The system uses this
                 when discovering solution networks for Boolean
                 functions which can be decomposed into two identical
                 Boolean functions. Importantly, the system achieved
                 significantly better results than a modified version of
                 the system in which the features enabling efficient
                 network specification were not present. This suggests
                 that incorporating a modular construction process for
                 building networks is useful for obtaining solution
                 networks to decomposable problems.",
  notes =        "p129 XOR {"}Therefore Gruau's result is significantly
                 better than the GPNN result{"}",
}

@InProceedings{talko:1999:ai,
  author =       "Bret Talko and Linda Stern and Les Kitchen",
  title =        "Evolving Modular Neural Networks Using Rule-Based
                 Genetic Programming",
  booktitle =    "12th Australian Joint Conference on Artificial
                 Intelligence",
  year =         "1999",
  editor =       "Norman Foo",
  volume =       "1747",
  series =       "LNCS",
  pages =        "482--483",
  address =      "Sydney, Australia",
  publisher_address = "Berlin",
  month =        "6-10 " # dec,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-66822-5",
  URL =          "http://www.cs.mu.oz.au/~talko/posterai99.ps",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66822-5",
  size =         "2 pages",
  notes =        "http://www.cse.unsw.edu.au/~ai99/ masters thesis",
}

@InProceedings{tan:2002:mmccrugp,
  author =       "K. C. Tan and A. Tay and T. H. Lee and C. M. Heng",
  title =        "Mining multiple comprehensible classification rules
                 using genetic programming",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "1302--1307",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic Programming (GP) has been emerged as a
                 promising approach to deal with classification task in
                 data mining. This work extends the tree representation
                 of GP to evolve multiple comprehensible IF-THEN
                 classification rules. In the paper, we introduce a
                 concept mapping technique for fitness evaluation of
                 individuals. A covering algorithm that employs an
                 artificial immune system-like memory vector is used to
                 produce multiple rules as well as to remove redundant
                 rules. The proposed GP classifier is validated upon
                 nine benchmark datasets and the simulation results
                 confirm the viability and effectiveness of the GP
                 approach for solving data mining problems in a wide
                 spectrum of application domains.",
  notes =        "Michigan approach.

                 GPc, groovy Java GP (gjprog), WEKA. problem specific
                 population sizes 10-100 and w_1 and w_2.

                 ",
}

@Article{tanev:2000:pdiGP,
  author =       "Ivan T. Tanev and Takashi Uozumi and Koichi Ono",
  title =        "{DCOM}-based Parallel Distributed Implementation of
                 {GP}",
  journal =      "Parallel and Distributed Computing Practices",
  year =         "2000",
  volume =       "3",
  number =       "1",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1097-2803",
  abstract =     "We present an approach for parallel distributed
                 implementation of genetic programming, which is devoted
                 to improve the computational performance of genetic
                 programming by exploiting parallelism at the level of
                 evaluation of the individuals. The approach is based on
                 DCOM client-server model. Using the DCOM-paradigm
                 offers the advantages of parallel distributed
                 implementation of genetic programming, such as binary
                 standardization, platform-, machine- and
                 protocol-neutrality, and seamless integration with
                 different Internet protocols. The developed
                 implementation of genetic programming runs in LAN
                 and/or Internet environments.

                 The double-queued multi-threaded architecture of the
                 DCOM-server, aimed to extend the functionality of the
                 DCOM with features, such as asynchronous communications
                 still implementing blocking-mode calls, and reduced
                 communication overhead of the evaluation of simple
                 GP-individuals, is developed. The implementation of
                 batching, directed towards the alleviation of
                 communication overhead during the evaluation of simple
                 GP-individuals, is proposed. Analytically estimated and
                 experimentally obtained performance evaluation results
                 are discussed. The results show that clear super linear
                 speedup can be achieved upon code growth in genetic
                 programming.",
  notes =        "parallel and distributed computing
                 http://www.cs.okstate.edu/~pdcp/vols/vol03/vol03no1abs.html",
}

@InProceedings{tanev:2000:piGPc,
  author =       "Ivan T. Tanev and Takashi Uozumi and Koichi Ono",
  title =        "Parallel Implementation of Genetic Programming on
                 Clusters",
  booktitle =    "Late Breaking Papers at the GECCO'2000 Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  pages =        "388--396",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We present an approach for developing parallel
                 distributed implementation of genetic programming
                 (PDIGP) based on exploitation of the inherent
                 parallelism among semi-isolated subpopulations.
                 Proposed implementation runs on cost-efficient
                 configurations of clusters in LAN and/or Internet
                 environment. PDIGP features single global migration
                 broker and centralized manager of the semi-isolated
                 subpopulations, which contribute to achieving quick
                 propagation of the globally fittest individuals among
                 the subpopulations, reducing the performance demands to
                 the communication network, and achieving flexibility of
                 system configurations by introducing dynamically
                 scaling up opportunities. PDIGP exploits distributed
                 component object model (DCOM) as a communication
                 paradigm, which offers generic support for the issues
                 of naming, locating and protecting the distributed
                 entities in PDIGP. Experimentally obtained results show
                 that in some system configurations the computational
                 effort is less than the computational effort in
                 canonical panmictic GP. Analytically obtained and
                 empirically proved results of the speedup of the
                 computational performance indicate that PDIGP features
                 linear, close to ideal characteristics, which, together
                 with the observed reduction of the computational effort
                 contribute to the acquaintance of hyper-linear overall
                 speedup in developed PDIGP.",
  notes =        "symbolic regression, many node distributed
                 computing

                 Muroran Institute of Technology Mizumoto 27-1,
                 Muroran,JAPAN 050-8585",
}

@Article{tanev:2001:SA,
  author =       "Ivan Tanev and Takashi Uozumi and Koichi Ono",
  title =        "Scalable architecture for parallel distributed
                 implementation of genetic programming on network of
                 workstations",
  journal =      "Journal of Systems Architecture",
  volume =       "47",
  pages =        "557--572",
  year =         "2001",
  number =       "7",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Distributed
                 component object model, Island model of parallelism,
                 Network of workstations",
  size =         "16 pages",
  abstract =     "We present an approach for developing a scalable
                 architecture for parallel distributed implementation of
                 genetic programming (PDIGP). The approach is based on
                 exploitation of the inherent parallelism among
                 semi-isolated subpopulations in genetic programming
                 (GP). Proposed implementation runs on cost-efficient
                 configurations of networks on workstations in LAN and
                 Internet environment. Developed architecture features
                 single global migration broker and centralized manager
                 of the semi-isolated subpopulations, which contribute
                 to achieving quick propagation of the globally fittest
                 individuals among the subpopulations, reducing the
                 performance demands to the communication network, and
                 achieving flexibility in system configurations by
                 introducing dynamically scaling up opportunities. PDIGP
                 exploits distributed component object model (DCOM) as a
                 communication paradigm, which as a true system model
                 offers generic support for the issues of naming,
                 locating and protecting the distributed entities in
                 proposed architecture of PDIGP. Experimentally obtained
                 results of computational effort of proposed PDIGP are
                 discussed. The results show that computational effort
                 of PDIGP marginally differs from the computational
                 effort in canonical panmictic GP evolving single large
                 population. For PDIGP running on systems configurations
                 with 16 workstations the computational effort is less
                 than panmictic GP, while for smaller configurations it
                 is insignificantly more. Analytically obtained and
                 empirically proved results of the speedup of
                 computational performance indicate that PDIGP features
                 linear, close to ideal characteristics. Experimentally
                 obtained results of PDIGP running on configurations
                 with eight workstations show close to 8-fold overall
                 speedup. These results are consistent with the
                 anticipated cumulative effect of the insignificant
                 increase of computational effort for the considered
                 configuration and the close to linear speedup of
                 computational performance.",
}

@InProceedings{Tanigawa:2000:GECCO,
  author =       "Toro Tanigawa and Qiangfu Zhao",
  title =        "A Study on Efficient Generation of Decision Trees
                 Using Genetic Programming",
  pages =        "1047--1052",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{taniguchi:2001:micscmagp,
  author =       "Ken Taniguchi and Setsuya Kurahashi and Takao Terano",
  title =        "Managing Information Complexity in a Supply Chain
                 Model by Agent-Based Genetic Programming",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "413--420",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2001LB. see also CEF2001 RePEc:sce:scecf1:238?",
}

@InCollection{tannenbaum:2000:CPPGP,
  author =       "David Tannenbaum",
  title =        "Co-Evolution of Predator and Prey using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "380--386",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{tanomaru:1996:tm,
  author =       "Julio Tanomaru and Akio Azuma",
  title =        "Automatic Generation of Turing Machines by a Genetic
                 Approach",
  booktitle =    "The First International Workshop on Machine Learning,
                 Forecasting, and Optimization (MALFO96)",
  year =         "1996",
  editor =       "Daniel Borrajo and Pedro Isasi",
  pages =        "173--184",
  address =      "Gatafe, Spain",
  month =        "10--12 " # jul,
  organisation = "Universidad Carlos III de Madrid",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "84-89315-04-3",
  URL =          "http://grial.uc3m.es/~dborrajo/malfo96.html",
}

@InProceedings{tanomaru:1998:etmx,
  author =       "Julio Tanomaru",
  title =        "Evolving Turing Machines from Examples",
  booktitle =    "Artificial Evolution",
  year =         "1993",
  editor =       "J.-K. Hao and E. Lutton and E. Ronald and M.
                 Schoenauer and D. Snyers",
  volume =       "1363",
  series =       "LNCS",
  address =      "Nimes, France",
  month =        oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://link.springer.de/link/service/series/0558/papers/1363/13630167.pdf",
  size =         "14 pages",
  abstract =     "The aim of this paper is to investigate the
                 application of evolutionary approaches to the automatic
                 design of automata in general, and Turing machines, in
                 particular. Here, each automaton is represented
                 directly by its state transition table and the number
                 of states is allowed to change dynamically as evolution
                 takes place. This approach contrasts with less natural
                 representation methods such as trees of genetic
                 programming, and allows for easier visualization and
                 hardware implementation of the obtained automata. Two
                 methods are proposed, namely, a straightforward,
                 genetic-algorithm-like one, and a more sophisticated
                 approach involving several operators and the 1/5 rule
                 of evolution strategy. Experiments were carried out for
                 the automatic generation of Turing machines from
                 examples of input and output tapes for problems of
                 sorting, unary arithmetic, and language acceptance, and
                 the results indicate the feasibility of the
                 evolutionary approach. Since Turing machines can be
                 viewed as general representations of computer programs,
                 the proposed approach can be thought of as a step
                 towards the generation of programs and algorithms by
                 evolution.",
  notes =        "AE'97",
}

@InCollection{tarnikova:2000:DSSNPGP,
  author =       "Yuliya Tarnikova",
  title =        "Discovering Strategies for Solving a Number Puzzle
                 using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "387--396",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InCollection{taylor:1995:DPTCA,
  author =       "Caz Taylor",
  title =        "Discovering Patterns in Two-Dimensional Cellular
                 Automata",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "269--278",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{taylor:1998:gadiirsab,
  author =       "Janet Taylor and Jem J Rowland and Richard J Gilbert
                 and Alun Jones and Michael K Winson and Douglas B
                 Kell",
  title =        "Genetic Algorithm Decoding for the Interpretation of
                 Infra-red Spectra in Analytical Biotechnology",
  booktitle =    "Late Breaking Papers at EuroGP'98: the First European
                 Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and Marc Schoenauer
                 and Terry Fogarty and Wolfgang Banzhaf",
  pages =        "21--25",
  address =      "Paris, France",
  publisher_address = "School of Computer Science",
  month =        "14-15 " # apr,
  publisher =    "CSRP-98-10, The University of Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  size =         "5 pages",
  notes =        "EuroGP'98LB part of Poli:1998:egplb",
}

@InProceedings{taylor:1998:GPiftis,
  author =       "Janet Taylor and Jem J. Rowland and Royston Goodacre
                 and Richard J. Gilbert and Michael K. Winson and
                 Douglas B. Kell",
  title =        "Genetic Programming in the Interpretation of Fourier
                 Transform Infrared Spectra: Quantification of
                 Metabolites of Pharmaceutical Importance",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "377--380",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{taylor:1998:GParsabs,
  author =       "Janet Taylor and Jem Rowland and Douglas Kell",
  title =        "Genetic Programming Applied to the Rapid Spectroscopic
                 Analysis of Biological Samples",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@Article{taylor:1998:dpmsGP:aises,
  author =       "Janet Taylor and Royston Goodacre and William G. Wade
                 and Jem J. Rowland and Douglas B. Kell",
  title =        "The deconvolution of pyrolysis mass spectra using
                 genetic programming: application to the identification
                 of some Eubacterium species",
  journal =      "FEMS Microbiology Letters",
  year =         "1998",
  volume =       "160",
  pages =        "237--246",
  organisation = "Federation of European Microbiological Societies",
  publisher =    "Elsevier Science",
  keywords =     "genetic algorithms, genetic programming, Chemometrics,
                 Eubacterium, pyrolysis mass spectrometry",
  abstract =     "Pyrolysis mass spectrometry was used to produce
                 complex biochemical fingerprints of Eubacterium
                 exiguum, E. infirmum, E. tardum and E. timidum. To
                 examine the relationship between these organisms the
                 spectra were clustered by canonical variates analysis,
                 and four clusters, one for each species, were observed.
                 In an earlier study we trained artificial neural
                 networks to identify these clinical isolates
                 successfully; however, the information used by the
                 neural network was not accessible from this so-called
                 'black box' technique. To allow the deconvolution of
                 such complex spectra (in terms of which masses were
                 important for discrimination) it was necessary to
                 develop a system that itself produces 'rules' that are
                 readily comprehensible. We here exploit the
                 evolutionary computational technique of genetic
                 programming; this rapidly and automatically produced
                 simple mathematical functions that were also able to
                 classify organisms to each of the four bacterial groups
                 correctly and unambiguously. Since the rules used only
                 a very limited set of masses, from a search space some
                 50 orders of magnitude greater than the dimensionality
                 actually necessary, visual discrimination of the
                 organisms on the basis of these spectral masses alone
                 was also then possible.",
}

@PhdThesis{JanetTaylor:2000:thesis,
  author =       "Janet Taylor",
  title =        "Genetic Programming and Genetic Algorithms in the
                 Spectroscopic Analysis of Biological Samples",
  school =       "University of Wales, Aberystwyth",
  year =         "2000",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{taylor:2001:sasgsam,
  author =       "Janet Taylor and Jem J. Rowland and Douglas B. Kell",
  title =        "Spectral Analysis via Supervised Genetic Search with
                 Application-specific Mutations",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "481--486",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 biotechnology, supervised, spectroscopy, calibration",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =

                 Hybrid GA-GP? High throughput screening. Fourier
                 transform. E. Coli. Hill climbing. Exponent not useful?
                 Discusses comparison with authors' previous GP
                 approach.",
}

@InCollection{taylor:1994:lamarckian,
  author =       "Stewart Taylor",
  title =        "Using {Lamarckian} Evolution to Increase the
                 Effectiveness of Neural Network Training with a Genetic
                 Algorithm and Backpropagation",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "181--186",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-182105-2",
  notes =        "This volume contains 22 papers written and submitted
                 by students describing their term projects for the
                 course in artificial life (Computer Science 425) at
                 Stanford University offered during the spring quarter
                 quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InCollection{taylor:1994:juggling,
  author =       "Stewart N. Taylor",
  title =        "Evolution by Genetic Programming of a Spatial Robot
                 Juggling Algorithm",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "160--169",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "See stewart:1995:juggling

                 3 result producing branches + 3 ADFs

                 This volume contains 20 papers written and submitted by
                 students describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{stewart:1995:juggling,
  author =       "Stewart N. Taylor",
  title =        "Evolution by Genetic Programming of a Spatial Robot
                 Juggling Control Algorithm",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "104--110",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming, multi-tree",
  size =         "7 pages",
  abstract =     "Keeps a ball aloft by hitting it with a hard paddle
                 and a particular {"}read-life{"} robot model.",
  notes =        "3 result producing branches plus 2+ ADFs. {"}Fitness
                 measure is subject to a fair ammount of deception{"}
                 part of rosca:1995:ml See taylor:1994:juggling",
}

@PhdThesis{TJTaylor:thesis,
  author =       "Timothy John Taylor",
  title =        "From Artificial Evolution to Artificial Life",
  school =       "Division of Informatics, University of Edinburgh",
  year =         "1999",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.dai.ed.ac.uk/daidb/homes/timt/papers/thesis/",
  URL =          "http://www.dai.ed.ac.uk/daidb/homes/timt/papers/thesis/thesis.ps.gz",
  size =         "317 pages",
  abstract =     "This work addresses the question: What are the basic
                 design considerations for creating a synthetic model of
                 the evolution of living systems (i.e. an `artificial
                 life' system)? It can also be viewed as an attempt to
                 elucidate the logical structure (in a very general
                 sense) of biological evolution. However, with no
                 adequate definition of life, the experimental portion
                 of the work concentrates on more specific issues, and
                 primarily on the issue of open-ended evolution. An
                 artificial evolutionary system called Cosmos, which
                 provides a virtual operating system capable of
                 simulating the parallel processing and evolution of a
                 population of several thousand self-reproducing
                 computer programs, is introduced. Cosmos is related to
                 Ray's established Tierra system, but there are a number
                 of significant differences. A wide variety of
                 experiments with Cosmos, which were designed to
                 investigate its evolutionary dynamics, are reported. An
                 analysis of the results is presented, with particular
                 attention given to the role of contingency in
                 determining the outcome of the runs. The results of
                 this work, and consideration of the existing literature
                 on artificial evolutionary systems, leads to the
                 conclusion that artificial life models such as this are
                 lacking on a number of theoretical and methodological
                 grounds. It is emphasised that explicit theoretical
                 considerations should guide the design of such models,
                 if they are to be of scientific value. An analysis of
                 various issues relating to self-reproduction,
                 especially in the context of evolution, is presented,
                 including some extensions to von Neumann's analysis of
                 self-reproduction. This suggests ways in which the
                 evolutionary potential of such models might be
                 improved. In particular, a shift of focus is
                 recommended towards a more careful consideration of the
                 phenotypic capabilities of the reproducing individuals.
                 Phenotypic capabilities fundamentally involve
                 interactions with the environment (both abiotic and
                 biotic), and it is further argued that the theoretical
                 grounding upon which these models should be based must
                 include consideration of the kind of environments and
                 the kind of interactions required for open-ended
                 evolution. A number of useful future research
                 directions are identified. Finally, the relevance of
                 such work to the original goal of modelling the
                 evolution of living systems (as opposed to the more
                 general goal of modelling open-ended evolution) is
                 discussed. It is suggested that the study of open-ended
                 evolution can lead us to a better understanding of the
                 essential properties of life, but only if the questions
                 being asked in these studies are phrased
                 appropriately.",
}

@InProceedings{tchernev:1998:fxNm,
  author =       "Elko Tchernev",
  title =        "Forth Crossover Is Not a Macromutation?",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "381--386",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{tchernev:2002:gecco:lbp,
  title =        "Stack-Correct Crossover Methods in Genetic
                 Programming",
  author =       "Elko Tchernev",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "443--449",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming, stack-based
                 GP",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 many types of crossover, two boxes, sextic polynomial
                 problems",
}

@InCollection{tea:2000:GAATSP,
  author =       "Hakara Tea",
  title =        "Genetic Algorithms Applied to the Traveling Salesman
                 Problem",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "397--406",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@Unpublished{hampo:toolkit,
  author =       "C. Tebbe and R. J. Hampo and B. D. Bryant and K. A.
                 Marko",
  title =        "Genetic Programming Toolkit",
  year =         "1995?",
  note =         "Ford Proprietary",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Ford Motor Company Research program (C++) for solving
                 automotive problems with genetic programming. Software
                 and manual.",
}

@PhdThesis{teich:thesis,
  author =       "Tobias Teich",
  title =        "Optimierung von Maschinenbelegungsplänen unter
                 Benutzung heuristischer Verfahren",
  school =       "Department of of Economics, Technical University of
                 Chemnitz",
  year =         "1998",
  address =      "Germany",
  month =        "30 " # jun,
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
  notes =        "

                 ",
}

@InProceedings{Teller:1993:lmm,
  author =       "A. Teller",
  title =        "Learning mental models",
  booktitle =    "Proceedings of the Fifth Workshop on Neural Networks:
                 An International Conference on Computational
                 Intelligence: Neural Networks, Fuzzy Systems,
                 Evolutionary Programming, and Virtual Reality",
  year =         "1993",
  organisation = "The Society for Computer Simulation",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/LearnModels.ps",
  keywords =     "genetic algorithms, genetic programming, memory",
  abstract =     "The process of learning is not always as simple as
                 mapping inputs to the best outputs. Often internal
                 state is needed to distinguish between observably
                 identical states of the world. Genetic programming has
                 concentrated on solving problems in the
                 functional/reactive arena, in part because of the
                 absence of a natural way to incorporate memory into the
                 paradigm. This paper presents a simple addition to the
                 genetic programming paradigm that seamlessly
                 incorporates the evolution of the effective gathering,
                 storage, and retrieval of arbitrarily complicated state
                 information. Experimental results show that the
                 effective production and use of complex state
                 structures can be evolved and that agents evolving the
                 use of memory quickly and permanently displace purely
                 reactive and non-deterministic functions. These results
                 may not only aid future research into the causes and
                 constituents of mental models but may expand the types
                 of problems that can be practically tackled by genetic
                 programming.",
  notes =        "You can get these papers by anonymous ftp to any CMU
                 machine. (e.g. GS61.SP.CS.CMU.EDU (128.2.203.143) or
                 J.GP.CS.CMU.EDU (128.2.250.198))

                 then cd to /afs/cs/usr/astro/public/papers/

                 Since several come from the Mac, they won't work in
                 GhostView, but they should print fine.

                 Tartarus",
}

@InCollection{kinnear:teller,
  author =       "Astro Teller",
  title =        "The Evolution of Mental Models",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/MentalModels.ps",
  chapter =      "9",
  pages =        "199--219",
  size =         "~20pages",
  abstract =     "Most interesting problems do not have solutions that
                 are simple mappings from the inputs to the correct
                 outputs; some kind of internal state or memory is
                 needed to operate well or optimally in these domains.
                 Traditionally, genetic programming has concentrated on
                 solving problems in the functional/reactive arena. This
                 may be due in part to the absence of a natural way to
                 incorporate memory into the paradigm. This chapter
                 proposes a simple, Turing-complete addition to the
                 genetic programming paradigm that seamlessly
                 incorporates the evolution of the effective gathering,
                 storage, and retrieval of arbitrarily complicated state
                 information. A new environment is presented and used to
                 evaluate this addition to the paradigm. Experimental
                 results show that the effective production and use of
                 complex memory structures can be evolved and that
                 functions evolving the intelligent use of state quickly
                 and permanently displace purely reactive and
                 non-deterministic functions. These results may aid
                 future research into the causes and constituents of
                 mental models and are shown to open the field of
                 genetic programming to include all learning strategies
                 that are Turing-possible.",
  notes =        "Addition of 20 memory elements via READ and WRITE to
                 box pushing inside a matrix of 6*6 cells You can get
                 these papers by anonymous ftp to any CMU machine. (e.g.
                 GS61.SP.CS.CMU.EDU (128.2.203.143) or J.GP.CS.CMU.EDU
                 (128.2.250.198))

                 then cd to /afs/cs/usr/astro/public/papers/

                 Since several come from the Mac, they won't work in
                 GhostView, but they should print fine.

                 ",
}

@InProceedings{fairs94:teller,
  author =       "Astro Teller",
  title =        "Genetic Programming, Indexed memory, the Halting
                 problem, and other curiosities",
  booktitle =    "Proceedings of the 7th annual Florida Artificial
                 Intelligence Research Symposium",
  year =         "1994",
  pages =        "270--274",
  address =      "Pensacola, Florida, USA",
  month =        may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  url2 =         "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Curiosities.ps",
  URL =          "ftp://cs.ucl.ac.uk/genetic/ftp.io.com/papers/Curiosities.ps",
  abstract =     "The genetic programming (GP) paradigm was designed to
                 evolve functions that are progressively better
                 approximations to some target function. The
                 introduction of memory into GP has opened the Pandora's
                 box which is algorithms. It has been shown that the
                 combination of GP and Indexed Memory can be used to
                 evolve any target algorithm. What has not been shown is
                 the practicality of doing so. This paper addresses some
                 of the fundamental issues in the process of evolving
                 algorithms and proposes a variety of partial solutions,
                 in general and for GP in particular.",
  notes =        "You can get these papers by anonymous ftp to any CMU
                 machine. (e.g. GS61.SP.CS.CMU.EDU (128.2.203.143) or
                 J.GP.CS.CMU.EDU (128.2.250.198))

                 then cd to /afs/cs/usr/astro/public/papers/

                 Since several come from the Mac, they won't work in
                 GhostView, but they should print fine.

                 Discuses anytime algorithm for extracting {"}answer{"}
                 from evolved program via its use of indexed memory.",
  size =         "5 pages",
}

@InProceedings{wcci94:teller,
  author =       "Astro Teller",
  title =        "Turing Completeness in the Language of Genetic
                 Programming with Indexed Memory",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "136--141",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Turing.ps",
  size =         "6 pages",
  abstract =     "Genetic Programming is a method for evolving functions
                 that find approximate or exact solutions to problems.
                 There are many problems that traditional Genetic
                 Programming (GP) cannot solve, due to the theoretical
                 limitations of its paradigm. A Turing machine (TM) is a
                 theoretical abstraction that express the extent of the
                 computational power of algorithms. Any system that is
                 Turing complete is sufficiently powerful to recognize
                 all possible algorithms. GP is not Turing complete.
                 This paper will prove that when GP is combined with the
                 technique of indexed memory, the resulting system is
                 Turing complete. This means that, in theory, GP with
                 indexed memory can be used to evolve any algorithm.",
  notes =        "Proof that Language of GP+Indexed Memory is Turing
                 Complete, Nb does NOT show GP+IM itself will solve
                 anything. You can get these papers by anonymous ftp to
                 any CMU machine. (e.g. GS61.SP.CS.CMU.EDU
                 (128.2.203.143) or J.GP.CS.CMU.EDU
                 (128.2.250.198))

                 then cd to /afs/cs/usr/astro/public/papers/

                 Since several come from the Mac, they won't work in
                 GhostView, but they should print fine.",
}

@TechReport{TechTeller,
  author =       "Astro Teller and Manuela Veloso",
  institution =  "Department of Computer Science, Carnegie Mellon
                 University",
  address =      "Pittsburgh, PA, USA",
  title =        "{PADO}: Learning Tree Structured Algorithms for
                 Orchestration into an Object Recognition System",
  number =       "CMU-CS-95-101",
  year =         "1995",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/PADO-Tech-Report.ps",
  keywords =     "genetic algorithms, genetic programming, memory",
  size =         "2.54 Mbytes",
  abstract =     "Around the world there are innumerable databases of
                 information. The quantity of information available has
                 created a high demand for automatic methods for
                 searching these databases and extracting specific kinds
                 of information. Unfortunately, the information in these
                 databases increasingly contains signals that have no
                 corresponding classification symbols. Examples include
                 databases of images, sounds, etc. A few systems have
                 been written to help solve these search and retrieve
                 issues. But we can not write a new system for every
                 kind of signal we want to recognize and extract. Some
                 work has been done on automating (i.e. learning) the
                 task of identifying desired signal elements. It would
                 be useful to automate (learn) not just a part of the
                 classification function, but the entire signal
                 identification program. It would be helpful if we could
                 use the same learning architecture to automatically
                 create these programs for distinguishing many different
                 classes of the same signal type. It would be better
                 still if we could use the same learning architecture to
                 create these programs even for signal types as
                 different as images and sound waves. We introduce PADO
                 (Parallel Architecture Discovery and Orchestration), a
                 learning architecture designed to deliver this. PADO
                 has at its core a variant of genetic programming (GP)
                 that extends the paradigm to explore the space of
                 algorithms. PADO learns the entire classification
                 algorithm for an arbitrary signal type with arbitrary
                 signal class distinctions. This architecture has been
                 designed specifically for signal understanding and
                 classification. The architecture of PADO and its
                 achievements on the recovery of visual and acoustic
                 signal classes from test databases are the subjects of
                 this article. Keywords: Machine Learning, Signal
                 Understanding, Data Mining, Genetic Programming,
                 Algorithm Evolution",
  notes =        "You can get these papers by anonymous ftp to any CMU
                 machine. (e.g. GS61.SP.CS.CMU.EDU (128.2.203.143) or
                 J.GP.CS.CMU.EDU (128.2.250.198))

                 then cd to /afs/cs/usr/astro/public/papers/

                 Since several come from the Mac, they won't work in
                 GhostView, but they should print fine.

                 Our printer barfed at page 10! This appears to be very
                 close to teller:1995:PADO",
}

@Article{Teller-ESJ,
  author =       "Astro Teller and Manuela Veloso",
  title =        "Program Evolution for Data Mining",
  editor =       "Sushil Louis",
  publisher =    "JAI Press",
  journal =      "The International Journal of Expert Systems",
  year =         "1995",
  volume =       "8",
  number =       "3",
  pages =        "216--236",
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Astro-ESJ.ps",
  url_2 =        "ftp://cs.ucl.ac.uk/genetic/papers/Astro-ESJ.ps.Z",
  size =         "21 pages",
  abstract =     "Around the world there are innumerable databases of
                 information. The quantity of information available has
                 created a high demand for automatic methods for
                 searching these databases and extracting specific kinds
                 of information. Unfortunately, the information in these
                 databases increasingly contains signals that have no
                 corresponding classification symbols. Examples include
                 databases of images, sounds, etc. A few systems have
                 been written to help solve these search and retrieve
                 issues. But we can not write a new system for every
                 kind of signal we want to recognize and extract. Some
                 work has been done on automating (i.e. learning) the
                 task of identifying desired signal elements. It would
                 be useful to automate (learn) not just a part of the
                 classification function, but the entire signal
                 identification program. It would be helpful if we could
                 use the same learning architecture to automatically
                 create these programs for distinguishing many different
                 classes of the same signal type. It would be better
                 still if we could use the same learning architecture to
                 create these programs even for signal types as
                 different as images and sound waves. We introduce PADO
                 (Parallel Architecture Discovery and Orchestration), a
                 learning architecture designed to deliver this. PADO
                 has at its core a variant of genetic programming (GP)
                 that extends the paradigm to explore the space of
                 algorithms. PADO learns the entire classification
                 algorithm for an arbitrary signal type with arbitrary
                 signal class distinctions. This architecture has been
                 designed specifically for signal understanding and
                 classification. The architecture of PADO and its
                 achievements on the recovery of visual and acoustic
                 signal classes from test databases are the subjects of
                 this article.

                 ",
  notes =        "Third Quarter. Special Issue on Genetic Algorithms and
                 Knowledge Bases.",
}

@InCollection{teller:1995:PADO,
  author =       "Astro Teller and Manuela Veloso",
  title =        "{PADO}: {A} New Learning Architecture for Object
                 Recognition",
  booktitle =    "Symbolic Visual Learning",
  publisher =    "Oxford University Press",
  year =         "1996",
  editor =       "Katsushi Ikeuchi and Manuela Veloso",
  pages =        "81--116",
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/PADO.ps",
  abstract =     "Most artificial intelligence systems today work on
                 simple problems and artificial domains because they
                 rely on the accurate sensing of the task world. Object
                 recognition is a crucial part of the sensing challenge
                 and machine learning stands in a position to catapult
                 object recognition into real world domains. Given that,
                 to date, machine learning has not delivered general
                 object recognition, we propose a different point of
                 attack: the learning architectures themselves. We have
                 developed a method for directly learning and combining
                 algorithms in a new way that imposes little burden on
                 or bias from the humans involved. This learning
                 architecture, PADO, and the new results it brings to
                 the problem of natural image object recognition is the
                 focus of this chapter.",
  notes =        "This is NOT the same as TechTeller. The overlap is
                 about 20 of the 34 pages but it is different enough",
  size =         "34 pages",
}

@InProceedings{Teller-EPIA,
  author =       "Astro Teller and Manuela Veloso",
  title =        "A Controlled Experiment: Evolution for Learning
                 Difficult Image Classification",
  booktitle =    "Seventh Portuguese Conference On Artificial
                 Intelligence",
  year =         "1995",
  publisher =    "Springer-Verlag",
  series =       "Lecture Notes in Computer Science",
  volume =       "990",
  pages =        "165--176",
  address =      "Funchal, Madeira Island, Portugal",
  month =        oct # " 3-6",
  keywords =     "genetic algorithms, genetic programming, memory",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/TellerVelosoEPIA.ps",
  abstract =     "The signal-to-symbol problem is the task of converting
                 raw sensor data into a set of symbols that Artificial
                 Intelligence systems can reason about. We have
                 developed a method for directly learning and combining
                 algorithms that map signals into symbols. This new
                 method is based on evolutionary computation and imposes
                 little burden on or bias from the humans involved.
                 Previous papers of ours have focused on PADO, our
                 learning architecture. We showed how it applies to the
                 general signal-to-symbol task and in particular the
                 impressive results it brings to natural image object
                 recognition. The most exciting challenge this work has
                 received is the idea that PADO's success in natural
                 image object recognition may be due to the underlying
                 simplicity of the problems we posed it. This implicitly
                 assumes that our approach may suffer from many of same
                 afflictions that traditional computer vision approaches
                 suffer in natural image object recognition. This paper
                 responds to this challenge by designing and executing a
                 controlled experiment specifically designed to solidify
                 PADO's claim to success.",
  notes =        "EPIA'95

                 ",
}

@InProceedings{teller:1995:db,
  author =       "Astro Teller",
  title =        "The Discovery of Algorithms for Automatic Database
                 Retrieval",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "76--88",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming, memory",
  size =         "13 pages",
  abstract =     "PADO",
  notes =        "part of rosca:1995:ml",
}

@InProceedings{teller-FSS-GP,
  author =       "Astro Teller",
  title =        "Language Representation Progression in Genetic
                 Programming",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "106--113",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming, memory",
  size =         "7 pages",
  abstract =     "The signal-to-symbol problem is the task of converting
                 raw sensor data into a set of symbols that Artificial
                 Intelligence systems can reason about. We have
                 developed a method for directly learning and combining
                 algorithms that map signals into symbols. This new
                 method is based on Genetic Programming (GP). Previous
                 papers have focused on PADO, our learning architecture.
                 We showed how PADO applies to the general
                 signal-to-symbol task and in particular the positive
                 results it brings to natural image object recognition.
                 Originally, PADO's programs were written in a Lisp-like
                 language formulated in~\cite{teller2}. PADO's programs
                 are now written in a very different language. Using
                 this new language, PADO's performance has increased
                 substantially on several domains including two vision
                 domains this paper will mention. This paper will
                 discuss these two language representations, the results
                 they produced, and some analysis of the performance
                 improvement. The higher level goals of this paper are
                 to give some justification for PADO's specific language
                 progression, some explanation for the improved
                 performance this progression generated, and to offer
                 PADO's new language representation as an advancement in
                 GP.",
  notes =        "AAAI-95f GP

                 {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457
                 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InProceedings{Teller-ICEC-95,
  author =       "Astro Teller and Manuela Veloso",
  title =        "Algorithm Evolution for Face Recognition: What Makes a
                 Picture Difficult",
  booktitle =    "International Conference on Evolutionary Computation",
  year =         "1995",
  pages =        "608--613",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "1--3 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, memory,
                 computer vision",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/icecFinal.ps",
  size =         "6 pages",
  abstract =     "One of the classic problems in computer vision is the
                 face recognition problem. In general this problem can
                 take on a wide variety of forms, but the most common
                 face recognition problem is ``Who is this a picture
                 of?'' Evolution computation has, in the past, been
                 applied indirectly to this problem through techniques
                 like learning Neural Networks. This paper introduces a
                 Genetic Programming style approach to learning
                 algorithms that directly investigate face images and
                 are coordinated into a face recognition system. Through
                 a series of experiments, we will show that evolved
                 algorithms can accomplish the face recognition task. We
                 will also highlight several pitfalls and misconceptions
                 surrounding face recognition as a learning problem.",
  notes =        "ICEC-95 PADO

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html

                 ",
}

@InCollection{teller:1996:aigp2,
  author =       "Astro Teller",
  title =        "Evolving Programmers: The Co-evolution of Intelligent
                 Recombination Operators",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "45--68",
  chapter =      "3",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming,memory",
  ISBN =         "0-262-01158-1",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AIGPII.ps",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/chapterII/chapterII.html",
  abstract =     "The genetic programming process searches over a
                 fitness landscape. The shape of this landscape is
                 determined by the task to be solved and the
                 representation in which the population members are
                 expressed. The movement through this space is
                 determined by the operators that act to recombine the
                 population members. These factors make it imperative
                 that our search for increased power and understanding
                 in genetic programming include the study and
                 improvement of representations and operators. This
                 chapter describes a process for learning SMART
                 recombination programs in a co-evolutionary process and
                 a new representation for the evolution of algorithms.
                 How these SMART operator programs are created, how they
                 act, how they co-evolve with a main population of
                 programs, and experimental results on their use are the
                 subjects of this chapter.

                 ",
  notes =        "PADO + SMART recombination html version available from
                 http://www.cs.cmu.edu/~astro/",
}

@InProceedings{teller:1996:npirp,
  author =       "Astro Teller and Manuela Veloso",
  title =        "Neural Programming and an Internal Reinforcement
                 Policy",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "186--192",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming, ANN",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{teller:1996:npirpSV,
  author =       "Astro Teller and Manuela Veloso",
  title =        "Neural Programming and an Internal Reinforcement
                 Policy",
  booktitle =    "International Conference Simulated Evolution and
                 Learning",
  year =         "1996",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, ANN",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AS.ps",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/astroseal/astro/seal.html",
  size =         "8 pages",
  abstract =     "An important reason for the continued popularity of
                 Artificial Neural Networks (ANNs) in the machine
                 learning community is that the gradient-descent
                 backpropagation procedure gives ANNs a locally optimal
                 change procedure and, in addition, a framework for
                 understanding the ANN learning performance. Genetic
                 programming (GP) is also a successful evolutionary
                 learning technique that provides powerful parameterized
                 primitive constructs. Unlike ANNs, though, GP does not
                 have such a principled procedure for changing parts of
                 the learned system based on its current performance.
                 This paper introduces Neural Programming, a
                 connectionist representation for evolving programs that
                 maintains the benefits of GP. The connectionist model
                 of Neural Programming allows for a regression
                 credit-blame procedure in an evolutionary learning
                 system. We describe a general method for an informed
                 feedback mechanism for Neural Programming, Internal
                 Reinforcement. We introduce an Internal Reinforcement
                 procedure and demonstrate its use through an
                 illustrative experiment.",
  notes =        "html version available from
                 http://www.cs.cmu.edu/~astro/ SEAL, PADO bucket-brigade
                 IRNP reach given level of performace in 30% of
                 generations taken by NP",
}

@InProceedings{Teller:1997:acnfc,
  author =       "Astro Teller and David Andre",
  title =        "Automatically Choosing the Number of Fitness Cases:
                 The Rational Allocation of Trials",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "321--328",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/GR.ps",
  size =         "8 pages",
  abstract =     "For many problems to which genetic programming has
                 been applied, choosing the number of fitness cases with
                 which to evaluate the individuals is a crucial
                 decision. If too few fitness cases are used,
                 overfitting may occur, and the measured fitness of an
                 individual may not be representative of its true
                 fitness. On the other hand, if too many fitness cases
                 are used, a great deal of computer time can be wasted.
                 This paper presents a method for the Rational
                 Allocation of Trials (RAT) that dynamically allocates a
                 boundedly optimal number of fitness cases for each
                 individual. RAT allocates individuals to tournaments
                 prior to their evaluation, and then, borrowing from
                 previous work in model selection, allocates trials
                 (fitness cases) only to those individuals for whom the
                 cost of evaluating another fitness case is outweighed
                 by the expected utility that the new information will
                 provide. For most evolutionary computation approaches,
                 including genetic programming, and for most problems,
                 the RAT algorithm will provide significant time savings
                 at minimal additional system complexity.",
  notes =        "GP-97",
}

@InProceedings{teller:1997:aesu,
  author =       "Astro Teller",
  title =        "Algorithm Evolution for Signal Understanding",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "299",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@PhdThesis{AstroTeller:thesis,
  author =       "Astro Teller",
  title =        "Algorithm Evolution with Internal Reinforcement for
                 Signal Understanding",
  school =       "School of Computer Science, Carnegie Mellon
                 University",
  year =         "1998",
  address =      "Pittsburgh, USA",
  month =        "5 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/thesis.ps",
  size =         "5.9 Mbytes, 166 pages",
  abstract =     "Automated program evolution has existed in some form
                 for almost forty years. Signal understanding (e.g.,
                 signal classification) has been a scientific concern
                 for longer than that. Generating a general machine
                 learning signal understanding system has more recently
                 attracted considerable research interest. First, this
                 thesis defines and creates a general machine learning
                 approach for signal understanding independent of the
                 signal's type and size. This is accomplished through an
                 evolutionary strategy of signal understanding programs
                 that is an extension of genetic programming. Second,
                 this thesis introduces a suite of sub-mechanisms that
                 increase the power of genetic programming and
                 contribute to the understanding of the learning
                 technique developed. The central algorithmic innovation
                 of this thesis is the process by which a novel
                 principled credit-blame assignment is introduced and
                 incorporated into the evolution of algorithms, thus
                 improving the evolutionary process. This principled
                 credit-blame assignment is done through a new program
                 representation called neural programming and applied
                 through a set of principled processes collectively
                 called internal reinforcement in neural programming.
                 This thesis concentrates on these algorithmic
                 innovations in real world signal domains where the
                 signals are typically large and/or poorly understood.
                 This evolutionary learning of algorithms takes place in
                 PADO, a system developed in this thesis for ``parallel
                 algorithm discovery and orchestration'' and as a
                 demonstrably effective strategy for divide-and-conquer
                 in signal classification domains. This thesis includes
                 an extensive empirical evaluation of the techniques
                 developed in a rich variety of real-world signals. The
                 results obtained demonstrate, among other things, the
                 effectiveness of principled credit-blame assignment in
                 algorithm evolution. This work is unique in three
                 aspects. No other currently existing system can learn
                 to classify or otherwise ``symbolize'' signals with no
                 space or size penalties for the signal's size or type.
                 No other system based on genetic programming currently
                 exists that purposefully generates and orchestrates a
                 variety of experts along problem specific lines. And,
                 most centrally, the thesis introduces the first
                 analytically sound mechanism for explaining and
                 reinforcing specific parts of an evolving program. The
                 goal of this thesis is to argue, explain, and
                 demonstrate how representation and search are
                 intimately connected in evolutionary computation and to
                 address these dual concerns in the context of the
                 evolution of Turing complete programs. Ideally, this
                 thesis will inspire future research in this same area
                 and along similar lines.",
  notes =        "Publication Number: CMU-CS-98-132",
}

@InCollection{teller:1999:aigp3,
  author =       "Astro Teller",
  title =        "The Internal Reinforcement of Evolving Algorithms",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "14",
  pages =        "325--354",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@Article{Teller:2000:AI,
  author =       "Astro Teller and Manuela Veloso",
  title =        "Internal reinforcement in a connectionist genetic
                 programming approach",
  journal =      "Artificial Intelligence",
  volume =       "120",
  pages =        "165--198",
  year =         "2000",
  number =       "2",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6TYF-40TY77M-1/1/c54fc0ab842b831a76c9e61e1c1c6b85",
  abstract =     "Genetic programming (GP) can learn complex concepts by
                 searching for the target concept through evolution of a
                 population of candidate hypothesis programs. However,
                 unlike some learning techniques, such as Artificial
                 Neural Networks (ANNs), GP does not have a principled
                 procedure for changing parts of a learned structure
                 based on that structure's performance on the training
                 data. GP is missing a clear, locally optimal update
                 procedure, the equivalent of gradient-descent
                 backpropagation for ANNs. This article introduces a new
                 algorithm, {"}internal reinforcement{"}, for defining
                 and using performance feedback on program evolution.
                 This internal reinforcement principled mechanism is
                 developed within a new connectionist representation for
                 evolving parameterized programs, namely {"}neural
                 programming{"}. We present the algorithms for the
                 generation of credit and blame assignment in the
                 process of learning programs using neural programming
                 and internal reinforcement. The article includes a
                 comprehensive overview of genetic programming and
                 empirical experiments that demonstrate the increased
                 learning rate obtained by using our principled program
                 evolution approach.",
}

@InProceedings{terashima-marin:1999:ECSSET,
  author =       "Hugo Terashima-Marin and Peter Ross and Manuel
                 Valenzuela-Rendon",
  title =        "Evolution of Constraint Satisfaction Strategies in
                 Examination Timetabling",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "635--642",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{teredesai:2001:EuroGP,
  author =       "Ankur Teredesai and J. Park and Venugopal
                 Govindaraju",
  title =        "Active Handwritten Character Recognition using Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "371--379",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Pattern
                 Recognition, Active Character Recognition, Digit
                 Recognition, Handwritten digit classification",
  ISBN =         "3-540-41899-7",
  size =         "10 pages",
  abstract =     "This paper is intended to demonstrate the effective
                 use of genetic programming in handwritten character
                 recognition. When the resources used by the classifier
                 increase incrementally and depend on the complexity of
                 classification task, we term such a classifier as
                 active. The design and implementation of active
                 classifiers based on genetic programming principles
                 becomes very simple and efficient. Genetic Programming
                 has helped optimize handwritten character recognition
                 problem in terms of feature set selection. We propose
                 an implementation with dynamism in pre-processing and
                 classification of handwritten digit images. This
                 paradigm will supplement existing methods by providing
                 better performance in terms of accuracy and processing
                 time per image for classification. Different levels of
                 informative detail can be present in image data and our
                 proposed paradigm helps highlight these information
                 rich zones. We compare our performance with passive and
                 active handwritten digit classification schemes that
                 are based on other pattern recognition techniques.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{tettamanzi:1996:GP-f,
  author =       "Andrea G. B. Tettamanzi",
  title =        "Genetic Programming without Fitness",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "193--195",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InCollection{Teunen:1997:apgoGP,
  author =       "Remco Teunen",
  title =        "Automatic Pronunciation Generation from Orthography
                 using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "207--215",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "phonetic translation of a given word",
  notes =        "part of koza:1997:GAGPs",
}

@Misc{teuscher:1999:RPSFATRE,
  author =       "Christof Teuscher",
  title =        "Romero's Pilgrimage to Santa Fe: {A} Tale of Robot
                 Evolution",
  booktitle =    "GECCO-99 Student Workshop",
  year =         "1999",
  editor =       "Una-May O'Reilly",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithm, evolutionary programming,
                 robotics",
  URL =          "http://www.teuschers.ch/christof/romero.html",
}

@InProceedings{tezuka:1999:A,
  author =       "Masaru Tezuka and Masahiro Hiji",
  title =        "A genetic algorithm approach to improve production
                 schedule",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "254--259",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InCollection{thedens:1994:ddbd,
  author =       "Daniel R. Thedens",
  title =        "Detector Design by Genetic Programming for Automated
                 Border Definition in Cardiac Magnetic Resonance
                 Images",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "170--179",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{thierens:1999:ESNGP,
  author =       "Dirk Thierens",
  title =        "Estimating the Significant Non-Linearities in the
                 Genome Problem-Coding",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "643--648",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{thomas:1999:isvGPdmfd,
  author =       "James D Thomas and Katia Sycara",
  title =        "The importance of simplicity and validation in genetic
                 programming for data mining in financial data",
  booktitle =    "Data Mining with Evolutionary Algorithms: Research
                 Directions",
  year =         "1999",
  editor =       "Alex Alves Freitas",
  pages =        "7--11",
  address =      "Orlando, Florida",
  publisher_address = "445 Burgess Drive, Menlo Park, California 94025,
                 USA",
  month =        "18 " # jul,
  publisher =    "AAAI Press",
  note =         "Technical Report WS-99-06",
  keywords =     "genetic algorithms, genetic programming, data mining",
  ISBN =         "1-57735-090-1",
  notes =        "Joint AAAI-99 & GECCO-99 Workshop. Workshop
                 information at http://www.ppgia.pucpr.br/~dmea/",
}

@MastersThesis{Thomemann:1992:masters,
  author =       "U. W. Thonemann",
  title =        "Verbesserung des Simulated Annealing unter Anwendung
                 Genetischer Programmierung am Beispiel des Diskreten
                 Quadratischen Layoutproblems",
  school =       "University of Paderborn, Germany",
  year =         "1992",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@InProceedings{thompson:1996:SiE,
  author =       "Adrian Thompson",
  title =        "Silicon Evolution",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "444--452",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.cogs.susx.ac.uk/users/adrianth/gp96/paper.ps.Z",
  notes =        "GP-96 GA paper",
}

@InCollection{thompson:1995:AYRSFGPRCFT,
  author =       "Howard Thompson",
  title =        "Are Your Ready for Some Football? Genetically Produced
                 Ratings for College Football Teams",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "279--290",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{Thonemann:1994:SAGP,
  author =       "Ulrich Wilhelm Thonemann",
  title =        "Finding improved simulated annealing schedules with
                 genetic programming",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "391--395",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, simulated
                 annealing, quadratic assignment problem QAP.",
  size =         "6 pages",
  notes =        "Uses GP to generate cooling schedule for simulated
                 annealing. Demonstrates this on a series of QAP and
                 compares very favorably with published QAP results. GP
                 fitness found by runining simulated annealing, so end
                 up doing loads of work. Best cooling schedules found
                 are problem dependant but several are highly
                 oscillitary and most dont drop to zero!",
}

@Article{bolte:1996:oSAsGP,
  author =       "A Bolte and U W Thonemann",
  title =        "Optimizing Simulated Annealing Schedules with Genetic
                 Programming",
  journal =      "European Journal of Operational Research",
  year =         "1996",
  volume =       "92",
  number =       "2",
  pages =        "402--416",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{tian:1999:ASSABFSEV,
  author =       "Yajie Tian and Nobuo Sannomiya and Toru Yokokura",
  title =        "A Simulation Study on Adaptive Behavior of Fish
                 Schools under Environmental Variation",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1451",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{todd:1999:DMOSDSUGANN,
  author =       "David S. Todd and Pratyush Sen",
  title =        "Directed Multiple Objective Search of Design Spaces
                 Using Genetic Algorithms and Neural Networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1738--1743",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{tomassini:ppsn2002:pp641,
  author =       "Marco Tomassini and Leonardo Vanneschi and Francisco
                 Fern'andez and Germ'an Galeano",
  title =        "Experimental Investigation Of Three Distributed
                 Genetic Programming Models",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "641 ff.",
  year =         "2002",
  editor =       "J.-J. Merelo Guerv\'os and P. Adamidis and H.-G. Beyer
                 and J.-L. Fern\'andez-Villaca\~nas and H.-P. Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  note =         "Keywords: Implementation::Parallel EAs,
                 Technique::Genetic programming - general",
  annote =       "Available from
                 http://link.springer.de/link/service/series/0558/papers/2439/243900641.pdf",
}

@InProceedings{tomlinson:1999:OCCSIBRL,
  author =       "Andy Tomlinson and Larry Bull",
  title =        "On Corporate Classifier Systems: Increasing the
                 Benefits of Rule Linkage",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "649--656",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ICARCV2000Tongchim,
  author =       "Shisanu Tongchim and Prabhas Chongstitvatana",
  title =        "Nearest Neighbor Migration in Parallel Genetic
                 Programming for Automatic Robot Programming",
  booktitle =    "Proceedings of the Sixth International Conference on
                 Control, Automation, Robotics and Vision",
  year =         "2000",
  month =        dec,
  address =      "Singapore",
  keywords =     "genetic algorithms, genetic programming, Parallel
                 Genetic Programming, Mobile Robot Navigation",
  URL =          "http://www.cp.eng.chula.ac.th/faculty/pjw/paper/tongchim-226.pdf",
  abstract =     "This work presents a study of parallelization of
                 genetic programming for automatically creating a robot
                 control program in a mobile robot navigation problem. A
                 nearest neighbor migration topology is proposed to
                 reduce the communication time. This study compares the
                 performance both in terms of the solution quality and
                 the gain in execution time. The timing analysis is
                 investigated to give insight into the behavior of
                 parallel implementations. The results show that the
                 parallel algorithm with asynchronous migration using 10
                 processors is 32 times faster than the serial
                 algorithm.",
  notes =        "Citation from author",
}

@InProceedings{Tongchim:ASIAN99,
  author =       "Shisanu Tongchim and Prabhas Chongstitvatana",
  title =        "Asynchronous Migration in Parallel Genetic
                 Programming",
  booktitle =    "Proceedings of Asian Computing Science Conference",
  year =         "1999",
  editor =       "P. S. Thiagarajan and Roland Yap",
  volume =       "1742",
  series =       "LNCS",
  pages =        "388--389",
  address =      "Phuket, Thailand",
  publisher_address = "Berlin",
  month =        "10-12 " # dec,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-66856-X",
}

@InProceedings{topchy:2001:gecco,
  title =        "Faster Genetic Programming based on Local Gradient
                 Search of Numeric Leaf Values",
  author =       "Alexander Topchy and W. F. Punch",
  pages =        "155--162",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, gradient
                 optimization, algorithmic, differentiation, Baldwin
                 effect, Lamarckian learning, symbolic regression",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{toropov:1998:GPcsga,
  author =       "Vassili V. Toropov and Luis F. Alvarez",
  title =        "Application of Genetic Programming to the Choice of a
                 Structure of Global Approximations",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "387--390",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{Toropov:1998:,
  author =       "Vassili V. Toropov and Luis F. Alvarez",
  title =        "Application of Genetic Programming to the Choice of a
                 Structure of Multipoint Approximations",
  booktitle =    "1st ISSMO/NASA Internet Conf. on Approximations and
                 Fast Reanalysis in Engineering Optimization",
  year =         "1998",
  month =        jun # " 14-27",
  organisation = "ISSMO/NASA/AIAA",
  note =         "Published on a CD ROM",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.brad.ac.uk/staff/vtoropov/luis/paper.htm",
  notes =        "ISSMO at
                 http://www.aero.ufl.edu/~issmo/program.htm

                 Nice www page.

                 {"}The simplified model is characterized not only by
                 its structure (to be found by the GP) but also by a set
                 of tuning parameters a to be found by model tuning,
                 i.e. the least squares fitting of the model into the
                 set of values of the original response function:{"}
                 {"}The allocation of tuning parameters a to an
                 individual tree follows the basic algebraic rules. To
                 identify the parameters of the expression by the
                 nonlinear least-squares fitting, i.e. to solve the
                 optimization problem in (1), a combination of a GA and
                 a nonlinear mathematical programming method [9] is
                 used. The output of the GA is the initial guess for the
                 subsequent derivative-based optimization method which
                 amounts to a variation of the Newton's method in which
                 the Hessian matrix is approximated by the secant
                 (quasi-Newton) updating method. Once the technique
                 comes sufficiently close to a local solution, it
                 normally converges quite rapidly. To promote
                 convergence from poor starting guesses the algorithm
                 uses the adaptive update of the Hessian and,
                 consequently, the algorithm is reduced to either a
                 Gauss-Newton or Levenberg-Marquardt method.
                 {"}

                 {"}Three-bar truss optimization problem{"}

                 {"}The output of the algorithm still needs some manual
                 post-processing in order to get rid of those terms in
                 the expression that give a null or tiny contribution,
                 for example when the same value is added and
                 subtracted. It is then suggested to run the problem
                 several times in order to identify, by comparison, the
                 most likely components.{"}",
}

@Article{torresen:2002:GPEM,
  author =       "Jim Torresen",
  title =        "A Scalable Approach to Evolvable Hardware",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "3",
  pages =        "259--282",
  month =        sep,
  keywords =     "genetic algorithms, evolvable hardware, classifier
                 systems, digital logic, evolvable hardware, FPGA",
  ISSN =         "1389-2576",
  abstract =     "Evolvable Hardware (EHW) has been proposed as a new
                 method for designing systems for complex real-world
                 applications. However, so far, only relatively simple
                 systems have been shown to be evolvable. In this paper,
                 it is proposed that concepts from biology should be
                 applied to EHW techniques to make EHW more applicable
                 to solving complex problems. One such concept has led
                 to the increased complexity scheme presented, where a
                 system is evolved by evolving smaller sub-systems.
                 Experiments with two different tasks illustrate that
                 inclusion of this scheme substantially reduces the
                 number of generations required for evolution. Further,
                 for the prosthesis control task, the best performance
                 is obtained by the novel approach. The best circuit
                 evolved performs better than the best trained neural
                 network.",
  notes =        "Article ID: 5091791",
}

@InCollection{townsend:2000:SGWGPGA,
  author =       "Jason Townsend",
  title =        "Search in Grid World using Genetic Programming and
                 Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "407--414",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{Trenaman:1997:agentslb,
  author =       "Adrian Trenaman",
  title =        "A Framework for the Evolution of Autonomous Agents",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "246--254",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{trenaman:1999:FIEACGP,
  author =       "Adrian Trenaman",
  title =        "Further Investigations into the Evolution of Agents
                 with Concurrent Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1452",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, artificial
                 life, adaptive behavior and agents, poster papers",
  ISBN =         "1-55860-611-4",
  abstract =     "java, Tartarus, Dozer",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{trenaman-ai97,
  author =       "Adrian Trenaman",
  title =        "Towards the Evolution of Stateful Autonomous Agents",
  pages =        "56--63",
  editor =       "Paul Mc Kevitt",
  booktitle =    "Eight Irish Conference on Artificial Intelligence
                 (AI-97)",
  year =         "1997",
  volume =       "2",
  address =      "University of Ulster, Magee College, Derry, Northern
                 Ireland",
  publisher =    "University of Ulster at Coleraine",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{trenaman:1998:cGPueseapke,
  author =       "Adrian Trenaman",
  title =        "Concurrent Genetic Programming and the Use of Explicit
                 State to Evolve Agents in Partially-Known
                 Environments",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "391--398",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{trenaman:1998:cGPtpr,
  author =       "Adrian Trenaman",
  title =        "Concurrent Genetic Programming and the Tartarus
                 Problem Revisited",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{trenaman:1999:cGPtda,
  author =       "Adrian Trenaman",
  title =        "Concurrent Genetic Programming, Tartarus and Dancing
                 Agents",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'99",
  year =         "1999",
  editor =       "Riccardo Poli and Peter Nordin and William B. Langdon
                 and Terence C. Fogarty",
  volume =       "1598",
  series =       "LNCS",
  pages =        "270--282",
  address =      "Goteborg, Sweden",
  publisher_address = "Berlin",
  month =        "26-27 " # may,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65899-8",
  notes =        "EuroGP'99, part of poli:1999:GP",
}

@PhdThesis{trenaman:thesis,
  author =       "Adrian Trenaman",
  title =        "The Evolution of Autonomous Agents Using Concurrent
                 Genetic Programming",
  school =       "Department of Computer Science, National University of
                 Ireland, Maynooth",
  year =         "1999",
  address =      "Ireland",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/trenaman/at_thesis1.ps.gz",
  size =         "136 pages",
  abstract =     "This thesis addresses the issue of how computational
                 agents interact with and represent their environment in
                 order to effect goal-achieving behaviour. It argues
                 that the internal representations used by the agent to
                 describe objects in the world should be based on how
                 the agent perceives these objects and not necessarily
                 on the representations a human designer might impose. A
                 bottom-up methodology is proposed for the automatic
                 design of distributed algorithms and internal
                 representations to control autonomous agents. In
                 particular, this thesis proposes and evaluates a new
                 mechanism for the evolution of agents: {"}concurrent
                 genetic programming''. In this encoding scheme an agent
                 is controlled by a set of evolved programs that are
                 executed concurrently to yield an emergent control
                 algorithm for the agent. This encoding forms a natural
                 interpretation of the emergent principles of the
                 discipline of artificial life in an evolutionary
                 context, and so elucidates the ability of evolutionary
                 computation to create such emergent systems. The
                 performance of the approach is investigated as a
                 function of several parameters. These are: using
                 different numbers of programs in the agents, explicit
                 memory, distributed memory architectures, deterministic
                 and non-deterministic scheduling strategies, different
                 levels of granularity of concurrency, and the evolution
                 of scheduling strategy. These issues are investigated
                 through the application of concurrent genetic
                 programming to the standard Tartarus and Dozer
                 virtual-robotics benchmarks. It is shown that
                 concurrent genetic programming produces better agents
                 for these environments than a conventional genetic
                 programming approach. It does this by employing an
                 implicit form of state that supports the development of
                 cyclical behaviour strategies. Implicit representations
                 of the environment are acquired at an evolutionary
                 level rather than at the level of the agent's
                 experience. Although this form of internal
                 representation leads to fit agents, it does not exhibit
                 the formation of explicit models of the agent's
                 environment. Instead, it allows the development of a
                 form of internal state appropriate to achieving good
                 fitness.",
  notes =        "

                 ",
}

@Article{tsang:1998:eddie,
  author =       "Edward P. K. Tsang and Jin Li and James M. Butler",
  title =        "{EDDIE} beats the bookies",
  journal =      "Software: Practice and Experience",
  year =         "1998",
  volume =       "28",
  number =       "10",
  pages =        "1033--1043",
  keywords =     "genetic algorithms, genetic programming, finance,
                 forecasting, horse racing, investment",
  ISSN =         "0038-0644",
  URL =          "http://www3.interscience.wiley.com/cgi-bin/abstract/10007354/START",
  abstract =     "Investment involves the maximisation of return on ones
                 investment whilst minimising risk. Good forecasting,
                 which often requires expert knowledge, can help to
                 reduce risk. In this paper, we propose a genetic
                 programming-based system, EDDIE (Evolutionary Dynamic
                 Data Investment Evaluator), as a forecasting tool.
                 Genetic programming is inspired by evolution theory,
                 and has been demonstrated to be successful in other
                 areas. EDDIE interacts with the users and generates
                 decision trees, which can also be seen as rule sets. We
                 argue that EDDIE is suitable for forecasting because
                 apart from using the power of genetic programming to
                 efficiently search the space of decision trees, it
                 allows expert knowledge to be channelled into
                 forecasting, and it generates rules which can easily be
                 understood and verified. EDDIE has been applied to
                 horse racing and achieved outstanding results. When
                 experimented on 180 handicap races (real data) in the
                 UK, it out-performed other common strategies used in
                 horse race betting by great margins. The idea was then
                 extended to financial forecasting. When tested on
                 historical S&P-500 data EDDIE achieved a respectable
                 annual rate of return over a three and a half year
                 period. While luck may play a part in the success of
                 EDDIE, our experimental results do indicate that EDDIE
                 is a tool which deserves more research. © 1998 John
                 Wiley & Sons, Ltd.",
  notes =        "See also butler:1995:eddie",
}

@Article{Tsang:2000:JME,
  author =       "Edward P. K. Tsang and Jin Li and Sheri Markose and
                 Hakan Er and Abdel Salhi and Giulia Iori",
  title =        "{EDDIE} In Financial Decision Making",
  journal =      "Journal of Management and Economics",
  year =         "2000",
  pages =        "October",
  keywords =     "genetic algorithms, genetic programming, financial
                 forecasting",
  URL =          "http://privatewww.essex.ac.uk/~scher/EDDIE%20PROJ/Tsang-Eddie-JMgtEcon2000.doc",
  abstract =     "This paper gives an overview of the EDDIE project. It
                 describes the principles and applications of EDDIE in
                 making financial decisions, including applications to
                 share prices and indices forecasting and arbitrage.
                 EDDIE is designed as an interactive decision tool, not
                 a replacement of expert knowledge. Experts channel
                 their knowledge into the system through (a) selection
                 and preparation of data and (b) providing feedback to
                 EDDIE. EDDIE's main role is to explore interactions
                 between variables and to find thresholds for the
                 variables. Performance of EDDIE depends on both the
                 quality of the users' input and the efficiency of its
                 genetic programming based search engine.",
}

@InProceedings{tseng:1999:M,
  author =       "Chris Tseng and Arkady Epshteyn",
  title =        "Modular learning with genetic aggregation ({MOLGA}) in
                 data prediction",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "260--267",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{tsutsui:1999:MRSCRCGA,
  author =       "Shigeyoshi Tsutsui and Masayuki Yamamura and Takahide
                 Higuchi",
  title =        "Multi-parent Recombination with Simplex Crossover in
                 Real Coded Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "657--664",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{tucker:1999:EEPMLDBN,
  author =       "Allan Tucker and Xiaohui Liu",
  title =        "Extending Evolutionary Programming Methods to the
                 Learning of Dynamic Bayesian Networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "923--929",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{tufts93,
  author =       "Patrick Tufts",
  title =        "Parallel Case Evaluation for Genetic Programming",
  booktitle =    "1993 Lectures in Complex Systems",
  publisher =    "Addison-Wesley",
  year =         "1995",
  editor =       "Lynn Nadel and Daniel L. Stein",
  volume =       "VI",
  series =       "Santa Fe Institute Studies in the Science of
                 Complexity",
  pages =        "591--596",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 From GP-list Wed, 21 Jun 95 18:00:35 EDT

                 deals primarily with data-parallel GP, but mentions
                 some work I did on time-series prediction. I applied GP
                 to the credit card attrition problem -- predicting when
                 a cardholder is going to drop one card in favor of
                 another.

                 The book should be on shelves by July 1995.

                 From GP-list Thu, 18 Jun 1998 20:29:58 EDT

                 I wrote what is probably the first massively parallel
                 version of GP. It is in *Lisp for the CM-5. It uses
                 SIMD parallelism in the eval step, and is probably only
                 useful for functions where you have a large number of
                 test cases that you can distribute across the
                 processors (for example: data mining and time-series
                 prediction)

                 ",
}

@InProceedings{tufts:1995:dcGPcs,
  author =       "Patrick Tufts",
  title =        "Dynamic Classifiers: Genetic Programming and
                 Classifier Systems",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "114--119",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP

                 The abstract for my paper on the Dynamic Classifier
                 System (DCS) and the slides from my talk may be found
                 at: http://www.cs.brandeis.edu/~zippy/papers.html {\em
                 Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em
                 email} info@aaai.org {\em URL:} http://www.aaai.org/",
}

@InCollection{tufts:1996:aigp2,
  author =       "Patrick Tufts",
  title =        "Genetic Programming Resources on the World-Wide Web",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "499--506",
  chapter =      "A",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
}

@Article{ETasoft96,
  author =       "Edward Tunstel and Mo Jamshidi",
  title =        "On Genetic Programming of Fuzzy Rule-Based Systems for
                 Intelligent Control",
  journal =      "International Journal of Intelligent Automation and
                 Soft Computing",
  year =         "1996",
  volume =       "2",
  number =       "3",
  pages =        "273--284",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.eece.unm.edu/grad/tunstel/papers/GP_Fuzzy96.ps",
  abstract =     "Intelligent robot navigation can be achieved using a
                 control system comprised of a collection of
                 special-purpose motion routines, or behaviors. An
                 approach to behavior coordination in multi-behavior
                 systems is described with emphasis on evolution of
                 fuzzy coordination rules using the genetic programming
                 (GP) paradigm. Both conventional GP and steady-state GP
                 are applied to evolve a fuzzy-behavior for sensor-based
                 goal-seeking to be used in a hierarchical fuzzy
                 navigation controller. The usefulness of GP is
                 demonstrated by simulating performance of evolved
                 coordination rules for autonomous navigation.",
  notes =        "

                 ",
}

@InProceedings{ETissci96,
  author =       "Edward Tunstel and Tanya Lippincott",
  title =        "Genetic Programming of Fuzzy Coordination Behaviors
                 for Mobile Robots",
  booktitle =    "International Symposium on Soft Computing for
                 Industry, 2nd World Automation Congress",
  year =         "1996",
  pages =        "647--652",
  address =      "Montpellier, France",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.eece.unm.edu/grad/tunstel/papers/issci96.ps",
  abstract =     "Intelligent robot navigation can be achieved using a
                 control system comprised of a collection of
                 special-purpose motion routines, or behaviors. An
                 approach to behavior coordination in multi-behavior
                 systems is described with emphasis on evolution of
                 fuzzy coordination rules using the genetic programming
                 (GP) paradigm. Both conventional GP and steady-state GP
                 are applied to evolve a fuzzy-behavior for sensor-based
                 goal-seeking to be used in a hierarchical fuzzy
                 navigation controller. The usefulness of GP is
                 demonstrated by simulating performance of evolved
                 coordination rules for autonomous navigation.",
  notes =        "

                 ",
}

@Article{ETasoft97,
  author =       "Edward Tunstel and Tanya Lippincott and Mo Jamshidi",
  title =        "Behavior Hierarchy for Autonomous Mobile Robots:
                 Fuzzy-behavior modulation and evolution",
  journal =      "International Journal of Intelligent Automation and
                 Soft Computing, Special Issue: Autonomous Control
                 Engineering at NASA ACE Center",
  year =         "1997",
  volume =       "3",
  number =       "1",
  pages =        "37--49",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.eece.unm.edu/grad/tunstel/papers/acesi.ps",
  abstract =     "Realization of autonomous behavior in mobile robots,
                 using fuzzy logic control, requires formulation of
                 rules which are collectively responsible for necessary
                 levels of intelligence. Such a collection of rules can
                 be conveniently decomposed and efficiently implemented
                 as a hierarchy of fuzzy-behaviors. This article
                 describes how this can be done using a behavior-based
                 architecture. A behavior hierarchy and mechanisms of
                 control decision-making are described. In addition, an
                 approach to behavior coordination is described with
                 emphasis on evolution of fuzzy coordination rules using
                 the genetic programming (GP) paradigm. Both
                 conventional GP and steady-state GP are applied to
                 evolve a fuzzy-behavior for sensor-based goal-seeking.
                 The usefulness of the behavior hierarchy, and partial
                 design by GP, is evident in performance results of
                 simulated autonomous navigation.",
  notes =        "

                 ",
}

@PhdThesis{tunstel:thesis,
  author =       "Edward W. Tunstel",
  title =        "Adaptive Hierarchy of Distributed Fuzzy Control:
                 Application to Behavior Control of Rovers",
  school =       "Electrical and Computer Engineering, University of New
                 Mexico",
  year =         "1996",
  address =      "Albuquerque, New Mexico, NM 87131, USA",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, robot",
  URL =          "http://www.eece.unm.edu/grad/tunstel/diss/disspage.htm",
  size =         "pages",
  abstract =     "This dissertation addresses the synthesis of
                 knowledge-based controllers for complex autonomous
                 systems that interact with the real world. A fuzzy
                 logic rule-based architecture is developed for
                 intelligent control of dynamic systems possessing a
                 significant degree of autonomy. It represents a novel
                 approach to controller synthesis which incorporates
                 fuzzy control theory into the framework of
                 behavior-based control. The controller intelligence is
                 distributed amongst a number of individual fuzzy logic
                 controllers and systems arranged in a hierarchical
                 structure such that system behavior at any given level
                 is a function of behavior at the level(s) below. This
                 structure addresses the combinatorial problem
                 associated with large rule-base cardinality, as the
                 totality of rules in the system are not processed
                 during any control cycle. A method of computationally
                 evolving fuzzy rule-bases is also introduced. It is
                 based on the genetic programming paradigm of
                 evolutionary computation and directly manipulates
                 linguistic terminology of the system. This provides a
                 systematic rule-base design method which is more direct
                 than current approaches that mandate numerical
                 encoding/decoding of rule representations. Finally, a
                 mechanism for multi-rule base coordination is devised
                 by generalization of fuzzy logic theoretic concepts. It
                 is incorporated to endow the system with the capability
                 to dynamically adapt its control policy in response to
                 goals, internal system state, and perception of the
                 environment.

                 The validity and practical utility of the approach is
                 verified by application to autonomous navigation
                 control of wheeled mobile robots, or rovers. Simulated
                 and experimental navigation results produced by the
                 adaptive hierarchy of distributed fuzzy control are
                 reported. Results show that the proposed ideas can be
                 useful for realization of autonomous rovers that are
                 meant to be deployed in dynamic and possibly
                 unstructured environments. This class of
                 computer-controlled, wheeled mobile vehicles includes
                 industrial mobile robots, automated guided vehicles,
                 office or hospital robots, and in some cases natural
                 terrain vehicles such as planetary rovers.

                 The proposed intelligent control architecture is
                 generally applicable to autonomous systems whose
                 overall behavior can be decomposed into a bottom-up
                 hierarchy of increased behavioral complexity, or a
                 decentralized structure of multiple rule-bases.",
}

@InCollection{TuringAM:maci69,
  author =       "Alan M. Turing",
  title =        "Intelligent Machinery",
  booktitle =    "Machine Intelligence",
  year =         "1969",
  editors =      "Bernard Meltzer and Donald Michie",
  publisher =    "Edinburgh University Press",
  volume =       "5",
  annote =       "Hodges page 377 note 6.53.",
  chapter =      "1",
  pages =        "3--23",
  address =      "Edinburgh, UK",
  keywords =     "genetic algorithms, genetic programming, robot",
  notes =        "Published after the author's death",
  size =         "20 pages",
}

@InProceedings{turton:1996:geog,
  author =       "Turton and Openshaw and Diplock",
  title =        "Some Geographic Applications of Genetic Programming on
                 the Cray {T3D} Supercomputer",
  booktitle =    "UK Parallel'96",
  year =         "1996",
  editor =       "Jesshope and Shafarenko",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.geog.leeds.ac.uk/staff/i.turton/ai/ukpar96.ps.Z",
  size =         "16 pages",
  notes =        "{"}REplacement of ephemeral constant by a parameter,
                 the value of which is optimised using an embedded
                 non-linar paramter estimation proceedure.{"} Spatial
                 Interaction Modelling, Seattle, USA Subglacial Water
                 System Case Study. Trapridge Glacier, Yukon.

                 From messages to genetic-programming@cs.stanford.edu
                 Tue, 03 Sep 1996 08:52:15 BST Thu, 05 Sep 1996 08:38:51
                 BST Basically we use an asyncronous steady state system
                 which implicitly gives preferential treatment to faster
                 evaluating models. Its written in F77 and MPI at the
                 moment but RSN I will be reworking it as F90 with some
                 more functionallity such as user configurable islands
                 and such like.

                 genetic-programming@cs.stanford.edu Tue, 10 Sep 1996
                 08:32:17 BST I have produced a program that converts
                 s-experessions to infix equations that can be pasted
                 into maple (or free equivalents, I'd guess) which has a
                 simplify command. My code is at
                 ftp://gam.leeds.ac.uk/pub/ian/pretoin.f",
}

@Misc{turton:1998:summary,
  author =       "Ian Turton and Stan Openshaw",
  title =        "High Performance Computing and Geography:
                 developments, issues and case studies",
  howpublished = "www",
  year =         "1998",
  month =        "16 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ccg.leeds.ac.uk/ian/hpciwp/hpciwp.html",
  size =         "pages",
  abstract =     "The Centre for Computational Geography and the School
                 of Computer Studies at the University of Leeds formed a
                 consortium under the EPSRC's High Performance Computing
                 Initiative in 1994. This paper outlines some of the
                 results that were obtained during the first two years
                 of the three year project.",
  notes =        "Fortran MPI 512 node Cray T3D",
}

@InProceedings{uchibe:1999:CCBAMRC,
  author =       "Eiji Uchibe and Masateru Nakamura and Minoru Asada",
  title =        "Cooperative and Competitive Behavior Acquisition for
                 Mobile Robots through Co-evolution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1406--1413",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, artificial
                 life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  abstract =     "REVOLVER",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{uesaka:2000:sl2df,
  author =       "Kazuyoshi Uesaka and Masayuki Kawamata",
  title =        "Synthesis of low-sensitivity second-order digital
                 filters using genetic programming with automatically
                 defined functions",
  journal =      "IEEE Signal Processing Letters",
  year =         "2000",
  volume =       "7",
  number =       "4",
  pages =        "83--85",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, IIR filters,
                 second-order digital filters, automatically defined
                 functions, IIR digital filter, filter synthesis method,
                 S-expressions, fitness measure, magnitude sensitivity,
                 low coefficient sensitivity filters, ADF",
  ISSN =         "1070-9908",
  URL =          "http://ieeexplore.ieee.org/iel5/97/18028/00833004.pdf",
  abstract =     "This letter proposes a synthesis method for low
                 coefficient sensitivity second-order IIR digital filter
                 structures using genetic programming with automatically
                 defined functions (GP-ADF). In this letter, digital
                 filter structures are represented as S-expressions with
                 subroutines. It is easy to generate syntactically valid
                 S-expressions and perform the genetic operations,
                 because the representation is suitable for GP. A
                 numerical example uses the fitness measure, including
                 the magnitude sensitivity, and demonstrates that the
                 proposed method can synthesize efficiently very low
                 coefficient sensitivity filter structures.",
}

@InProceedings{unemi:2000:SAI,
  author =       "Tatsuo Unemi",
  title =        "{SBART} 2.4: An {IEC} tool for creating 2{D} images,
                 movies, and collage",
  booktitle =    "Genetic Algorithms in Visual Art and Music",
  year =         "2000",
  editor =       "Colin G. Johnson and Juan Jesus Romero Cardalda",
  pages =        "153--157",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@InProceedings{uno:1999:EBESIL,
  author =       "Kimitaka Uno and Akira Namatame",
  title =        "Evolutionary Behaviors Emerged through Strategic
                 Interactions in the Large",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1414--1421",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{ursem:2002:gpwsofaedas,
  author =       "Rasmus K. Ursem and Thiemo Krink",
  title =        "Genetic Programming with Smooth Operators for
                 Arithmetic Expressions: Diviplication and Subdition",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
                 and Garry Greenwood and Hitoshi Iba and Paul Marrow and
                 Mark Shackleton",
  pages =        "1372--1377",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "smooth operators for arithmetic expressions as an
                 approach to smoothening the search space in Genetic
                 Programming (GP). Smooth operator GP interpolates
                 between arithmetic operators such as times and divide,
                 thereby allowing a gradual adaptation to the problem.
                 The suggested approach is compared to traditional GP on
                 a system identification problem.",
}

@InProceedings{urzelai:1998:irs,
  author =       "Joseba Urzelai and Dario Floreano and Marco Dorigo and
                 Marco Colombetti",
  title =        "Incremental Robot Shaping",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "832--842",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Evolutionary Robotics",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{valenzuela:1999:EDCT,
  author =       "Christine L. Valenzuela",
  title =        "Evolutionary Divide and Conquer ({II}) for the {TSP}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1744--1749",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{vallejo:1999:RAFCGP,
  author =       "Edgar E. Vallejo and Fernando Ramos",
  title =        "Result-Sharing: {A} Framework for Cooperation in
                 Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1238",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{vallejo:2001:EuroGP,
  author =       "Edgar E. Vallejo and Fernando Ramos",
  title =        "Evolving Turing machines for Biosequences Recognition
                 and Analysis",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "192--203",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming,
                 Bioinformatics, DNA, Turing machines, Multiple Sequence
                 aligment",
  ISBN =         "3-540-41899-7",
  size =         "12 pages",
  abstract =     "This article presents a genetic programming system for
                 biosequence recognition and analysis. In our model, a
                 population of Turing machines evolves the capability of
                 biosequence recognition using genetic algorithms. We
                 use HIV sequences as the working example. Experimental
                 results indicate that evolved Turing machines are
                 capable of recognizing HIV sequences in a collection of
                 training sets. In addition, we demonstrate that the
                 evolved Turing machines can be used to approximate the
                 multiple sequence alignment problem.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{vanbelle:2002:EuroGP,
  title =        "Uniform Subtree Mutation",
  author =       "Terry {Van Belle} and David H. Ackley",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "152--161",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "Genetic programming methods often suffer from `code
                 bloat,' in which evolving solution trees rapidly become
                 unmanageably large. To provide a measure of sensitivity
                 to tree size in a natural way, we introduce a simple
                 uniform subtree mutation (USM) operator that provides
                 an approximately constant probability of mutation per
                 tree node, rather than per tree. To help model
                 circumstances where tree size cannot be ignored, we
                 introduce a new notion of computational effort called
                 size effort. Initial empirical tests show that genetic
                 programming using only uniform subtree mutation reduces
                 evolved tree sizes dramatically, compared to crossover,
                 but does impact solution quality somewhat. In some
                 cases, however, using using a combination of USM and
                 crossover yielded both smaller trees and superior
                 performance, as measured both by size effort and
                 traditional metrics.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{vanbelle:2002:gecco,
  author =       "Terry {Van Belle} and David H. Ackley",
  title =        "Code Factoring And The Evolution Of Evolvability",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1383--1390",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "search-based software engineering, automatically
                 defined functions, code factoring, dynamic environment,
                 evolution of evolvability, genetic programming,
                 software engineering",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InCollection{vanhoucke:2000:SDACGP,
  author =       "Vincent Vanhoucke",
  title =        "Speech Detection in Adverse Conditions using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "415--424",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{vanneschi:2002:gecco:workshop,
  title =        "A Study on Fitness Distance Correlation and Problem
                 Difficulty for Genetic Programming",
  author =       "Leonardo Vanneschi and Marco Tomassini",
  pages =        "307--310",
  booktitle =    "Graduate Student Workshop",
  editor =       "Sean Luke and Conor Ryan and Una-May O'Reilly",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{VanVeldhuizen:1998:eccpf,
  author =       "David A. {Van Veldhuizen} and Gary B. Lamont",
  title =        "Evolutionary Computation and Convergence to a Pareto
                 Front",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{vanyi:2000:grden,
  author =       "Robert Vanyi and Gabriella Kokai and Zoltan Toth and
                 T-unde Peto",
  title =        "Grammatical Retina Description with Enhanced Methods",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "193--208",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "In this paper the enhanced version of the GREDEA
                 system is presented. The main idea behind the system is
                 that with the help of evolutionary algorithms a
                 grammatical description of the blood circulation of the
                 human retina can be inferred. The system uses
                 parametric Lindenmayer systems as description language.
                 It can be applied on patients with diabetes who need to
                 be monitored over long periods of time. Since the first
                 version some improvements were made, e.g. new fitness
                 function and new genetic operators. In this paper these
                 changes are described.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{vanyi2:2001:gecco,
  title =        "Giving Structural Descriptions of Tree-like Objects
                 from Binary Images Using Genetic Programming",
  author =       "Robert Vanyi and Gabriella Kokai",
  pages =        "163--172",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, binary
                 images, Lindenmayer systems, branching, structures,
                 structural descriptions, image reconstruction",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{Vassilev:2000:GECCO,
  author =       "Vesselin K. Vassilev and Julian F. Miller",
  title =        "Embedding Landscape Neutrality to Build a Bridge from
                 the Conventional to a More Efficient Three-bit
                 Multiplier Circuit",
  pages =        "539",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{vavak:1998:pGAvlssrrfeg,
  author =       "F. Vavak and K. A. Jukes and T. C. Fogarty",
  title =        "Performance of a Genetic Algorithm with Variable Local
                 Search Range Relative to Frequency of the Environmental
                 Changes",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "602--608",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{Vazquez:2000:GECCOlb,
  author =       "Katya Rodriguez Vazquez",
  title =        "Identification of {MIMO} Non-Linear Systems Using
                 Evolutionary Computation",
  pages =        "411--417",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Part of whitley:2000:GECCOlb",
}

@InProceedings{veach:1996:rrvgGA,
  author =       "Marshall S. Veach",
  title =        "Recognition and Reconstruction of Visibility Graphs
                 Using a Genetic Algorithm",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "491--498",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  notes =        "GP-96 GA paper",
}

@InCollection{veach:1995:RRVGUGA,
  author =       "Marshall S. Veach",
  title =        "Recognition and Reconstruction of Visibility Graphs
                 Using a Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "291--300",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{Vedarajan:1997:ipoGA,
  author =       "Ganesh Vedarajan and Louis Chi Chan and David
                 Goldberg",
  title =        "Investment Portfolio Optimization using Genetic
                 Algorithms",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "255--263",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@MastersThesis{veennan:mastersthesis,
  author =       "C. J. Veenman",
  title =        "Positional Genetic Programming",
  school =       "Dept. of Mathematics and Computer Science",
  year =         "1996",
  address =      "Leiden University, The Netherlands",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-ict.its.tudelft.nl/~cor/thesis96.ps.gz",
  size =         "pages",
  notes =        "

                 Positional Genetic Programming (genetic algorithms with
                 encoded tree structures), 1996. This report describes a
                 genetic programming engine implemented as a genetic
                 algorithm, i.e. it uses fixed sized chromosomes and
                 allows for ordinary crossover operators. Positional
                 Genetic Programming turns out to perform better than
                 the usual genetic programming scheme, that uses
                 variable sized structures and random subtree
                 exchange.

                 See also eiben:email:10-Nov-1997 thesis96.ps.gz doesnt
                 work with ghostview 6-apr-98",
}

@InProceedings{vega:2000:mgpabd,
  author =       "F. Fernandez {de Vega} and Laura M. Roa and Marco
                 Tomassini and J. M. Sanchez",
  title =        "Multipopulation Genetic Programing Applied to Burn
                 Diagnosing",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1292--1296",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, novel
                 applications i",
  ISBN =         "0-7803-6375-2",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@TechReport{Vekaria:1997:GPgdTR,
  author =       "K. Vekaria and C. Clack",
  title =        "Haploid Genetic Programming with Dominance",
  institution =  "University College London",
  year =         "1997",
  type =         "Research Note",
  number =       "RN/97/121",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/K.Vekaria/publications/haploidGP.ps",
  notes =        "haploidGP.ps doesnt work with ghostview but prints ok.
                 See also Vekaria:1997:GPgd

                 ",
  size =         "6 pages",
}

@InProceedings{Vekaria:1997:GPgd,
  author =       "Kanta Vekaria",
  title =        "Genetic Programming With Gene Dominance",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "300",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  URL =          "http://www.cs.ucl.ac.uk/staff/K.Vekaria/publications/gp97.ps",
  size =         "1 page",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670 /gp97.ps doesnt work with ghostview See
                 also Vekaria:1997:GPgdTR",
}

@InProceedings{vekaria:1998:sxGA,
  author =       "Kanta Vekaria and Chris Clack",
  title =        "Selective Crossover in Genetic Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "609",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{vekaria:1999:BIARO,
  author =       "Kanta Vekaria and Chris Clack",
  title =        "Biases Introduced by Adaptive Recombination
                 Operators",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "670--677",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{vekaria:1999:S,
  author =       "Kanta Vekaria and Chris Clack",
  title =        "Schema propagation in selective crossover",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "268--275",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{vemuri:1995:epSISAL,
  author =       "V. Rao Vemuri and Patrick Miller",
  title =        "Evolving Parallel {SISAL} Programs Using {GP}",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "120--121",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP, {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InProceedings{venturini:1997:igakdd,
  author =       "G. Venturini and M. Slimane and F. Morin and J.-P.
                 {Asselin de Beauville}",
  title =        "On Using Interactive Genetic Algorithms for Knowledge
                 Discovery in Databases",
  booktitle =    "Genetic Algorithms: Proceedings of the Seventh
                 International Conference",
  year =         "1997",
  editor =       "Thomas Back",
  pages =        "696--703",
  address =      "Michigan State University, East Lansing, MI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "19-23 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms, Data Mining",
  ISBN =         "1-55860-487-1",
  size =         "8 pages",
  abstract =     "This paper presents an new interactive algorithm for
                 exploring numerical databases and discovering
                 regularities, called Genetic Interactive Data Explorer
                 (GIDE). GIDE, which is based on the specifications of a
                 previous prototype (Venturini et al. 1996), uses the
                 principles of interactive genetic algorithms to evolve,
                 in cooperation with the domain expert, 2D graphical
                 representations of the data. These 2D representations
                 are encoded by two new variables represented as Lisp
                 functions using the genetic programming paradigm. The
                 domain expert selects interesting representations which
                 can be further improved by the genetic algorithm. The
                 interaction between the expert and the knowledge
                 discovery tool has been greatly improved. Results are
                 presented on several standard databases.",
  notes =        "ICGA-97 Iris, Wine database, glass database, Pima
                 indians diabetes database",
}

@InProceedings{vianna:1998:ehmsvphf,
  author =       "Luiz S. Ochi and Dalessandro S. Vianna and Lucia M. A.
                 Drummond and Andre O. Victor",
  title =        "An Evolutionary Hybrid Metaheuristic for Solving the
                 Vehicle Routing Problem with Heterogeneous Fleet",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
                 and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "187--195",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  abstract =     "We present a new hybrid metaheuristic which combines
                 Genetic Algorithms and Scatter Search coupled with a
                 decomposition-into-petals procedure for solving a class
                 of Vehicle Routing and Scheduling Problems. Its
                 performance is evaluated for a heterogeneous fleet
                 model, which is considered a problem much harder to
                 solve than the homogeneous vehicle routing problem.",
  notes =        "EuroGP'98",
}

@TechReport{vitanyi:1997:gfourmmc,
  author =       "Paul Vitanyi",
  title =        "Genetic Fitness Optimization Using Rapidly Mixing
                 Markov Chains",
  institution =  "NeuroCOLT",
  year =         "1997",
  type =         "technical report",
  number =       "NC-TR-005",
  address =      "Computer Science, Royal Holloway, Egham, Surrey,
                 England",
  month =        feb,
  email =        "neurocolt@dcs.rhbnc.ac.uk",
  keywords =     "evolutionary computation",
  URL =          "http://www.neurocolt.com/tech_reps/1997/nc-tr-97-005.ps.gz",
  URL =          "http://www.neurocolt.com/abs/1997/abs97005.html",
  abstract =     "A notion of highly probable fitness optimization
                 through evolutionary computing runs on small size
                 populations in a very general setting is proposed. This
                 has applications to evolutionary learning. Based on
                 rapidly mixing Markov chains, the approach pertains to
                 most types of evolutionary genetic algorithms, genetic
                 programming and the like. For systems having associated
                 rapidly mixing Markov chains and appropriate stationary
                 distributions the new method finds optimal programs
                 (individuals) with probability almost 1.
                 Algorithmically, the novel approach prescribes a
                 strategy of executing many short computation runs,
                 rather than one long computation run. Given an
                 arbitrary evolutionary program it may be infeasible to
                 determine whether its associated matrix is rapidly
                 mixing. In our proposed structured evolutionary program
                 discipline, the development of the program and the
                 guaranty of the rapidly mixing property go hand in
                 hand. We conclude with a tentative toy example.",
  notes =        "See also alt96*67 and Vitanyi:2000:DEP Although refers
                 several time to GP, approach is evolutionary as a
                 whole, ie not just GP",
  size =         "17 pages",
}

@InProceedings{alt96*67,
  author =       "Paul Vitanyi",
  title =        "Genetic fitness optimization using rapidly mixing
                 {Markov} chains",
  pages =        "67--82",
  ISBN =         "3-540-61863-5",
  editor =       "Setsuo Arikawa and Arun K. Sharma",
  booktitle =    "Proceedings of the 7th International Workshop on
                 Algorithmic Learning Theory",
  month =        oct # "~23--25",
  series =       "LNAI",
  volume =       "1160",
  publisher =    "Springer-Verlag",
  address =      "Berlin",
  year =         "1996",
  notes =        "see also vitanyi:1997:gfourmmc and Vitanyi:2000:DEP",
}

@Article{Vitanyi:2000:DEP,
  author =       "Paul Vitanyi",
  title =        "A discipline of evolutionary programming",
  journal =      "Theoretical Computer Science",
  year =         "2000",
  volume =       "241",
  number =       "1--2",
  pages =        "3--23",
  month =        "28 " # jun,
  keywords =     "genetic algorithms, genetic programming, Neural and
                 Evolutionary Computing, Artificial Intelligence,
                 Computational Complexity, Data Structures and
                 Algorithms, Learning, Multiagent Systems",
  ISSN =         "0304-3975",
  CODEN =        "TCSCDI",
  bibdate =      "Tue Oct 31 11:38:29 MST 2000",
  URL =          "http://xxx.lanl.gov/abs/cs.NE/9902006",
  URL =          "http://www.cwi.nl/~paulv/papers/genetic.ps",
  URL =          "http://www.elsevier.nl/gej-ng/10/41/16/175/21/22/article.pdf",
  size =         "21 pages",
  abstract =     "Genetic fitness optimization using small populations
                 or small population updates across generations
                 generally suffers from randomly diverging evolutions.
                 We propose a notion of highly probable fitness
                 optimization through feasible evolutionary computing
                 runs on small size populations. Based on rapidly mixing
                 Markov chains, the approach pertains to most types of
                 evolutionary genetic algorithms, genetic programming
                 and the like. We establish that for systems having
                 associated rapidly mixing Markov chains and appropriate
                 stationary distributions the new method finds optimal
                 programs (individuals) with probability almost 1. To
                 make the method useful would require a structured
                 design methodology where the development of the program
                 and the guarantee of the rapidly mixing property go
                 hand in hand. We analyze a simple example to show that
                 the method is implementable. More significant examples
                 require theoretical advances, for example with respect
                 to the Metropolis filter.",
  notes =        "Update of alt96*67",
}

@InProceedings{voicu:1999:TA,
  author =       "Anca M. Voicu and Richard C. Barrett and Liviu I.
                 Voicu and Harley R. Myler",
  title =        "Trade models generated by evolutionary programming:
                 {A} comparison with the gravity trade model",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "276--283",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{voss:1999:EAFSO,
  author =       "Mark S. Voss and Christopher M. Foley",
  title =        "Evolutionary Algorithm For Structural Optimization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "678--685",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{voss:1999:TA,
  author =       "Mark S. Voss and Christopher M. Foley",
  title =        "The (mu, lambda, alpha, beta) distribution: {A}
                 selection scheme for ranked populations",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "284--291",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{voss:2002:gecco,
  author =       "Mark S. Voss and Xin Feng",
  title =        "A New Methodology For Emergent System Identification
                 Using Particle Swarm Optimization ({PSO}) And The Group
                 Mehtod Data Handling ({GMDH})",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1227--1232",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "real world applications, genetic programming, GMDH,
                 group method for data handling, particle swarm
                 optimization, PSO, system identification",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",
}

@InProceedings{vrajitoru:1999:GPOAGA,
  author =       "Dana Vrajitoru",
  title =        "Genetic Programming Operators Applied to Genetic
                 Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "686--693",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, classifier
                 systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{vriend:1999:TDIPGA,
  author =       "Nicolaas J. Vriend",
  title =        "The Difference between Individual and Population
                 Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "812",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{wada:1994:essf,
  author =       "{Ken-nosuke} Wada and Yoshiko Wada and Hirofumi Doi
                 and {Shin-ichi} Tanaka and Mitsuri Furusawa",
  title =        "Evolutionary Systems: Structures and Functions",
  booktitle =    "Proceedings of IEEE International Conference on
                 Evolutionary Computation (ICEC-94), World Congress on
                 Computational Intelligence",
  publisher =    "IEEE Computer Society Press, New York",
  year =         "1994",
  pages =        "796--801",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  keywords =     "genetic algorithms, cellular automata",
  notes =        "Not really a GP but a wide ranging article, includes
                 evolution of cellular automata. On a 0-1 knapsack
                 problem GA does better than Simulated annealing and
                 Hopfield neural net but branch and bound does better
                 than it.

                 models gene duplication, different mutation rates on
                 the two strands of DNA (observed in E-coli). ERATO.
                 Argues for mutations building up where they make no
                 difference until environment changes, when some may be
                 beneficial.

                 ",
}

@Article{Altenberg:1994EEGP,
  author =       "Gunter P. Wagner and Lee Altenberg",
  year =         "1996",
  title =        "Complex Adaptations and the Evolution of
                 Evolvability",
  journal =      "Evolution",
  pages =        "In press",
  keywords =     "genetic algorithms, theoretical biology, modular
                 genotype-phenotype map",
  URL =          "ftp://ftp.mhpcc.edu/pub/incoming/altenberg/GunterLeeCAEE.ps.Z",
  url_2 =        "http://pueo.mhpcc.edu/~altenber/PAPERS/GunterLeeCAEE.html",
  size =         "24 pages",
  abstract =     "The problem of complex adaptations is studied in two
                 largely disconnected research traditions: evolutionary
                 biology and evolutionary computer science. This paper
                 summarizes the results from both areas and compares
                 their implications. In evolutionary computer science it
                 was found that the Darwinian process of mutation,
                 recombination and selection is not universally
                 effective in improving complex systems like computer
                 programs or chip designs. For adaptation to occur,
                 these systems must possess {"}evolvability{"}, i.e. the
                 ability of random variations to sometimes produce
                 improvement. It was found that evolvability critically
                 depends on the way genetic variation maps onto
                 phenotypic variation, an issue known as the
                 representation problem. The genotype-phenotype map
                 determines the variability of characters, which is the
                 propensity to vary. Variability needs to be
                 distinguished from variation, which are the actually
                 realized differences between individuals. The
                 genotype-phenotype map is the common theme underlying
                 such varied biological phenomena as genetic
                 canalization, developmental constraints, biological
                 versatility, developmental dissociability,
                 morphological integration, and many more. For
                 evolutionary biology the representation problem has
                 important implications: how is it that extant species
                 acquired a genotype-phenotype map which allows
                 improvement by mutation and selection? Is the
                 genotype-phenotype map able to change in evolution?
                 What are the selective forces, if any, that shape the
                 genotype-phenotype map? We propose that the
                 genotype-phenotype map can evolve by two main routes:
                 epistatic mutations, or the creation of new genes. A
                 common result for organismic design is modularity. By
                 modularity we mean a genotype-phenotype map in which
                 there are few pleiotropic effects among characters
                 serving different functions, with pleiotropic effects
                 falling mainly among characters that are part of a
                 single functional complex. Such a design is expected to
                 improve evolvability by limiting the interference
                 between the adaptation of different functions. Several
                 population genetic models are reviewed that are
                 intended to explain the evolutionary origin of a
                 modular design. While our current knowledge is
                 insufficient to assess the plausibility of these
                 models, they form the beginning of a framework for
                 understanding the evolution of the genotype-phenotype
                 map.

                 Copyright 1996 Gunter Wagner and Lee Altenberg",
  notes =        "survey connecting real genetics with evolutionary
                 computation",
}

@InProceedings{wagner:1999:HCCS,
  author =       "Kyle Wagner",
  title =        "Habitat, Communication and Cooperative Strategies",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "694--701",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{wagner:2001:gpepcftsp,
  author =       "Neal Wagner and Zbigniew Michalewicz",
  title =        "Genetic Programming with Efficient Population Control
                 for Financial Time Series Prediction",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "458--462",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.coe.uncc.edu/~nwagner/gecco/GeccoPresentation_files/v3_document.htm",
  notes =        "GECCO-2001LB, bloat control by dynamic size/depth
                 limits",
}

@Article{Wak01,
  author =       "Julie Wakefield",
  title =        "Complexity's Buisness Model",
  journal =      "Scientific American",
  year =         "2001",
  pages =        "24--25",
  month =        jan,
  keywords =     "genetic algorithms",
  URL =          "http://www.sciam.com/2001/0101issue/0101techbus1.html",
  size =         "2 pages",
  notes =        "general. A few examples of USA commercially succesful
                 applications of GAs.",
}

@InCollection{walker:1995:ceuga,
  author =       "R. F. Walker and E. W. Haasdijk and M. C. Gerrets",
  title =        "Credit Evaluation Using a Genetic Algorithm",
  booktitle =    "Intelligent Systems for Finance and Business",
  publisher =    "Wiley",
  year =         "1995",
  editor =       "Suran Goonatilake and Philip Treleaven",
  chapter =      "3",
  pages =        "39--59",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.cs.ucl.ac.uk/staff/S.Goonatilake/busbook.html
                 contains info on book.

                 Authors from Cap Volmac, Daltonlaan 400, PO BOX 2575,
                 3500 GN Utrecht, The Netherlands.

                 OMEGA Credit scoring for loans based on characteristics
                 of the applicant (eg salary, age, marital status)

                 {"}In all trials conducted to date it (GAAF, a GP like
                 GA) outperformed any known method for credit
                 scoring.{"}

                 Mon, 17 Mar 1997 22:25:28 Company now called {"}Cap
                 Gemini the Netherlands{"}",
}

@InProceedings{Walker:1997:mdcva,
  author =       "John Walker",
  title =        "Methodologies to the Design and Control of Virtual
                 Agents",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "301",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670

                 Air combat simulation, 2 agents",
}

@InProceedings{walker:1999:SNN,
  author =       "Richard Walker and Orazio Miglino",
  title =        "Simulating exploratory behavior in evolving Neural
                 Networks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1422--1428",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{walker:1999:AWGP,
  author =       "Reginald L. Walker",
  title =        "Assessment of the Web using Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1750--1755",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, information retrieval, internet, world
                 wide web, search engines",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{walker:2001:pcsumec,
  author =       "Reginald L. Walker",
  title =        "Parallel Clustering System Using the Methodologies of
                 Evolutionary Computations",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "831--938",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 Bioinformatics Evolutionary computations, Biological
                 modeling, Cluster analysis, Distributive/parallel
                 computing, MPI, WWW",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 {"}only females of equal status compete in order to
                 ...mate{"} p831 {"}even-numbered node ID{"}. Tocorime
                 Apicu. bloat. cf pygmies and civil servants?
                 experimental www search engine",
}

@Article{Walker:2001:PC,
  author =       "Reginald L. Walker",
  title =        "Search engine case study: searching the web using
                 genetic programming and {MPI}",
  journal =      "Parallel Computing",
  volume =       "27",
  pages =        "71--89",
  year =         "2001",
  number =       "1-2",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Distributed
                 computing, Information retrieval, World Wide Web,
                 Search engines",
  URL =          "http://www.sciencedirect.com/science/article/B6V12-42K5HNX-4/1/57eb870c72fb7768bb7d824557444b72",
  abstract =     "The generation of a Web page follows distinct sources
                 for the incorporation of information. The earliest
                 format of these sources was an organized display of
                 known information determined by the page designers'
                 interest and/or design parameters. The sources may have
                 been published in books or other printed literature, or
                 disseminated as general information about the page
                 designer. Due to a growth in Web pages, several new
                 search engines have been developed in addition to the
                 refinement of the already existing ones. The use of the
                 refined search engines, however, still produces an
                 array of diverse information when the same set of
                 keywords are used in a Web search. Some degree of
                 consistency in the search results can be achieved over
                 a period of time when the same search engine is used,
                 yet, most initial Web searches on a given topic are
                 treated as final after some form of
                 refinement/adjustment of the keywords used in the
                 search process. To determine the applicability of a
                 genetic programming (GP) model for the diverse set of
                 Web documents, search strategies behind the current
                 search engines for the World Wide Web were studied. The
                 development of a GP model resulted in a parallel
                 implementation of a pseudo-search engine indexer
                 simulator. The training sets used in this study
                 provided a small snapshot of the computational effort
                 required to index Web documents accurately and
                 efficiently. Future results will be used to develop and
                 implement Web crawler mechanisms that are capable of
                 assessing the scope of this research effort. The GP
                 model results were generated on a network of SUN
                 workstations and an IBM SP2.",
}

@InProceedings{walsh:1996:Paragen,
  author =       "Paul Walsh and Conor Ryan",
  title =        "Paragen: {A} Novel Technique for the
                 Autoparallelisation of Sequential Programs using
                 Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "406--409",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{walsh:1998:epfp,
  author =       "Paul Walsh",
  title =        "Evolving Pure Functional Programs",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "399--402",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{walsh:1998:gii,
  author =       "Robert W. Walsh and Bryant A. Julstrom",
  title =        "Generalized Instant Insanity: {A} {GA}-Difficult
                 Problem",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{walsh:1999:AFSFESIHLP,
  author =       "Paul Walsh",
  title =        "A Functional Style and Fitness Evaluation Scheme for
                 Inducting High Level Programs",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1211--1216",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{walsh:1999:APEAHPI,
  author =       "Paul J. Walsh",
  title =        "A Parallel Evolutionary Algorithm for High-Level
                 Program Induction",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1027--1034",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, parallel and
                 distributed processing",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@Article{Walters:1994:GERM,
  author =       "D. Eric Walters and R. Michael Hinds",
  title =        "Genetically Evolved Receptor Models ({GERM}): {A}
                 Computational Approach to Construction of Receptor
                 Models",
  journal =      "Journal of Medicinal Chemistry",
  year =         "1994",
  volume =       "37",
  pages =        "2527--2536",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Given the three-dimensional structure of a receptor
                 site, there are several methods available for designing
                 ligands to occupy the site; frequently, the
                 three-dimensional structure of interesting receptors is
                 not known, however. The GERM program uses a genetic
                 algorithm to produce atomic-level models of receptor
                 sites, based on a small set of known structure-activity
                 relationships. The evolved models show a high
                 correlation between calculated intermolecular energies
                 and bioactivities; they also give reasonable
                 predictions of bioactivity for compounds which were not
                 included in model generation. Such models may serve as
                 starting points for computational or human ligand
                 design efforts.",
  notes =        "

                 reprints available on request--send a mailing address
                 D. Eric Walters, Ph.D., Associate Professor, Biological
                 Chemistry Finch University of Health Sciences/The
                 Chicago Medical School 3333 Green Bay Road, North
                 Chicago, IL 60064 ph 708-578-3000, x-498;fax
                 708-578-3240; email: walterse@mis.fuhscms.edu",
}

@InProceedings{LimanWang:1998:sDNA:DR,
  author =       "Liman Wang and Qinghua Liu and Anthony G. Frutos and
                 Susan D. Gillmor and Andrew J. Thiel and Todd C.
                 Strother and Anne E. Condon and Robert M. Corn and Max
                 G. Lagally and Lloyd M. Smith",
  title =        "Surface-Based {DNA} Computing Operations: {DESTROY}
                 and {READOUT}",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InCollection{wang:2000:SMCGA,
  author =       "Jinlin Wang",
  title =        "Scheduling of a Machining Cell using Genetic
                 Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "425--434",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InCollection{ward:1997:morse,
  author =       "David Ward",
  title =        "A Program to Decode Morse Code Developed with a
                 Genetic Programming Technique",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "216--225",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "accuracy typically greater than 95 percent",
  notes =        "part of koza:1997:GAGPs",
}

@InCollection{warren:1994:stockpp,
  author =       "Mark A. Warren",
  title =        "Stock Price Prediction Using Genetic Programming",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "180--184",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "{"}While these experiments failed to find THE
                 prediction model, they did demonstrate that ... recent
                 price is a very good indicator of a stocks'
                 performance.{"} p182

                 This volume contains 20 papers written and submitted by
                 students describing their term projects for the course
                 {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InCollection{warren:1999:ACSDMHDC,
  author =       "James Warren",
  title =        "A Co-Evolutionary Scheme for Discovering Maximal
                 Hamming Distance Codes",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "236--244",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{watabe:1999:SCOPMGA,
  author =       "Hirokazu Watabe and Tsukasa Kawaoka",
  title =        "Solving Combinatorial Optimization Problems with
                 Multi-Step Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "813",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{waters:1999:GPCEFALE,
  author =       "Michael Waters and John Sheppard",
  title =        "Genetic Programming and Co-Evolution with Exogenous
                 Fitness in an Artificial Life Environment",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "3",
  pages =        "1641--1648",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, coevolution",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@InProceedings{watson:1996:ifs,
  author =       "A. H. Watson and I. C. Parmee",
  title =        "Identification Of Fluid Systems Using Genetic
                 Programming",
  booktitle =    "Proceedings of the Second Online Workshop on
                 Evolutionary Computation (WEC2)",
  year =         "1996",
  number =       "2",
  pages =        "45--48",
  address =      "http://www.bioele.nuee.nagoya-u.ac.jp/wec2/",
  month =        "4--22 " # mar,
  organisation = "Research Group on ECOmp of the Society of Fuzzy Theory
                 and Systems (SOFT)",
  publisher =    "Nagoya University, Japan",
  keywords =     "genetic algorithms, genetic programming, Fluid
                 Systems, Evolutionary Computing",
  URL =          "http://www.bioele.nuee.nagoya-u.ac.jp/wec2/papers/files/watson.ps",
  size =         "4 pages",
  abstract =     "In recent years, applied researchers have become
                 increasingly interested in Adaptive Search (AS),
                 techniques such as the Genetic Algorithm (GA), and
                 Genetic Programming GP, for engineering design. This
                 paper illustrates the effectiveness of the genetic
                 programming paradigm for simple fluid systems
                 identification problems. The objective of the paper is
                 to establish methods for systems identification using
                 GP and sets of empirical data. The manipulation and
                 optimisation of these approximate functions that
                 describe the physical process is achieved using the GP
                 approach and by the development of complementary AS
                 techniques. Two new GP operators are introduced, the
                 first searches through possible values of terminals for
                 a particular functional tree structure, and the second
                 uses functional induction to improve the performance of
                 the technique.",
}

@InProceedings{Watson:1997:ssGPccc,
  author =       "Andrew H. Watson and Ian C. Parmee",
  title =        "Steady State Genetic Programming With Constrained
                 Complexity Crossover",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "329",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{Watson:1997:cgtdsGPast,
  author =       "Andrew H. Watson",
  title =        "Calibrating Gas Turbine Design Software using Genetic
                 Programming and Adaptive Search Techniques",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "302",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@InProceedings{watson:1997:ssGPcccssp,
  author =       "Andrew H. Watson and Ian C. Parmee",
  title =        "Steady State Genetic Programming with Constrained
                 Complexity Crossover Using Species Sub Population",
  booktitle =    "Genetic Algorithms: Proceedings of the Seventh
                 International Conference",
  year =         "1997",
  editor =       "Thomas Back",
  pages =        "315--321",
  address =      "Michigan State University, East Lansing, MI, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "19-23 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-487-1",
  size =         "8 pages",
  abstract =     "This paper introduces an alternative approach to
                 Genetic Programming (GP), which is based upon a steady
                 state population and a novel constrained complexity
                 crossover operator. This technique, called
                 {"}DRAM-GP{"} (i.e. Distributed, Rapid, Attenuated
                 Memory Genetic Programming), uses node complexity
                 weightings as a basis for speciation. The population is
                 decomposed into smaller sub-populations which
                 communicate with each other through the action of
                 crossover. The effectiveness of this method is
                 demonstrated by successful application to Boolean
                 concept formation and to symbolic regression problems.
                 The results show that improved performance is possible
                 with a dramatic reduction in population size and
                 associated memory requirements.",
  notes =        "ICGA-97",
}

@InProceedings{watson:1998:mbbi,
  author =       "Richard A. Watson and Gregory S. Hornby and Jordan B.
                 Pollack",
  title =        "Modeling Building-Block Interdependency",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB",
}

@InProceedings{watson:1999:APAMNMPOP,
  author =       "Jean-Paul Watson",
  title =        "A Performance Assessment of Modern Niching Methods for
                 Parameter Optimization Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "702--709",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{watson:1999:ICGA,
  author =       "Richard A. Watson and Jordan B. Pollack",
  title =        "Incremental Commitment in Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "710--717",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{watson:1999:HS,
  author =       "Richard A. Watson and Jordan B. Pollack",
  title =        "Hierarchically consistent test problems for genetic
                 algorithms: Summary and additional results",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "292--297",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "Genetic Algorithms",
  notes =        "GECCO-99LB",
}

@InCollection{waugh:1994:priceoi,
  author =       "Lawrence Waugh",
  title =        "Complexity and Survivability: The Price of
                 Intelligence under Genetic Pressure",
  booktitle =    "Artificial Life at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "187--195",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, machine learning, complexity,
                 natural selection",
  ISBN =         "0-18-182105-2",
  notes =        "Alife toroidal world simulation. Creature's brain is
                 neural network.

                 This volume contains 22 papers written and submitted by
                 students describing their term projects for the course
                 in artificial life (Computer Science 425) at Stanford
                 University offered during the spring quarter quarter
                 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs425.html",
}

@InCollection{wayland:2000:SPAEGP,
  author =       "John Wayland",
  title =        "Solving the 5-Tile Puzzle: An Exercise in Genetic
                 Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "435--441",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@Misc{wayner:2002:db,
  author =       "Peter Wayner",
  title =        "Digital Biology",
  howpublished = "http://slashdot.org/article.pl?sid=02/03/04/195222",
  year =         "2002",
  month =        "11 " # mar,
  note =         "Book review",
  keywords =     "genetic algorithms",
  size =         "pages",
  abstract =     "Does a good job of bridging the analogical gap between
                 the worlds of computers and biology; may not be deep
                 but will probably enlighten readers with an interest in
                 either or both of these fields. Peter J. Bentley's book
                 {"}Digital Biology{"}, ISBN 0-7432-0447-6",
}

@Misc{weinbrenner:1997:diploma,
  author =       "Thomas Weinbrenner",
  title =        "Genetic Programming Techniques Applied to Measurement
                 Data",
  howpublished = "Diploma Thesis",
  year =         "1997",
  school =       "Institute for Mechatronics, Technical University of
                 Darmstadt",
  type =         "Diplomabeit 1362",
  address =      "Germany",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, System
                 identification, Genetic Programming C++ class library,
                 helicopter engine",
  URL =          "http://www.emk.e-technik.tu-darmstadt.de/~thomasw/da1362.ps.gz",
  size =         "79 pages",
  abstract =     "In this project, an unknown system structure was
                 identified using the Genetic Programming technique. A
                 program was developed that, instead of combining linear
                 systems, evolves nonlinear ordinary differential
                 equations to describe a system. The solution space was
                 increased by using this approach. Automatically defined
                 functions were used to represent the ordinary
                 differential equations.

                 A new way was introduced to speed up the evolution of
                 genetic trees. The genetic trees representing the
                 equations were written in C notation to a file to be
                 compiled by a C compiler and evaluated by the computer.
                 This required modifications to the Genetic Programming
                 kernel formerly used. The modifications facilitated
                 evaluation of a complete generation at one time to
                 minimise compiler overhead. In order to build up a C++
                 class hierarchy, the kernel was completely
                 restructured. A lot of features like shrink mutation,
                 variable tournament size, improved deme handling etc.
                 were also added. A parameter study was carried out to
                 investigate the influence of important control
                 parameters.

                 The Genetic Programming system was applied to a known,
                 nonlinear system to verify its ability. After this
                 proved to be successful, the input/output response data
                 of a helicopter engine was used to identify that
                 system. Candidate models were derived from that
                 analysis.",
  notes =        "Documentation on final year project. New version of
                 Adam Fraser's GPc++
                 http://www.emk.e-technik.tu-darmstadt.de/~thomasw/gpc++0.5.2.tar.gz",
}

@InProceedings{weinert2:2001:gecco,
  title =        "Reconstruction of Particle Flow Mechanisms with
                 Symbolic Regression via Genetic Programming",
  author =       "Klaus Weinert and Marc Stautner",
  pages =        "1439--1443",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithm, genetic programming, real world
                 applications, Symbolic regression, Flow mechanisms,
                 Mechanical engineering",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{weinert:2001:gecco,
  title =        "Evolutionary Surface Reconstruction Using
                 {CSG}-{NURBS}-Hybrids",
  author =       "Klaus Weinert and Tobias Surmann and Jorn Mehnen",
  pages =        "1456",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and W. B.
                 Langdon and Hans-Michael Voigt and Mitsuo Gen and
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max
                 H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "real world applications: Poster, genetic programming,
                 surface reconstruction, NURBS, CSG",
  ISBN =         "1-55860-774-9",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of spector:2001:GECCO",
}

@InProceedings{weinert:2002:EuroGP,
  title =        "Parallel Surface Reconstruction",
  author =       "Klaus Weinert and Tobias Surmann and J{\"o}rn Mehnen",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "93--102",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "The task of surface reconstruction is to find a
                 mathematical representation of a surface which is given
                 only by a set of discrete sampling points. The
                 mathematical description in the computer allows to save
                 or transfer the geometric data via internet, to
                 manipulate (e.g. for aerodynamic or design specific
                 reasons) or to optimize the machining of the work
                 pieces. The reconstruction of the shape of an object is
                 a difficult mathematical and computer scientific
                 problem. For this reason a GP/ES-hybrid algorithm has
                 been used. Due to the high complexity of the problem
                 and in order to speed up the reconstruction process,
                 the algorithm has been enhanced to work as a
                 multipopulation GP/ES that runs in parallel on a
                 network of standard PCs.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{weinert:2002:EuroGPa,
  title =        "A New View on Symbolic Regression",
  author =       "Klaus Weinert and Marc Stautner",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "113--122",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "Symbolic regression is a widely used method to
                 reconstruct mathematical correlations. This paper
                 presents a new graphical representation of the
                 individuals reconstructed in this process. This new
                 three dimensional representation allows the user to
                 recognize certain possibilities to improve his setup of
                 the process parameters. Furthermore this new
                 representation allows a wider usage of the generated
                 three dimensional objects with nearly every CAD program
                 for further use. To show the practical usage of this
                 new representation it was used to reconstruct
                 mathematical descriptions of the dynamics in a
                 machining process namely in orthogonal cutting.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{weiss:1999:TGAIPPSE,
  author =       "Gary M. Weiss",
  title =        "Timeweaver: a Genetic Algorithm for Identifying
                 Predictive Patterns in Sequences of Events",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "718--725",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{weller:2002:GVGEBI,
  author =       "Chris Weller",
  title =        "Generation of Vector-Based Graphics from Existing
                 Bitmap Images by Means of the Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "243--252",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp fitness based on pixel wise
                 comparison",
}

@PhdThesis{werner:thesis,
  author =       "James Cunha Werner",
  title =        "Active Noise Control in Ducts Using Genetic
                 Algorithms",
  school =       "Mechanical Engineering Department Sao Paulo
                 University",
  year =         "1999",
  address =      "Brazil",
  month =        sep # " 24",
  email =        "jamwer@usp.br",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://puck.mcca.ep.usp.br/~jamwer/tese.mpg huge
                 movie",
  size =         "pages",
  abstract =     "Genetic Programming + Genetic algorithm = Genetic
                 Control

                 This thesis addresses the problem of actively control
                 acoustic noise in ducts through the application of
                 genetic algorithm - GA and genetic programming - GP
                 (called genetic control - GC). Genetic programming
                 obtain a self structured autonomous control model and
                 genetic algorithms adapt model's parameters under real
                 time. Three different strategies were adopted with GA.
                 In the Simple Genetic Algorithm (SGA) each individual
                 of a generation represents a specific frequency, phase
                 and amplitude used in cancellation of noise and the
                 fitness function is the average energy of the signal.
                 The Successive Approach Genetic Algorithm (SAGA) is a
                 modification of SGA, where a first level procedure
                 searches for candidate frequencies and a second level
                 improves them between fixed limits, with phase and
                 amplitude. To run in real time, a gain/delay model was
                 coded into the chromosome. A simulation model was
                 developed to test the software and to analyses the
                 behavior of the genetic algorithm parameters. The
                 software was designed to work in a parallel DSP
                 TMS320C44 architecture managing processors
                 communication and shared memory with high performance.
                 A mono processor version was developed to control the
                 duct system under real time with noise reduction. The
                 acoustic feedback was removed through the microphone
                 confinement, special sound boxes and through adaptive
                 model approach. Genetic programming applied to the
                 system converges to the genetic algorithms gain/delay
                 model as foreseen by the theory and experiment",
  abstract =     "Esta tese estuda o problema de controlar o ruído
                 acústico em dutos mediante o fornecimento de energia
                 acústica, através da associação do algoritmo genético -
                 GA e da programação genética - GP (constituindo o
                 controle genético - GC).A programação genética é
                 utilizada para obter um modelo de controle auto
                 estruturado e autônomo, e o algoritmo genético é
                 utilizado para adaptar os parâmetros do modelo em tempo
                 real. Foram adotadas três estratégias de adaptação
                 usando o GA. Uma, com o algoritmo genético simples
                 (SGA): cada indivíduo de uma geração representa uma
                 freqüência, fase e amplitude específicas, usadas no
                 cancelamento do ruído, sendo a função de desempenho
                 obtida pela média da energia do sinal. Segunda, a de
                 refinamento sucessivo (SAGA) foi utilizada em dois
                 níveis: um nível codificando a freqüência e depois um
                 nível refinando-a junto com a fase e a amplitude.
                 Finalmente, a terceira abordagem utiliza em tempo real
                 um modelo de atraso e ganho codificado no cromossomo.
                 Um simulador foi desenvolvido com um modelo
                 simplificado para testar o software e para analisar o
                 comportamento dos parâmetros do algoritmo genético. O
                 software foi migrado para trabalhar em arquitetura
                 paralela de DSPs TMS320C44, gerenciando a comunicação
                 entre os processadores e a memória compartilhada com
                 alto desempenho. Uma versão com um processador
                 TMS320C32 foi desenvolvida para controlar o sistema do
                 duto em tempo real, reduzindo o ruído em todas as
                 faixas de freqüência. O tratamento da realimentação
                 acústica foi feito através de: confinamento do
                 microfone, confecção de caixas acústicas especiais e
                 mediante a remoção através de um modelo baseado na
                 técnica adaptativa. A programação genética aplicada ao
                 sistema, convergiu para o modelo de atraso e ganho,
                 utilizado pelo GA e previsto pela teoria.",
  notes =        "in Portuguese",
}

@InProceedings{werner:2001:idamap,
  author =       "James Cunha Werner and Terence C. Fogarty",
  title =        "Genetic programming applied to severe diseases
                 diagnosis",
  booktitle =    "Procedings Intelligent Data Analysis in Medicine and
                 Pharmacology (IDAMAP-2001)",
  year =         "2001",
  note =         "a workshop at MedInfo-2001",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 classification",
  URL =          "http://magix.fri.uni-lj.si/idamap2001/papers/werner.pdf",
  notes =        "IDAMAP workshop
                 http://magix.fri.uni-lj.si/idamap2001/",
}

@InProceedings{werner:2002:EuroGP,
  title =        "Genetic control applied to asset managements",
  author =       "James Cunha Werner and Terence C. Fogarty",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "192--201",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "This paper address the problem of investment
                 optimisation, with deals with obtain stock time series
                 from data extracted of graphics available in internet,
                 predict assets price by adaptive algorithms, optimise
                 the portfolio with genetic algorithms and obtain a
                 recursive model of portfolio composition on-fly using
                 genetic programming, all steps integrated to obtain an
                 automatic management. The final result is a real-time
                 update portfolio composition for each asset.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{westerberg:2001:EvoWorks,
  author =       "C. Henrik Westerberg and John Levine",
  title =        "Investigatioons of Different Seeding Strategies in a
                 Genetic Planner",
  booktitle =    "Applications of Evolutionary Computing",
  year =         "2001",
  editor =       "Egbert J. W. Boers and Stefano Cagnoni and Jens
                 Gottlieb and Emma Hart and Pier Luca Lanzi and Gunther
                 R. Raidl and Robert E. Smith and Harald Tijink",
  volume =       "2037",
  series =       "LNCS",
  pages =        "505--514",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, population
                 seeding, classical planning, STRIPS, one point
                 crossover, linear representation, plan, artificial
                 intellience, blocks world",
  ISBN =         "3-540-41920-9",
  URL =          "http://www.dai.ed.ac.uk/homes/carlw/publications/EvoSTIM_HenrikWesterberg.ps",
  notes =        "EvoWorkshops2001. Fitness by simulation",
}

@InProceedings{westerberg:2002:gecco:workshop,
  title =        "Elite Crossover in Genetic Planning",
  author =       "C. Henrik Westerberg",
  pages =        "311--314",
  booktitle =    "Graduate Student Workshop",
  editor =       "Sean Luke and Conor Ryan and Una-May O'Reilly",
  year =         "2002",
  month =        "8 " # jul,
  publisher =    "AAAI",
  address =      "New York",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Bird-of-a-feather Workshops, GECCO-2002. A joint
                 meeting of the eleventh International Conference on
                 Genetic Algorithms (ICGA-2002) and the seventh Annual
                 Genetic Programming Conference (GP-2002) part of
                 barry:2002:GECCO:workshop",
}

@InProceedings{Westerdale:1997:nowork,
  author =       "T. H. Westerdale",
  title =        "Classifier Systems--No Wonder They Don't Work",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "genetic algorithms, classifier systems",
  pages =        "529--537",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{whigham:1994:GPsi,
  author =       "P. Whigham",
  title =        "Genetic programming and spatial information",
  booktitle =    "Proceedings of the 7th Australian Joint Conference on
                 Artificial Intelligence (AI'94)",
  year =         "1994",
  editor =       "C.~Zhang and J. Debenham and D. Lukose",
  pages =        "124--131",
  publisher_address = "Singapore",
  publisher =    "World Scientific Publishing Company",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{whigham:1995:GBGP,
  author =       "P. A. Whigham",
  title =        "Grammatically-based Genetic Programming",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "33--41",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://www.cs.adfa.oz.au/pub/xin/whigham_ml95.ps.Z",
  size =         "9 pages",
  abstract =     "context free grammar to define the structure of the
                 initial population and to direct crossover and muation
                 operators",
  notes =        "part of rosca:1995:ml",
}

@TechReport{whigham:1995:ggls,
  author =       "Peter A. Whigham",
  title =        "Grammatical Genetic Learning and Schemata: Restated",
  institution =  "Department of Computer Science, University College,
                 University of New South Wales, Australia",
  year =         "1995",
  type =         "Technical Report",
  number =       "CS13/95",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{whigham:1995:ingp,
  author =       "P. A. Whigham",
  title =        "Inductive Bias and Genetic Programming",
  booktitle =    "First International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1995",
  editor =       "A. M. S. Zalzala",
  volume =       "414",
  pages =        "461--466",
  address =      "Sheffield, UK",
  publisher_address = "London, UK",
  month =        "12-14 " # sep,
  publisher =    "IEE",
  keywords =     "genetic algorithms, genetic programming, context free
                 grammar",
  ISBN =         "0-85296-650-4",
  URL =          "ftp://www.cs.adfa.oz.au/pub/xin/whigham_galesia.ps.Z",
  notes =        "12--14 September 1995, Halifax Hall, University of
                 Sheffield, UK see also
                 http://www.iee.org.uk/LSboard/Conf/program/galprog.htm

                 Using 6-multiplexor problem shows using a syntax (of
                 the correct sort, specified using a context free
                 grammar) to constrain the form of the program trees
                 helps GP solve the problem. More restrictions, easier
                 it is.

                 Then presents a method based on the syntax of the
                 fitest member of the population to modify the grammar
                 whilst the GP runs. Shows improvement on 6-multiplexor.
                 Still greater improvements obtained by introducing a
                 fitness for rules within the grammar. This weakly
                 biases the grammar, ie all legal program are still
                 legal, but now some are more likley to be produced than
                 they where before the fitness of the grammar rules
                 where changed.

                 10\% of population each generation regenerated using
                 his {"}replacement{"} operator.",
}

@InCollection{whigham:1995:glrr,
  author =       "P. A. Whigham and R. I. McKay",
  title =        "Genetic approaches to learning recursive relations",
  booktitle =    "Progress in Evolutionary Computation",
  publisher =    "Springer-Verlag",
  year =         "1995",
  editor =       "X. Yao",
  volume =       "956",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "17--27",
  publisher_address = "Heidelberg, Germany",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InProceedings{whigham:1995:stcfg,
  author =       "P. A. Whigham",
  title =        "A Schema Theorem for Context-Free Grammars",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "1",
  pages =        "178--181",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "hftp://www.cs.adfa.oz.au/pub/xin/whigham_schema.ps.Z",
  abstract =     "The basic Schema Theorem for genetic algorithms is
                 modified for a grammatically-based learning system. A
                 context-free grammar is used to define a language in
                 which each sentence is mapped to a fitness value. The
                 derivation trees associated with these sentences are
                 used to define the structure of schemata. The effect of
                 crossover and mutation on schemata is described. A
                 schema theorem is developed which describes how
                 sentences of a language are propagated during
                 evolution.",
  notes =        "ICEC-95 Held December 1995, at University of Western
                 Australia, Perth, Australia. Editors not given by IEEE,
                 Organisers David Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html",
}

@InProceedings{whigham:1996:sblbGP,
  author =       "P. A. Whigham",
  title =        "Search Bias, Language Bias, and Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "230--237",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "ftp://dwr-ftp.adl.dwr.csiro.au/pub/gp96/whighamgp96.ps",
  url_2 =        "ftp://www.cs.adfa.oz.au/pub/xin/whigham_gp96.ps.gz",
  size =         "9 pages",
  abstract =     "The use of bias with automated learning systems has
                 become an important area of research. The use of bias
                 with evolutionary techniques of learning has been shown
                 to have advantages when complex structures are evolved.
                 This is especially true when the semantics of the
                 evolving population of structures is not explicitly
                 represented or analysed during the evolution. This
                 paper describes a framework which brings together two
                 types of bias, namely search bias (the way new
                 structures are created) and language bias (the form of
                 possible structures that may be created). The resulting
                 system extends genetic programming by allowing
                 declarative bias with both the form of possible
                 solutions that are created and the method by which they
                 are transformed.",
  notes =        "GP-96

                 ",
}

@PhdThesis{whigham:1996:phd,
  author =       "Peter Alexander Whigham",
  title =        "Grammatical Bias for Evolutionary Learning",
  school =       "School of Computer Science, University College,
                 University of New South Wales, Australian Defence Force
                 Academy",
  year =         "1996",
  address =      "Canberra, Australia",
  month =        "14 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://www.cs.adfa.oz.au/pub/xin/whigham_thesis.ps.gz",
  URL =          "http://divcom.otago.ac.nz/sirc/Peterw/Publications/thesis.zip",
  size =         "16 pages",
}

@Unpublished{whigham:1997:mrr,
  author =       "P. A. Whigham and P. F. Crapper",
  title =        "Applying Genetic Programming to Model
                 Rainfall-Runoff",
  note =         "CSIRO Land and Water, Canberra, Australia",
  month =        oct,
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Draft. Welsh and austrialian riverbasins modelled.
                 IHACRES unsatisfactory on oz catchment area but ok in
                 wales. CFG-GP ok on both. Published as
                 whigham:1997:mrrP ?",
  size =         "6 pages",
}

@InProceedings{whigham:1997:mrrP,
  author =       "P. A. Whigham and P. F. Crapper",
  title =        "Applying Genetic Programming to Model
                 Rainfall-Runoff",
  booktitle =    "International Congress on Modelling and Simulation:
                 Proceedings",
  year =         "1997",
  editor =       "D. McDonald and M. McAleer",
  month =        "8-11 Decemeber",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "see also whigham:1997:mrr

                 ",
}

@InProceedings{whigham:1997:epdfg,
  author =       "P. A. Whigham",
  title =        "Evolving a Program defined by a Formal Grammar",
  booktitle =    "Fourth International Conference on Neural Information
                 Processing -- The Annual Conference of the Asian
                 Pacific Neural Network Assembly (ICONIP'97)",
  year =         "1997",
  address =      "Dunedin, New Zealand",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "

                 ",
}

@InCollection{whigham:1999:aigp3,
  author =       "Peter A. Whigham and Peter F. Crapper",
  title =        "Time series Modelling Using Genetic Programming: An
                 Application to Rainfall-Runoff Models",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "5",
  pages =        "89--104",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@Article{whigham:2001:edc,
  author =       "Peter A. Whigham",
  title =        "Book Review: Evolutionary Design by Computers",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "1",
  pages =        "79--84",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware",
  ISSN =         "1389-2576",
  notes =        "{"}Evolutionary Design by Computers{"} was edited by
                 Peter J Bentley and published by Morgan Kaufmann ISBN
                 1-55860-605-X",
}

@InProceedings{whigham:2001:esc,
  author =       "P. A. Whigham and J. Keukelaar",
  title =        "Evolving Structure-Optimising Content",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1228--1235",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, grammar",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 TSOGP hybrid context-free grammar GP with... Powell,
                 simplex. variable arity tree nodes. Evaluation tree
                 speedup via terminal links. Lake Kasumigaura (Japan)
                 water quality.",
}

@Article{Whigham:2000:EM,
  author =       "P. A. Whigham",
  title =        "Induction of a marsupial density model using genetic
                 programming and spatial relationships",
  journal =      "Ecological Modelling",
  volume =       "131",
  pages =        "299--317",
  year =         "2000",
  number =       "2-3",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Machine learning techniques have been developed that
                 allow the induction of spatial models for the
                 prediction of habitat types and population
                 distribution. However, most learning approaches are
                 based on a propositional language for the development
                 of models and therefore cannot express a wide range of
                 possible spatial relationships that exist in the data.
                 This paper compares the application of a functional
                 evolutionary machine learning technique for prediction
                 of marsupial density to some standard machine learning
                 techniques. The ability of the learning system to
                 express spatial relationships allows an improved
                 predictive model to be developed, which is both
                 parsimonious and understandable. Additionally, the maps
                 produced from this approach have a generalised
                 appearance of the measured glider density, suggesting
                 that the underlying preferred habitat properties of the
                 greater glider have been identified.",
}

@Article{Whigham:2001:EM2,
  author =       "Peter A. Whigham and Friedrich Recknagel",
  title =        "An inductive approach to ecological time series
                 modelling by evolutionary computation",
  year =         "2001",
  journal =      "Ecological Modelling",
  volume =       "146",
  pages =        "275--287",
  number =       "1-3",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6VBS-44HYNCP-T/1/d33b3386f4d8934ac004f4d985e411ba",
  abstract =     "Building time series models for ecological systems
                 that can be physically interpreted is important both
                 for understanding the dynamics of these natural systems
                 and the development of decision support systems. This
                 work describes the application of an evolutionary
                 computation framework for the discovery of predictive
                 equations and rules for phytoplankton abundance in
                 freshwater lakes from time series data. The suggested
                 framework evolves several different equations and
                 rules, based on limnological and climate variables. The
                 results demonstrate that non-linear processes in
                 natural systems may be successfully modelled through
                 the use of evolutionary computation techniques.
                 Further, it shows that a grammar based genetic
                 programming system may be used as a tool for exploring
                 the driving processes underlying freshwater system
                 dynamics.",
}

@Article{Whigham:2001:MCM,
  author =       "P. A. Whigham and P. F. Crapper",
  title =        "Modelling rainfall-runoff using genetic programming",
  journal =      "Mathematical and Computer Modelling",
  volume =       "33",
  pages =        "707--721",
  year =         "2001",
  number =       "6-7",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V0V-42R1KRY-G/1/226d0ab4c2f13472b01ada47c8473fbf",
  abstract =     "Genetic programming is an inductive form of machine
                 learning that evolves a computer program to perform a
                 task defined by a set of presented (training) examples
                 and has been successfully applied to problems that are
                 complex, nonlinear and where the size, shape, and
                 overall form of the solution are not explicitly known
                 in advance. This paper describes the application of a
                 grammatically-based genetic programming system to
                 discover rainfall-runoff relationships for two vastly
                 different catchments. A context-free grammar is used to
                 define the search space for the mathematical language
                 used to express the evolving programs. A daily time
                 series of rainfall-runoff is used to train the evolving
                 population. A deterministic lumped parameter model,
                 based on the unit hydrograph, is compared with the
                 results of the evolved models on an independent data
                 set. The favourable results of the genetic programming
                 approach show that machine learning techniques are
                 potentially a useful tool for developing hydrological
                 models, especially when surface water movement and
                 water losses are poorly understood.",
}

@InProceedings{white:1998:ASGA,
  author =       "Tony White and Bernard Pagurek and Franz Oppacher",
  title =        "{ASGA}: Improving the Ant System by Integration with
                 Genetic Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "610--617",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  notes =        "SGA-98",
}

@InProceedings{white:1999:AORBA,
  author =       "Tony White and Bernard Pagurek",
  title =        "Application Oriented Routing with
                 Biologically-inspired Agents",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1453--1454",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{whitley:1995:pole,
  author =       "Darrell Whitley and Frederic Gruau and Larry Pyeatt",
  title =        "Cellular Encoding Applied to Neurocontrol",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "L. Eshelman",
  pages =        "460--467",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "Genetic Programming, Genetic Algorithms",
  ISBN =         "1-55860-370-0",
  size =         "8 pages",
}

@InProceedings{whitley:1999:AFLPGBE,
  author =       "D. Whitley",
  title =        "A Free Lunch Proof for Gray versus Binary Encodings",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "726--733",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  abstract =     "{"}Gray codes [better than binary] over a clear and
                 pragmatically defined subset of all possible
                 functions{"} p733",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Proceedings{whitley:2000:GECCO,
  title =        "Proceedings of the Genetic and Evolutionary
                 Computation Conference ({GECCO}-2000)",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming, evolvable
                 hardware, classifier systems, evolution strategies,
                 evolutionary programming, artificial life, adaptive
                 agents, ant colony optimization, DNA computing,
                 molecular computing, neural networks, data mining,
                 evolutionary robotics, genetic scheduling",
  ISBN =         "1-55860-708-0",
  URL =          "http://www.cs.colostate.edu/~genitor/GECCO-2000/gecco2000mainpage.htm",
  URL =          "http://www.mkp.com/books_catalog/catalog.asp?ISBN=1-55860-774-9",
  size =         "1088 pages",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000)",
}

@Proceedings{whitley:2000:GECCOlb,
  title =        "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  URL =          "http://www.cs.colostate.edu/~genitor/GECCO-2000/late-breaking-schedule.htm",
  size =         "444 pages",
}

@Article{Whitley:2001:IST,
  author =       "Darrell Whitley",
  title =        "An overview of evolutionary algorithms: practical
                 issues and common pitfalls",
  journal =      "Information and Software Technology",
  year =         "2001",
  volume =       "43",
  pages =        "817--831",
  number =       "14",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V0B-44D4196-3/1/f92596f9bf285c4ec4553d4cc40da4a0",
  abstract =     "An overview of evolutionary algorithms is presented
                 covering genetic algorithms, evolution strategies,
                 genetic programming and evolutionary programming. The
                 schema theorem is reviewed and critiqued. Gray codes,
                 bit representations and real-valued representations are
                 discussed for parameter optimization problems. Parallel
                 Island models are also reviewed, and the evaluation of
                 evolutionary algorithms is discussed.",
}

@Unpublished{widland:1999:ehiGP,
  author =       "Tom Widland and Kevin Oishi and Alex Feuchter and Ryan
                 Duryea and Ryan Davies",
  title =        "Evolution of Hive Intelligence Using Genetic
                 Programming",
  note =         "WWW pages",
  year =         "1999",
  email =        "Tom Widland <tomwid@bigfoot.com>",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://mode.lanl.k12.nm.us/~ch006kto/",
  abstract =     "Executive Summary: Our project deals with the
                 evolution of hive intelligence using genetic
                 programming with the classic video game Pacman as our
                 model environment. Pacman is an arcade game where a
                 group of {"}ghosts{"} try to catch a Pacman as he
                 attempts to eat all the dots in a maze in order to
                 progress to the next level. Hive intelligence is the
                 concept that a group of individual organisms working
                 together as a cohesive unit can efficiently accomplish
                 a defined task. In our model of Pacman, the ghosts are
                 the individual organisms that are assigned the task of
                 catching Pacman in a maze as quickly as possible. They
                 work together as a team, communicating with each other
                 to catch the Pacmen. At the end of each simulation our
                 program rates them on a fitness scale to determine
                 their prowess as a team. The ghost team that catches
                 the most Pacmen in a specified amount of time gets the
                 highest fitness score. We take the fittest teams and
                 mix their programs (genes) together using a crossover
                 algorithm. We then run another series of simulations
                 and our program tests the fitness of the new generation
                 of ghost teams. Our results show that genetic
                 programming is a powerful means of evolving a routine
                 to be more effective then any human created algorithm.
                 The applications of such a process are staggering. In
                 almost any situation in which computer programs are
                 used to perform a single, definable task in varying
                 situations, genetic programming can be used to increase
                 the efficiency of the program. From simulating the
                 function of organs in the human body to the exploration
                 of planets, genetic programming is a useful tool in
                 creating the best routines for the job.",
  size =         "pages",
}

@InProceedings{Wiens:2000:GECCOlb,
  author =       "Andrea L. Wiens and Brian J. Ross",
  title =        "Gentropy: Evolutionary 2{D} Texture Generation",
  pages =        "418--424",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Part of whitley:2000:GECCOlb",
}

@Article{wiens:2002:cg,
  author =       "Andrea L. Wiens and Brian J. Ross",
  title =        "Gentropy: Evolutionary 2{D} Texture Generation",
  journal =      "Computers and Graphics",
  year =         "2002",
  volume =       "26",
  number =       "1",
  pages =        "75--88",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Procedural
                 textures, Evolution, graphics",
  URL =          "http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6TYG-4549VJN-2-15&_cdi=5618&_orig=browse&_coverDate=02%2F28%2F2002&_sk=999739998&wchp=dGLSlV-lSzBS&_acct=C000010182&_version=1&_userid=125795&md5=15555074969fef108d1b1b0fcbecf47e&ie=f.pdf",
  URL =          "http://www.cosc.brocku.ca/~bross/research/Gentropy_evolving_2D_textures.pdf",
  abstract =     "Gentropy is a genetic programming system that evolves
                 two-dimensional procedural textures. It synthesizes
                 textures by combining mathematical and image
                 manipulation functions into formulas. A formula can be
                 re-evaluated with arbitrary texture-space coordinates,
                 to generate a new portion of the texture in texture
                 space. Most evolutionary art programs are interactive,
                 and require the user to repeatedly choose the best
                 images from a displayed generation. Gentropy uses an
                 unsupervised approach, where one or more target texture
                 image are supplied to the system, and represent the
                 desired texture features, such as colour, shape and
                 smoothness (contrast). Then, Gentropy evolves textures
                 independent of any further user involvement. The
                 evolved texture will not be identical to the target
                 texture, but rather, will exhibit characteristics
                 similar to it. When more than one texture is supplied
                 as a target, multiobjective feature analysis is
                 performed. These feature tests may be combined and
                 given different priorities during evaluation. It is
                 therefore possible to use several target images, each
                 with its own fitness function measuring particular
                 visual characteristics. Gentropy also permits the use
                 of multiple subpopulations, each of which may use its
                 own texture evaluation criteria and target texture.",
  notes =        "http://www.cosc.brocku.ca/~bross/gentropy/",
}

@InCollection{willeke:1995:GEBR,
  author =       "Thomas Willeke",
  title =        "Genetic Evolution of Behavior-Oriented Robots",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "301--308",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{williams:1999:TERAP,
  author =       "Kenneth P. Williams and Shirley A. Williams",
  title =        "Two Evolutionary Representations for Automatic
                 Parallelization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1429--1436",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "artificial life, adaptive behavior and agents",
  ISBN =         "1-55860-611-4",
  abstract =     "REVOLVER",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99).

                 Variable length representation and crossover. Program
                 loop transformation heuristics order and parameters
                 controlled by small population of evolving chromosomes.
                 Hill climbing, Simulated Annealing,
                 evolutionarystratigies, genetic algorithms used to
                 search",
}

@Article{Willihnganz:1999:s2s,
  author =       "Alexis Willihnganz",
  title =        "Software that writes software",
  journal =      "salon.com",
  year =         "1999",
  note =         "www article",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.salon.com/tech/feature/1999/08/10/genetic_programming/",
  size =         "3 pages",
  abstract =     "non technical popularist overview of GP",
}

@InProceedings{willis:1997:GPsurvey,
  author =       "Mark Willis and Hugo Hiden and Peter Marenbach and Ben
                 McKay",
  title =        "Genetic Programming: An Introduction and Survey of
                 Applications",
  booktitle =    "Second International Conference on Genetic Algorithms
                 in Engineering Systems: Innovations and Applications,
                 GALESIA",
  year =         "1997",
  editor =       "Ali Zalzala",
  address =      "University of Strathclyde, Glasgow, UK",
  publisher_address = "Savoy Place, London WC2R 0BL, UK",
  month =        "1-4 " # sep,
  publisher =    "Institution of Electrical Engineers",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper14.ps",
  abstract =     "In this paper Genetic Programming (GP) is described.
                 After an introduction to the basic methodology, areas
                 where GP has been applied are reviewed. A simple
                 tutorial example is used to illustrate the
                 functionality and utility of the GP approach.",
  notes =        "GALESIA'97

                 Online version is unfinished, Sep 97",
}

@Article{willis:1997:smGP,
  author =       "Mark Willis and Hugo Hiden and Mark Hinchliffe and Ben
                 McKay and Geoffrey W. Barton",
  title =        "Systems Modelling Using Genetic Programming",
  journal =      "Computers in Chemical Engineering",
  year =         "1997",
  volume =       "21",
  pages =        "S1161--1166",
  note =         "Supplemental",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "GP empirical model of vacuum distillation column and a
                 twin screw extruder for processing corn flour.
                 Comparison of artifical neural network and GP",
}

@InProceedings{willis:1997:ieaGP,
  author =       "M. J. Willis and H. G. Hiden and G. A. Montague",
  title =        "Developing Inferential Estimation Algorithms Using
                 Genetic Programming",
  booktitle =    "IFAC/ADCHEM International Symposium on Advanced
                 Control of Chemical Processes",
  year =         "1997",
  pages =        "219--224",
  address =      "Banaff, Canada",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "{"}For the industrial case study,...GP compared with
                 finite impulse res[ponse model and feedforward
                 artificial neural network....GP produces models with a
                 significantly lower root mean square error{"}",
  notes =        "model of plasticating extruder. {"}multiple gene{"}
                 model structure. Fitness proportionare selection.
                 mutation",
}

@InProceedings{wilson:1998:gXCScs,
  author =       "Stewart W. Wilson",
  title =        "Generalization in the {XCS} Classifier System",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "665--674",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InCollection{wilson:2002:FSVPEGP,
  author =       "Robert Scott Wilson",
  title =        "First Steps towards Violin Performance Extraction
                 using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "253--262",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{wineberg:1994:rsppi,
  author =       "Mark Wineberg and Franz Oppacher",
  title =        "A Representation Scheme to Perform Program Induction
                 in a Canonical Genetic Algorithm",
  booktitle =    "Parallel Problem Solving from Nature III",
  year =         "1994",
  editor =       "Yuval Davidor and Hans-Paul Schwefel and Reinhard
                 M{\"a}nner",
  series =       "LNCS",
  volume =       "866",
  pages =        "292--301",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  month =        "9-14 " # oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-58484-6",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
  abstract =     "Studies GP and its relationship to the GA. An
                 appropriate representation scheme is delveloped (EP-I
                 Evolutionary Programming with Introns) EP-I
                 demonstrated to perform identically to GP on 3
                 problems. EP-I able to simulate GP exactly, gaining
                 insights into GP as a GA.",
  notes =        "PPSN3",
}

@InProceedings{wineberg:1996:bci,
  author =       "Mark Wineberg and Franz Oppacher",
  title =        "The Benefits of Computing with Introns",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "410--415",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "6 pages",
  notes =        "GP-96",
}

@InProceedings{Winkeler:1997:GPod,
  author =       "Jay F. Winkeler and B. S. Manjunath",
  title =        "Genetic Programming for Object Detection",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "330--335",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{winkeler:1998:ieGP,
  author =       "Jay F. Winkeler and B. S. Manjunath",
  title =        "Incremental Evolution in Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "403--411",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@Article{winter:1994:est,
  author =       "C. S. Winter and P. W. A. McIlroy and J. L.
                 Fernandes-Villacanas",
  title =        "Evolving Software Techniques",
  journal =      "BT Technology Journal",
  year =         "1994",
  volume =       "12",
  number =       "2",
  pages =        "121--131",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "British Telecommunications, UK {"}Software engineering
                 is still a craftsman's industry, awaiting the
                 development of mass production. This paper describes
                 how the computer could replace the craftsman
                 programmer...{"} {"}Thus, if genetic programming can be
                 shown to scale, it is predicted that around the turn of
                 the century such techniques will compete with humans at
                 writing code{"}. {"}fitness specification language{"}.
                 Discuses Artificial life, classifiers, genetic
                 algorithms, genetic programming. BT Hermes A-life
                 system described.",
}

@InProceedings{withall:2002:gecco,
  author =       "Mark S. Withall and Chris J. Hinde and Roger G.
                 Stone",
  title =        "Evolving Readable {Perl}",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and
                 V. Honavar and G. Rudolph and J. Wegener and L. Bull
                 and M. A. Potter and A. C. Schultz and J. F. Miller and
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "894",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "learning classifier systems, genetic programming,
                 Perl, readable, symbolic regression",
  ISBN =         "1-55860-878-8",
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)

                 See also Withall:2002:gecco:lbp",
}

@InProceedings{withall:2002:gecco:lbp,
  title =        "Evolving Perl",
  author =       "Mark S. Withall and Chris J. Hinde and Roger G.
                 Stone",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "474--481",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 7 list operations. Fixed length chromosome (40 or
                 60).",
}

@InProceedings{wolff:2001:eegabruvf,
  author =       "Krister Wolff and Peter Nordin",
  title =        "Evolution of Efficient Gait with Autonomous Biped
                 Robot Using Visual Feedback",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "482--489",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, Elvina",
  notes =        "GECCO-2001LB
                 http://publik.svt.se/sth/index.nsf/8ed90fd2b855a062412566c000444c6d/82c0333920443814c125698900393e56/Body/4.11A8?OpenElement&FieldElemFormat=gif
                 robot weight 1.490kg height 11inches, EyeBot Mk3
                 RoBIOS, full colour 24bit digital camera, infrared
                 range sensor. Seeded population? popsize 30. 4
                 tournament selection, steady state, 126 genes,
                 recombination + mutation.",
}

@InProceedings{wollesen:1999:B,
  author =       "Eric A. Wollesen and Nicolai Krakowiak and Jason M.
                 Daida",
  title =        "Beowulf anytime for evolutionary computation",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "298--304",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InCollection{wong:2000:SMSPLHMMUGP,
  author =       "Dik Kin Wong",
  title =        "Simultaneous Model Selection and Parameter Learning of
                 Hidden Markov Model Using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "442--451",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{wong:1999:EPBMEPF,
  author =       "Kit Po Wong and Jason Yuryevich and An Li",
  title =        "Evolutionary Programming Based Method for Evaluation
                 of Power Flow",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1756--1761",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{wong:1994:ilpuga,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Inductive Logic Programming Using Genetic Algorithms",
  booktitle =    "Advances in Artificial Intelligence - Theory and
                 Application II",
  publisher =    "I.I.A.S.",
  year =         "1994",
  editor =       "J. W. Brahan and G. E. Lasker",
  pages =        "119--124",
  address =      "Ontario, Canada",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "Name: Man Leung Wong

                 ",
}

@InProceedings{wong:1994:l1rnd,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Learning First-order Relations from Noisy Databases
                 using Genetic Algorithms",
  booktitle =    "Proceedings of the Second Singapore International
                 Conference on Intelligent Systems",
  year =         "1994",
  pages =        "B159--164",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "SPICIS-94",
}

@Article{wong:1995:glp,
  author =       "M. L. Wong and K. S. Leung",
  title =        "Genetic Logic Programming and Applications",
  journal =      "IEEE Expert",
  year =         "1995",
  volume =       "10",
  number =       "5",
  pages =        "68--76",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  size =         "22 pages",
  notes =        "IEEE Expert Special Track on Evolutionary Programming
                 (P. J. Angeline editor) angeline:1995:er

                 ",
}

@InProceedings{wong:1995:ilpGP,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "An adaptive Inductive Logic Programming system using
                 Genetic Programming",
  booktitle =    "Evolutionary Programming {IV} Proceedings of the
                 Fourth Annual Conference on Evolutionary Programming",
  year =         "1995",
  editor =       "John Robert McDonnell and Robert G. Reynolds and David
                 B. Fogel",
  pages =        "737--752",
  publisher_address = "Cambridge, MA, USA",
  address =      "San Diego, CA, USA",
  month =        "1-3 " # mar,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-13317-2",
  notes =        "EP-95",
}

@InProceedings{wong:1995:lpdpGP,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Learning Programs in Different Paradigms using Genetic
                 Programming",
  booktitle =    "Proceedings of the Fourth Congress of the Italian
                 Association for Artificial Intelligence",
  year =         "1995",
  publisher_address = "Berlin, Germany",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{wong:1995:islpdpl,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "An Induction System that Learns Programs in different
                 Programming Languages using Genetic Programming and
                 Logic Grammars",
  booktitle =    "Proceedings of the 7th IEEE International Conference
                 on Tools with Artificial Intelligence",
  year =         "1995",
  keywords =     "genetic algorithms, genetic programming, LOGENPRO",
  notes =        "LOGENPRO better than GP on dot vector product.
                 {"}LOGENPRO can emulate the effect of STGP
                 effortlessly{"}. Chess endgame and Prolog fuzzy logic
                 examples.",
}

@InProceedings{wong:1995:cGPilp,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Combining Genetic Programming and Inductive Logic
                 Programming using Logic Grammars",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "733--736",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, LOGENPRO",
  abstract =     "Genetic Programming (GP) and Inductive Logic
                 Programming (ILP) have received increasing interest
                 recently. Since their formalisms are so different,
                 these two approaches cannot be integrated easily though
                 they share many common goals and functionalities. A
                 unification will greatly enhance their problem solving
                 power. In this paper, a framework to combine GP and ILP
                 is presented. The framework is based on a formalism of
                 logic grammars and a system called LOGENPRO (the LOgic
                 grammar based GENetic PROgramming system) is developed.
                 It is so flexible that programs in different
                 programming languages such as LISP, Prolog, and C can
                 be induced. The performance of LOGENPRO in inducing
                 logic programs from noisy examples is also evaluated. A
                 detailed comparison to FOIL and mFOIL has been
                 conducted. The experiment demonstrates that LOGENPRO is
                 a promising alternative to other inductive logic
                 programming systems and sometimes is superior for
                 handling noisy data.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html",
}

@InProceedings{wong:1995:algsf,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Applying Logic Grammars to Induce Sub-Functions in
                 Genetic Programming",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "737--740",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, LOGENPRO",
  abstract =     "Genetic Programming (GP) is a method of automatically
                 inducing S-expression in LISP to perform specified
                 tasks. The problem of inducing programs can be
                 reformulated as a search for a highly fit program in
                 the space of all possible programs. This paper presents
                 a framework in which the search space can be specified
                 declaratively by a user. Its application in inducing
                 sub-functions is detailed. The framework is based on a
                 formalism of logic grammars and it is implemented as a
                 system called LOGENPRO (the LOgic grammar based GENetic
                 PROgramming system). The formalism is powerful enough
                 to represent context-sensitive information and
                 domain-dependent knowledge. This knowledge can be used
                 to accelerate the learning speed and/or improve the
                 quality of the programs induced. The system is also
                 very flexible and programs in various programming
                 languages can be acquired. Automatic discovery of
                 sub-functions is one of the most important research
                 areas in Genetic Programming. An experiment is used to
                 demonstrate that LOGENPRO can emulate Koza's
                 Automatically Defined Functions (ADF). Moreover,
                 LOGENPRO can employ knowledge such as argument types in
                 a unified framework. The experiment shows that LOGENPRO
                 has superior performance to that of Koza's ADF when
                 more domain- dependent knowledge is available.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html",
}

@InCollection{wong:1996:aigp2,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Evolving Recursive Functions for the Even-Parity
                 Problem Using Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "221--240",
  chapter =      "11",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  abstract =     "One of the most important and challenging areas of
                 research in evolutionary algorithms is to investigate
                 ways to successfully apply evolutionary algorithms to
                 larger and more complicated problems. One approach to
                 make a given problem more tractable is to discover
                 problem representations automatically. Koza (1993) uses
                 the even-n-parity problem to demonstrate extensively
                 that his approach of Automatic Function Definition
                 (ADF) can facilitate the solution of the problem.
                 Unfortunately, the solutions found by GP with ADF can
                 only solved the problem for a particular value of n. If
                 a different value of n is used, GP with ADF must be
                 used again to find other programs that can solve the
                 new even-n-parity problem. Clearly, the solution found
                 is not general enough to solve all even-n-parity
                 problem for n greater than or equal to zero. In this
                 paper, we apply GGP (Generic Genetic Programming) to
                 evolve general recursive functions for the
                 even-n-parity problem. GGP is very flexible and
                 programs in various programming languages can be
                 acquired. Moreover, it is powerful enough to represent
                 context-sensitive information and domain-dependent
                 knowledge. This knowledge can be used to accelerate the
                 learning speed and/or improve the quality of the
                 programs induced. A number of experiments have been
                 performed to determine the impact of domain-specific
                 knowledge on the speed of learning.",
}

@InProceedings{wong:1996:lrfneGGP,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Learning Recursive Functions from Noisy Examples using
                 Generic Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "238--246",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  notes =        "GP-96",
}

@InProceedings{wong:1996:l-g-bGPs,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "The Logic-Grammars-Based Genetic Programming System",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and David B. Fogel
                 and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "433",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "1 page",
  notes =        "GP-96",
}

@PhdThesis{ManLeungWong:thesis,
  author =       "Man Leung Wong",
  title =        "Evolutionary Program Induction Directed by Logic
                 Grammars",
  school =       "Department of Computer Science and Engineering. The
                 Chinese University of Hong Kong",
  year =         "1995",
  keywords =     "genetic algorithms, genetic programming",
  size =         "pages",
}

@Article{ManLeungWong:1997:epidlg,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Evolutionary Program Induction Directed by Logic
                 Grammars",
  journal =      "Evolutionary Computation",
  year =         "1997",
  volume =       "5",
  number =       "2",
  pages =        "143--180",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, logic grammars",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=5&issue=2",
  abstract =     "Program induction generates a computer program that
                 can produce the desired behavior for a given set of
                 situations. Two of the approaches in program induction
                 are inductive logic programming (ILP) and genetic
                 programming (GP). Since their formalisms are so
                 different, these two approaches cannot be integrated
                 easily, although they share many common goals and
                 functionalities. A unification will greatly enhance
                 their problem-solving power. Moreover, they are
                 restricted in the computer languages in which programs
                 can be induced. In this paper, we present a flexible
                 system called LOGENPRO (The LOgic grammar-based GENetic
                 PROgramming system) that uses some of the techniques of
                 GP and ILP. It is based on a formalism of logic
                 grammars. The system applies logic grammars to control
                 the evolution of programs in various programming
                 languages and represent context-sensitive information
                 and domain-dependent knowledge. Experiments have been
                 performed to demonstrate that LOGENPRO can emulate GP
                 and GP with automatically defined functions (ADFs).
                 Moreover, LOGENPRO can employ knowledge such as
                 argument types in a unified framework. The experiments
                 show that LOGENPRO has superior performance to that of
                 GP and GP with ADFs when more domain-dependent
                 knowledge is available. We have applied LOGENPRO to
                 evolve general recursive functions for the
                 even-n-parity from noisy training examples. A number of
                 experiments have been performed to determine the impact
                 of domain-specific knowledge and noise in training
                 examples on the speed of learning.",
  notes =        "Special Issue: Trends in Evolutionary Methods for
                 Program Induction",
}

@Article{wong:1998:ESA,
  author =       "Man Leung Wong",
  title =        "An adaptive knowledge-acquisition system using generic
                 genetic programming",
  journal =      "Expert Systems with Applications",
  volume =       "15",
  pages =        "47--58",
  year =         "1998",
  number =       "1",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-3TGSH84-3/1/83b0941e6fae053fea766e293d408cf9",
  size =         "12 pages",
  abstract =     "The knowledge-acquisition bottleneck greatly obstructs
                 the development of knowledge-based systems. One popular
                 approach to knowledge acquisition uses inductive
                 concept learning to derive knowledge from examples
                 stored in databases. However, existing learning systems
                 cannot improve themselves automatically. This paper
                 describes an adaptive knowledge-acquisition system that
                 can learn first-order logical relations and improve
                 itself automatically. The system is composed of an
                 external interface, a biases base, a knowledge base of
                 background knowledge, an example database, an empirical
                 ILP learner, a meta-level learner, and a learning
                 controller. In this system, the empirical ILP learner
                 performs top-down search in the hypothesis space
                 defined by the concept description language, the
                 language bias, and the background knowledge. The search
                 is directed by search biases which can be induced and
                 refined by the meta-level learner based on generic
                 genetic programming.

                 It has been demonstrated that the adaptive
                 knowledge-acquisition system performs better than FOIL
                 on inducing logical relations from perfect or noisy
                 training examples. The result implies that the search
                 bias evolved by evolutionary learning is better than
                 that of FOIL which is designed by a top researcher in
                 the field. Consequently, generic genetic programming is
                 a promising technique for implementing a meta-level
                 learning system. The result is very encouraging as it
                 suggests that the process of natural selection and
                 evolution can successfully evolve a high-performance
                 learning system.",
}

@Book{ManLeungWong:book,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Data Mining Using Grammar Based Genetic Programming
                 and Applications",
  publisher =    "Kluwer Academic Publishers",
  year =         "2000",
  volume =       "3",
  series =       "Genetic Programming",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7923-7746-X",
  URL =          "http://www.wkap.nl/book.htm/0-7923-7746-X",
  notes =        "Data mining involves the non-trivial extraction of
                 implicit, previously unknown, and potentially useful
                 information from databases. Genetic Programming (GP)
                 and Inductive Logic Programming (ILP) are two of the
                 approaches for data mining. This book first sets the
                 necessary backgrounds for the reader, including an
                 overview of data mining, evolutionary algorithms and
                 inductive logic programming. It then describes a
                 framework, called GGP (Generic Genetic Programming),
                 that integrates GP and ILP based on a formalism of
                 logic grammars. The formalism is powerful enough to
                 represent context- sensitive information and
                 domain-dependent knowledge. This knowledge can be used
                 to accelerate the learning speed and/or improve the
                 quality of the knowledge induced. A grammar-based
                 genetic programming system called LOGENPRO (The LOGic
                 grammar based GENetic PROgramming system) is detailed
                 and tested on many problems in data mining. It is found
                 that LOGENPRO outperforms some ILP systems. We have
                 also illustrated how to apply LOGENPRO to emulate
                 Automatically Defined Functions (ADFs) to discover
                 problem representation primitives automatically. By
                 employing various knowledge about the problem being
                 solved, LOGENPRO can find a solution much faster than
                 ADFs and the computation required by LOGENPRO is much
                 smaller than that of ADFs. Moreover, LOGENPRO can
                 emulate the effects of Strongly Type Genetic
                 Programming and ADFs simultaneously and effortlessly.
                 Data Mining Using Grammar Based Genetic Programming and
                 Applications is appropriate for researchers,
                 practitioners and clinicians interested in genetic
                 programming, data mining, and the extraction of data
                 from databases. Contents

                 List of Figures. List of Tables. Preface. 1.
                 Introduction. 2. An Overview of Data Mining. 3. An
                 Overview on Evolutionary Algorithms. 4. Inductive Logic
                 Programming. 5. The Logic Grammars Based Genetic
                 Programming System (LOGENPRO). 6. Data Mining
                 Applications Using LOGENPRO. 7. Applying LOGENPRO for
                 Rule Learning. 8. Medical Data Mining. 9. Conclusion
                 and Future Work. Appendix A: The Rule Sets
                 Discovered.

                 Appendix B: The Grammar Used for the Fracture and
                 Scoliosis Databases. References. Index.",
  size =         "232 pages",
}

@Article{wong:2000:dkm,
  author =       "Man Leung Wong and Wai Lam and Kwong Sak Leung and Po
                 Shun Ngan and Jack C. Y. Cheng",
  title =        "Discovering knowledge from medical databases using
                 evolutionory algorithms",
  journal =      "IEEE Engineering in Medicine and Biology Magazine",
  year =         "2000",
  volume =       "19",
  number =       "4",
  pages =        "45--55",
  month =        jul # "-" # aug,
  keywords =     "genetic algorithms, genetic programming, database
                 management systems, medical databases, knowledge
                 discovery, Bayesian networks, causality relationship
                 models, Bayesian network learning process, continuous
                 variables, advanced evolutionary algorithms,
                 evolutionary programming, learning tasks, fracture
                 database, child fractures, scoliosis database,
                 scoliosis classification, novel clinical knowledge,
                 database errors",
  ISSN =         "0739-5175",
  URL =          "http://ieeexplore.ieee.org/iel5/51/18543/00853481.pdf",
  size =         "11 pages",
  abstract =     "Discusses learning roles and causal structures for
                 capturing patterns and causality relationships. The
                 authors present their approach for knowledge discovery
                 from two specific medical databases. First, rules are
                 learned to represent the interesting patterns of the
                 data. Second, Bayesian networks are induced to act as
                 causality relationship models among the attributes. The
                 Bayesian network learning process is divided into two
                 phases. In the first phase, a discretization policy is
                 learned to discretize the continuous variables, and
                 then Bayesian network structures are induced in the
                 second phase. The authors employ advanced evolutionary
                 algorithms such as generic genetic programming,
                 evolutionary programming, and genetic algorithms to
                 conduct the learning tasks. From the fracture database,
                 they discovered knowledge about the patterns of child
                 fractures. From the scoliosis database, they discovered
                 knowledge about the classification of scoliosis. They
                 also found unexpected rules that led to discovery of
                 errors in the database. These results demonstrate that
                 the knowledge discovery process can find interesting
                 knowledge about the data, which can provide novel
                 clinical knowledge as well as suggest refinements of
                 the existing knowledge.",
}

@InProceedings{wood:1998:DNAcadhp,
  author =       "David Harlan Wood",
  title =        "A {DNA} Computing Algorithm for Directed Hamiltonian
                 Paths",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "731--734",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{wood:1999:ADIMP,
  author =       "David Wood and Junghuei Chen and Eugene Antipov
                 Bertrand Lemieux and Walter Cedeno",
  title =        "A {DNA} Implementation of the Max 1s Problem",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1835--1842",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "dna and molecular computing",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{woodward:1999:GPatnlds,
  author =       "Andrew M. Woodward and Richard J. Gilbert and Douglas
                 B. Kell",
  title =        "Genetic programming as an analytical tool for
                 non-linear dielectric spectroscopy",
  journal =      "Bioelectrochemistry and Bioenergetics",
  year =         "1999",
  volume =       "48",
  number =       "2",
  pages =        "389--396",
  keywords =     "genetic algorithms, genetic programming, Dielectric
                 spectroscopy, Multivariate calibration, Non-linear,
                 Fermentation, Biotechnology",
  abstract =     "By modelling the non-linear effects of membranous
                 enzymes on an applied oscillating electromagnetic field
                 using supervised multivariate analysis methods,
                 Non-Linear Dielectric Spectroscopy (NLDS) has
                 previously been shown to produce quantitative
                 information that is indicative of the metabolic state
                 of various organisms. The use of Genetic Programming
                 (GP) for the multivariate analysis of NLDS data
                 recorded from yeast fermentations is discussed, and GPs
                 are compared with previous results using Partial Least
                 Squares (PLS) and Artificial Neural Nets (NN). GP
                 considerably outperforms these methods, both in terms
                 of the precision of the predictions and their
                 interpretability.",
  notes =        "5 demes (5Inject2Way) pop=5*10000, 200 gens. linear GP
                 (* / + -) and more complex function set. Automatic
                 deconvolution of equation tree evolved by LGP.",
}

@InProceedings{wright:1999:MCMGA,
  author =       "Alden H. Wright and Yong Zhao",
  title =        "Markov Chain Models of Genetic Algorithms",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "734--741",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{wu:1996:sirg,
  author =       "Annie S. Wu and Robert K. Lindsay",
  title =        "A Survey of Intron Research in Genetics",
  editor =       "Hans-Michael Voigt and Werner Ebeling and Ingo
                 Rechenberg and Hans-Paul Schwefel",
  booktitle =    "Parallel Problem Solving From Nature IV. Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  publisher =    "Springer-Verlag",
  volume =       "1141",
  series =       "LNCS",
  pages =        "101--110",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  ISBN =         "3-540-61723-X",
  size =         "10 pages",
  abstract =     "A brief survey of biological research on non-coding
                 DNA ... describes different types of non-coding DNA and
                 then surveys recnt intron research",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
                 wetware",
}

@Article{wu:1998:ECJintro,
  author =       "Annie S. Wu and Wolfgang Banzhaf",
  title =        "Introduction to the Special Issue: Variable-Length
                 Representation and Noncoding Segments for Evolutionary
                 Algorithms",
  journal =      "Evolutionary Computation",
  year =         "1998",
  volume =       "6",
  number =       "4",
  pages =        "iii--vi",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=6&issue=4",
  notes =        "Special Issue: Variable-Length Representation and
                 Noncoding Segments for Evolutionary Algorithms Edited
                 by Annie S. Wu and Wolfgang Banzhaf",
}

@Proceedings{wu:2000:GECCOWKS,
  title =        "Proceedings of the 2000 Genetic and Evolutionary
                 Computation Conference Workshop Program",
  year =         "2000",
  editor =       "Annie S. Wu",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  URL =          "http://www.cs.colostate.edu/~genitor/GECCO-2000/workshops.htm",
  size =         "317",
  notes =        "GECCO-2000WKS",
}

@Article{AnnieSWu:2002:GPEM,
  author =       "Annie S. Wu and Ivan Garibay",
  title =        "The Proportional Genetic Algorithm: Gene Expression in
                 a Genetic Algorithm",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2002",
  volume =       "3",
  number =       "2",
  pages =        "157--192",
  month =        jun,
  keywords =     "genetic algorithms, representation, gene expression,
                 proportional genetic algorithm",
  ISSN =         "1389-2576",
  abstract =     "We introduce a genetic algorithm (GA) with a new
                 representation method which we call the proportional GA
                 (PGA). The PGA is a multi-character GA that relies on
                 the existence or non-existence of genes to determine
                 the information that is expressed. The information
                 represented by a PGA individual depends only on what is
                 present on the individual and not on the order in which
                 it is present. As a result, the order of the encoded
                 information is free to evolve in response factors other
                 than the value of the solution, for example, in
                 response to the identification and formation of
                 building blocks. The PGA is also able to dynamically
                 evolve the resolution of encoded information. In this
                 paper, we describe our motivations for developing this
                 representation and provide a detailed description of a
                 PGA along with discussion of its benefits and
                 drawbacks. We compare the behavior of a PGA with that
                 of a canonical GA (CGA) and discuss conclusions and
                 future work based on these preliminary studies.",
  notes =        "Special issue on Gene Expression Kargupta:2002:GPEM",
}

@InCollection{wu:1999:EIYNAO,
  author =       "Gary I. Wu",
  title =        "Evolution of Infanticide and Youth Nurturing in
                 Artificial Organisms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "245--253",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{yamazaki:1998:HPlcrvgs,
  author =       "Koetsu Yamazaki and Sourav Kundu and Michitomo
                 Hamano",
  title =        "Genetic Programming Based Learning of Control Rules
                 for Variable Geometry Structures",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "412--415",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{yang:1997:fssGA,
  author =       "Jihoon Yang and Vasant Honavar",
  title =        "Feature Subset Selection Using {A} Genetic Algorithm",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Algorithms",
  pages =        "380",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{yang:1999:AEACG,
  author =       "Jinn-Moon Yang and Cheng-Yan Kao",
  title =        "An Evolutionary Algorithm for Continuous Global
                 optimization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "930--938",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolution strategies and evolutionary programming",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{yang:1999:AGETSPP,
  author =       "Congjun Yang and Dipankar Dasgupta and Yuehua Cao",
  title =        "A Group Encoding Technique for Set Partitioning
                 Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "742--749",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{yang:1999:FSSRIUR,
  author =       "Jihoon Yang and Asok Tiyyagura and Fajun Chen and
                 Vasant Honavar",
  title =        "Feature Subset Selection for Rule Induction Using
                 {RIPPER}",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1800",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "real world applications, poster papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InCollection{yang:1999:SCGA,
  author =       "Mei-Wan Yang",
  title =        "Space Configuration using Genetic Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "254--263",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:1999:GAGPs",
}

@InCollection{yang:2000:ADSSADDS,
  author =       "Brian Hang Wai Yang",
  title =        "A Data Switch Scheduling Algorithm Driven by Darwinian
                 Seleciton",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "452--461",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{yangiya:1995:eGPbdd,
  author =       "Masayuki Yangiya",
  title =        "Efficient Genetic Programming Based on Binary Decision
                 Diagrams",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "1",
  pages =        "234--239",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The performance of genetic programming can be
                 dramatically improved by using a data structure coded
                 by binary decision diagrams (BDDs). BDDs are a compact
                 representation of Boolean functions using directed
                 acyclic graphs. Efficient BDD-based crossover,
                 mutation, and evaluation algorithms have been developed
                 that allow all genetic operations to be performed on
                 BDDs throughout the search. BDD-based GP reduces
                 storage requirements by sharing isomorphic sub-graphs
                 among individuals, and saves computational power by
                 using a hash-based cache to make calculation more
                 efficient. The proposed approach is powerful enough to
                 solve the 20-multiplexer problem, which has never been
                 reportedly achieved before.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at
                 http://ciips.ee.uwa.edu.au/~dorota/icnn95.html",
}

@InProceedings{yao:1999:fogp,
  author =       "Xin Yao",
  title =        "Universal Approximation by Genetic Programming",
  booktitle =    "Foundations of Genetic Programming",
  year =         "1999",
  editor =       "Thomas Haynes and William B. Langdon and Una-May
                 O'Reilly and Riccardo Poli and Justinian Rosca",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/yao.ps.gz",
  size =         "2 pages",
  notes =        "GECCO'99 WKSHOP, part of haynes:1999:fogp",
}

@Article{yasunaga:2001:GPEM,
  author =       "Moritoshi Yasunaga and Jung Hwan Kim and Ikuo
                 Yoshihara",
  title =        "Evolvable Reasoning Hardware: Its Prototyping and
                 Performance Evaluation",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "3",
  pages =        "211--230",
  month =        sep,
  keywords =     "genetic algorithms, evolvable hardware, VLSI design
                 methodology, FPGA, reasoning, NETTalk, MBRTalk",
  ISSN =         "1389-2576",
  abstract =     "In this paper, we propose evolvable reasoning hardware
                 and its design methodology. In the proposed design
                 methodology, case databases of each reasoning task are
                 transformed into truth tables, which are evolved to
                 extract rules behind the past cases through a genetic
                 algorithm. Circuits for the evolvable reasoning
                 hardware are synthesized from the evolved truth-tables.
                 Parallelism in each task can be embedded directly in
                 the circuits through the direct hardware implementation
                 of the case databases. We developed the evolvable
                 reasoning hardware prototype using Xilinx Virtex FPGA
                 chips and applied it to the
                 English-pronunciation-reasoning (EPR) task. The
                 evolvable reasoning hardware for the EPR task was
                 implemented with 270K gates, achieving an extremely
                 high reasoning speed of less than 300 ns/phoneme. It
                 also achieved a reasoning accuracy of 82.1% which is
                 almost the same accuracy as NETTalk in neural networks
                 and MBRTalk in parallel AI.",
}

@InProceedings{Yeh:1997:masm,
  author =       "Chia-Hsuan Yeh",
  title =        "From Multi-Agent System to Macroeconomics:
                 Applications of Genetic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "303",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670

                 cobweb model",
}

@InProceedings{ChiaHsuanYeh:1998:GPlogm,
  author =       "Chia Hsuan Yeh",
  title =        "Genetic Programming Learning and the Overlapping
                 Generations Models",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ai.mit.edu/people/unamay/phd-ws-abstracts/yeh.ps",
  notes =        "GP-98LB, GP-98PhD Student Workshop",
}

@InProceedings{ChiaHsuanYeh:2001:SCE,
  author =       "Chia-Hsuan Yeh",
  title =        "The Influence of Market Size in an Artificial Stock
                 Market: The Approach Based on Genetic Programming",
  booktitle =    "7th International Conference of Society of
                 Computational Economics",
  year =         "2001",
  address =      "Yale",
  month =        "28-29 " # jun,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "http://www.econ.yale.edu/sce01/confpage.html",
}

@InCollection{yeh:1999:DBCNCPGP,
  author =       "Iwei Yeh",
  title =        "Diagnosis of Breast Cancer based on Nine Cytological
                 Parameters using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1999",
  year =         "1999",
  editor =       "John R. Koza",
  pages =        "264--271",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "15 " # mar,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:1999:GAGPs",
}

@InProceedings{yen:1999:ASSAMMO,
  author =       "John Yen and Linyu Yang and Bogju Lee and James C.
                 Liao",
  title =        "A Supervisory Simplex-{GA} Approach for Metabolic
                 Model Optimization",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "750--757",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@Article{YunSeogYeun:1999:AIE,
  author =       "Yun Seog Yeun and K. H. Lee and Y. S. Yang",
  title =        "Function approximations by coupling neural networks
                 and genetic programming trees with oblique decision
                 trees",
  journal =      "Artificial Intelligence in Engineering",
  year =         "1999",
  volume =       "13",
  number =       "3",
  pages =        "223--239",
  email =        "yeonyun@road.daejin.ac.kr",
  keywords =     "genetic algorithms, genetic programming, Federated
                 agents, Oblique decision tree, OC1",
  URL =          "http://members.kr.inter.net/yyshuj/paper/aie.zip",
  URL =          "http://www.sciencedirect.com/science/article/B6V1X-3WWT8F6-3/1/2e564ef70743de81b8e3369fb01b406e",
  abstract =     "This paper is concerning the development of multiple
                 neural networks system combined with genetic
                 programming (GP) trees for problem domains where the
                 complete input space can be decomposed into several
                 different regions, and these are well represented in
                 form of oblique decision tree. The overall architecture
                 of hybrid system, called the federated agents, consists
                 of a facilitator, local agents, and boundary agents.
                 Neural networks used as local agents, each of which is
                 expert at different subregions, and GP trees serve as
                 boundary agents. A boundary agent refer to the one that
                 is specialized at only the borders of subregions where
                 discontinuities or different patterns may exist. The
                 facilitator is responsible for choosing the local agent
                 that is suitable for the given input data using
                 information obtained from oblique decision tree
                 representing a divided input space. However, there are
                 large possibility of selecting the invalid local agent
                 due to the incorrect prediction of decision tree,
                 provided that input data is close enough to the
                 boundaries of regions. Such a situation can lead
                 federated agents to produce a much higher prediction
                 error than that of a single neural network trained over
                 all input space. To deal with this, the approach taken
                 in this paper is to make the facilitator select the
                 boundary agent instead of the local agent when input
                 data is closely located to the certain border of
                 regions. In this way, even if the result of decision
                 tree may be incorrect, the results of system are less
                 affected by it. The validity of our approach is
                 examined and verified by applying the federated agents
                 to the configuration design of a midship section of
                 bulk cargo ships.",
  size =         "17 pages",
  notes =        "Linear associative memories [Kohonen,1988] set
                 numerical parameters in GP trees with overfitting
                 avoidance.

                 Training set partitioned using {"}domain knowledge or
                 clustering methods{"} p255. Separate ANN trained on
                 each subset.

                 ",
}

@Article{YunSeogYeun:2001:IS,
  author =       "Yun Seog Yeun and Jun Chen Suh and Young-Soon Yang",
  title =        "Function approximations by superimposing genetic
                 programming trees:with applications to engineering
                 problems",
  journal =      "Informaion Sciences",
  year =         "2000",
  volume =       "122",
  number =       "2-4",
  pages =        "259--280",
  email =        "yeonyun@road.daejin.ac.kr",
  keywords =     "genetic algorithms, genetic programming, Function
                 approximation, Linear associative memory, Group of
                 additive genetic programming tree",
  URL =          "http://members.kr.inter.net/yyshuj/paper/gagpt.zip",
  URL =          "http://www.elsevier.com/gej-ng/10/23/143/56/27/34/article.pdf",
  abstract =     "This paper concerns fundamental issues regarding
                 genetic programming(GP) as a tool for real-valued
                 function approximations. Standard GP suffers from the
                 lack of estimation techniques for numerical parameters
                 of a functional tree. Unlike other research activities,
                 where non-linear optimization techniques are employed,
                 we adopt the use of a linear associative memory for the
                 estimation of these parameters under the GP algorithm.
                 Instead of dealing with a large associative matrix, we
                 present the method of building several associative
                 matrixes in small size, each of which is responsible
                 for determining the value for different small portions
                 of the whole parameter. This approach can significantly
                 reduce computational cost, and a reasonably accurate
                 value for parameters can be obtained. Due to the fact
                 that the GP algorithm is likely to fall into a local
                 minimum, the GP algorithm often fails to generate the
                 functional tree with the desired accuracy. This
                 motivates us to devise a group of additive genetic
                 programming trees(GAGPT) which consists of a primary
                 tree and a set of auxiliary trees. The output of the
                 GAGPT is the summation of outputs of the primary tree
                 and all auxiliary trees. The addition of auxiliary
                 trees makes it possible to improve both the learning
                 and generalization capability of the GAGPT, since the
                 auxiliary tree evolves toward refining the quality of
                 the GAGPT by optimizing its fitness function. The
                 effectiveness of our approach is verified by applying
                 the GAGPT to the estimation of the principal dimensions
                 of a bulk cargo ship and engine torque of a passenger
                 car.",
  notes =        "Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}

@Article{Yun01,
  author =       "Yun Seog Yeun and Kyung Ho Lee and Sang Min Han and
                 Young Soon Yang",
  title =        "Smooth Fitting with a Method for Determining the
                 Regularization Parameter under the Genetic Programming
                 Algorithm",
  journal =      "Information Sciences",
  year =         "2001",
  volume =       "133",
  number =       "3-4",
  pages =        "175--194",
  month =        apr,
  email =        "yeonyun@road.daejin.ac.kr",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.elsevier.nl/gej%2Dng/10/23/143/84/27/30/article.pdf",
  abstract =     "This paper deals with the smooth fitting problem under
                 the genetic programming(GP) algorithm. To reduce the
                 computational cost required for evaluating the fitness
                 value of GP trees, numerical weights of GP trees are
                 estimated by adopting both linear associative memories
                 and the Hook & Jeeves method. The quality of smooth
                 fitting is critically dependent on the choice of the
                 regularization parameter. So, we present a novel method
                 for choosing the regularization parameter. Two
                 numerical examples are given with the comparison of
                 generalized cross-validation B-splines",
  notes =        "Euclidean norm = zero-order Tikhonov regularisation,
                 is not sufficient p178 uses(?) weighting based on first
                 derivative of evolved function but too CPU
                 expensive(?). LAM HJ discrepancy principle DP
                 cross-validation CV composite residual and smoothing
                 operator CRESO L-curve zero crossing ZC considered in
                 text but use heuristic

                 two test functions 2*(sin(t))**4
                 1.5*(exp(-30*(t-0.25)**2)+sin(pi(t-0.2))**2)

                 {"}..sometimes the GP tree that is discarded by the
                 criterion proposed in this paper is far better than the
                 tree selected as the best one.{"} p192

                 Information Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}

@InCollection{ying:2002:DOSTDPPGA,
  author =       "Donald Ying",
  title =        "Determining an Optimal Solution to a Three Dimensional
                 Packing Problem using Genetic Algorithms",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2002",
  year =         "2002",
  editor =       "John R. Koza",
  pages =        "263--272",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  notes =        "part of koza:2002:gagp",
}

@InProceedings{Yoshihara:2000:GECCO,
  author =       "I. Yoshihara and T. Aoyama and M. Yasunaga",
  title =        "A Fast Model-Building Method for Time Series Using
                 Genetic Programming",
  pages =        "537",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and Erick Cantu-Paz
                 and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 whitley:2000:GECCO",
}

@InProceedings{yoshihara:2000:gmmtsppona,
  author =       "I. Yoshihara and T. Aoyama and M. Yasunaga",
  title =        "{GP}-Based Modeling Method for Time Series Prediction
                 with Parameter Optimization and Node Alternation",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1475--1481",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, time series",
  ISBN =         "0-7803-6375-2",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@Proceedings{cec:2000,
  title =        "Proceedings of the 2000 Congress on Evolutionary
                 Computation {CEC00}",
  year =         "2000",
  key =          "yoshihara",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "biological modeling/ breast cancer, biological
                 modelling, classifiers, coevolution, constraint
                 handling, control system design, controlling search,
                 design applications, devices developement and
                 applications, dynamic and parallel ec, ec techniques,
                 ecological modelling and information ecosystems,
                 engineering applications, evolutionary markets,
                 evolutionary scheduling, evolvable hardware, evolving
                 neural networks, fitness, games and game like tasks,
                 genetic algorithms, genetic programming, hybrid
                 systems, image processing applications, image/ signal
                 processing, intelligent agents, learning and search
                 spaces, local search optimization, medical
                 applications, multi-agent systems and cultural
                 algorithms, multi-objective optimization, network
                 applications, new paradigms, novel applications, novel
                 themes, operations research applications,
                 representations, revisiting the fossil record, robotic
                 applications, stroganoff, system modeling and control,
                 theory and foundations, time series",
  ISBN =         "0-7803-6375-2",
  size =         "1584 pages",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

@InProceedings{yoshimi:1998:hcscv,
  author =       "Takahiro Yoshimi and Toshiharu Taura",
  title =        "Hierarchical Classifier System Based on the Concept of
                 Viewpoint",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "675--678",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, classifiers",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{yoshimi:1999:ACMVPHCS,
  author =       "Takahiro Yoshimi and Toshiharu Taura",
  title =        "A Computational Model of a Viewpoint-Forming Process
                 in a Hierarchical Classifier System",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "758--766",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{yu:1997:polyGP,
  author =       "T. Yu and C. Clack",
  title =        "Poly{GP}: {A} Polymorphic Genetic Programming System
                 in Haskell",
  booktitle =    "Late Breaking Papers at the GP-97 Conference",
  year =         "1997",
  editor =       "John Koza",
  pages =        "264--273",
  address =      "Stanford, CA, USA",
  publisher_address = "Stanford, California, 94305-3079 USA",
  month =        "13-16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/t.yu/pgp.ps",
  abstract =     "In general, the machine learning process can be
                 accelerated through the use of heuristic knowledge
                 about the problem solution. For example, monomorphic
                 typed Genetic Programming (GP) uses type information to
                 reduce the search space and improve performance.
                 Unfortunately, monomorphic typed GP also loses the
                 generality of untyped GP: the generated programs are
                 only suitable for inputs with the specified type.
                 Polymorphic typed GP improves over monomorphic and
                 untyped GP by allowing the type information to be
                 expressed in a more generic manner, and yet still
                 imposes constraints on the search space. This paper
                 describes a polymorphic GP system which can generate
                 polymorphic programs: programs which take inputs of
                 more than one type and produces outputs of more than
                 one type. We also demonstrate its operation through the
                 generation of the {"}map{"} polymorphic program.",
  notes =        "GP-97LB",
}

@InProceedings{yu:1997:pegp,
  author =       "Chris Clack and Tina Yu",
  title =        "Performance Enhanced Genetic Programming.",
  booktitle =    "Proceedings of the Sixth Conference on Evolutionary
                 Programming",
  year =         "1997",
  editor =       "Peter J. Angeline and Robert G. Reynolds and John R.
                 McDonnell and Russ Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  address =      "Indianapolis, Indiana, USA",
  publisher_address = "Berlin",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www9.addr.com/~tinayu/ep97.pdf",
  abstract =     "Genetic Programming is increasing in popularity as the
                 basis for a wide range of learning algorithms. However,
                 the technique has to date only been successfully
                 applied to modest tasks because of the performance
                 overheads of evolving a large number of data
                 structures, many of which do not correspond to a valid
                 program. We address this problem directly and
                 demonstrate how the evolutionary process can be
                 achieved with much greater efficiency through the use
                 of a formally-based representation and strong typing.
                 We report initial experimental results which
                 demonstrate that our technique exhibits significantly
                 better performance than previous work.",
  notes =        "EP-97",
}

@InProceedings{yu:1997:FGP,
  author =       "Tina Yu",
  title =        "Functional Genetic Programming",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "304",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB PHD Students' workshop The email address for
                 the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}

@TechReport{yu:1997:ekr,
  author =       "Chris Clack and Tina Yu",
  title =        "Software -- The Next Generation: Evolving Knowledge
                 Reuse",
  institution =  "UCL, Andersen Consulting",
  year =         "1997",
  type =         "white paper",
  address =      "University College London, Gower Street, London",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "70--85",
  abstract =     "...This paper is an abridged version of yu:1997:pegp",
  notes =        "Part of {"}Emerging Technologies White Papers:
                 Software -- The Next Generation{"} which reports the
                 1996 workshop on Emerging technologies held in UCL
                 Computer Science dept. for Andersen Consulting's
                 Emerging Technologies Group and others.",
  size =         "16 pages",
}

@InProceedings{yu:1998:rlaGP,
  author =       "Tina Yu and Chris Clack",
  title =        "Recursion, Lambda-Abstractions and Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at EuroGP'98: the First European
                 Workshop on Genetic Programming",
  year =         "1998",
  editor =       "Riccardo Poli and W. B. Langdon and Marc Schoenauer
                 and Terry Fogarty and Wolfgang Banzhaf",
  pages =        "26--30",
  address =      "Paris, France",
  publisher_address = "School of Computer Science",
  month =        "14-15 " # apr,
  publisher =    "CSRP-98-10, The University of Birmingham, UK",
  keywords =     "genetic algorithms, genetic programming",
  size =         "5 pages",
  notes =        "EuroGP'98LB part of Poli:1998:egplb",
}

@InProceedings{yu:1998:PolyGP,
  author =       "Tina Yu and Chris Clack",
  title =        "Poly{GP}: {A} Polymorphic Genetic Programming System
                 in Haskell",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "416--421",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www9.addr.com/~tinayu/pgp.new.pdf",
  notes =        "GP-98 slides
                 http://www9.addr.com/~tinayu/poly.slide.pdf",
}

@InProceedings{yu:1998:rlaGP98,
  author =       "Tina Yu and Chris Clack",
  title =        "Recursion, Lambda Abstractions and Genetic
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "422--431",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www9.addr.com/~tinayu/Recursion.pdf",
  notes =        "GP-98 slides
                 http://www9.addr.com/~tinayu/Recursion.slide.pdf",
}

@InProceedings{TinaYu:1998:melp,
  author =       "Tina Yu and Peter Bentley",
  title =        "Methods to Evolve Legal Phenotypes",
  booktitle =    "Fifth International Conference on Parallel Problem
                 Solving from Nature",
  year =         "1998",
  editor =       "Agoston E. Eiben and Thomas Back and Marc Schoenauer
                 and Hans-Paul Schwefel",
  volume =       "1498",
  series =       "LNCS",
  pages =        "280--291",
  address =      "Amsterdam",
  publisher_address = "Berlin",
  month =        "27-30 " # sep,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-65078-4",
  URL =          "http://www9.addr.com/~tinayu/ppsn.pdf",
  abstract =     "Many optimization problems require the satisfaction of
                 constraints in addition to their objectives. When using
                 an evolutionary algorithm to solve such problems, these
                 constraints can be enforced in many different ways to
                 ensure that legal solutions (phenotypes) are evolved.
                 We have identified eleven ways to handle constraints
                 within various stages of an evolutionary algorithm.
                 Five of these methods are experimented on a run-time
                 error constraint in a Genetic Programming system. The
                 results are compared and analyzed.",
  notes =        "PPSN-V see also TinaYu:1998:melpLB",
}

@InProceedings{TinaYu:1998:melpLB,
  author =       "Tina Yu and Peter Bentley",
  title =        "Methods to Evolve Legal Phenotypes",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
                 Conference",
  year =         "1998",
  editor =       "John R. Koza",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-98LB see also TinaYu:1998:melp",
}

@InProceedings{yu:1999:SAGP,
  author =       "Tina Yu",
  title =        "Structure Abstraction and Genetic Programming",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "1",
  pages =        "652--659",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, theory",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://www9.addr.com/~tinayu/cec99.pdf",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143

                 ",
}

@PhdThesis{TinaYu:thesis,
  author =       "Gwoing Tina Yu",
  title =        "An Analysis of the Impact of Functional Programming
                 Techniques on Genetic Programming",
  school =       "University College, London",
  year =         "1999",
  address =      "Gower Street, London, WC1E 6BT",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.addr.com/~tinayu/Thesis.pdf",
  URL =          "http://www.addr.com/~tinayu/Thesis.ps",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/tinayu/TinaYuThesis.ps.gz",
  size =         "185 pages",
  abstract =     "Genetic Programming (GP) automatically generates
                 computer programs to solve specified problems. It
                 develops programs through the process of a
                 {"}create-test-modify{"} cycle which is similar to the
                 way a human writes programs. There are various
                 functional programming techniques that human
                 programmers can use to accelerate the program
                 development process. This research investigated the
                 applicability of some of the functional techniques to
                 GP and analyzed their impact on GP performance.

                 Among many important functional techniques, three were
                 chosen to be included in this research, due to their
                 relevance to GP. They are polymorphism, implicit
                 recursion and higher-order functions. To demonstrate
                 their applicability, a GP system was developed with
                 those techniques incorporated. Furthermore, a number of
                 experiments were conducted using the system. The
                 results were then compared to those generated by other
                 GP systems which do not support these functional
                 features. Finally, the program search space of the
                 general even-parity problem was analyzed to explain how
                 these techniques impact GP performance.

                 The experimental results showed that the investigated
                 functional techniques have made GP more powerful in the
                 following ways: 1) polymorphism has enabled GP to solve
                 problems that are very difficult for standard GP to
                 solve, i.e. nth and map programs; 2) higher-order
                 functions and implicit recursion have enhanced GP's
                 ability in solving the general even-parity problem to a
                 greater degree than with any other known methods.
                 Moreover, the analysis showed that these techniques
                 directed GP to generate program solutions in a way that
                 has never been previously reported. Finally, we provide
                 the guidelines for the application of these techniques
                 to other problems.",
  notes =        "My version of ghostview barfs 8 March 2000 but
                 Thesis.ps prints ok",
}

@InProceedings{TinaYu:2001:ACMKDD,
  author =       "Tina Yu and Jim Rutherford",
  title =        "Modeling Sparse Engine Test Data Using Genetic
                 programming",
  booktitle =    "The Seventh ACM SIGKDD International Conference on
                 Knowledge Discovery and Data Mining",
  year =         "2001",
  address =      "San Francisco, California, USA",
  month =        "26-29 " # aug,
  keywords =     "genetic algorithms, genetic programming, Data
                 Modeling, Sparse Data, High Dimensionality, Virtual
                 Testing",
  URL =          "http://www.acm.org/sigs/sigkdd/kdd2001/",
  URL =          "http://www.improvise.ws/KDDFinal.pdf",
  abstract =     "We demonstrate the generation of an engine test model
                 using Genetic Programming. In particular, a two-phase
                 modeling process is proposed to handle the
                 high-dimensionality and sparseness natures of the
                 engine test data. The resulting model gives high
                 accuracy prediction on training data. It is also very
                 good in predicting low range data values. However, at
                 least partly due to limitations of the data set, its
                 accuracy on validation data and high range data values
                 is not satisfactory. Moreover, the subject experts
                 could not interpret its real-world meaning. We hope the
                 results of this study can benefit other engine oil
                 modeling applications.",
}

@Article{TinaYu:2001:GPEM,
  author =       "Tina Yu",
  title =        "Hierachical Processing for Evolving Recursive and
                 Modular Programs Using Higher Order Functions and
                 Lambda Abstractions",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2001",
  volume =       "2",
  number =       "4",
  pages =        "345--380",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, hierarchical
                 processing, recursion, structure abstraction,
                 higher-order functions, lambda abstraction,
                 polymorphism, type systems",
  ISSN =         "1389-2576",
  abstract =     "We present a novel approach using higher-order
                 functions and l abstraction to evolve recursive and
                 modular programs. Moreover, a new term 'structure
                 abstraction' is introduced to describe the property
                 emerged from the higher-order function program
                 structure. We test this technique on the general
                 even-parity problem. The results indicate that this
                 approach is very effective with the general even-parity
                 problem due to the appropriate selection of the foldr
                 higher-order function. Initially, foldr structure
                 abstraction identify the promising area of the search
                 space at generation zero. Once the population is within
                 the promising area, foldr structure abstraction
                 provides hierarchical processing for search.
                 Consequently, solutions to the general even-parity
                 problem are found very efficiently. We identify the
                 limitations of this new approach and conclude that only
                 when the appropriate higher-order function is selected
                 that the benefits of structure abstraction show.",
  notes =        "STGP polymorphic general solution to even-n-parity
                 from 12 test cases",
}

@InCollection{yu:2000:OBBGP,
  author =       "Chia-Hao (Jack) Yu",
  title =        "Original Broom Balancer with Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 2000",
  year =         "2000",
  editor =       "John R. Koza",
  pages =        "462--471",
  address =      "Stanford, California, 94305-3079 USA",
  month =        jun,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "part of koza:2000:gagp",
}

@InProceedings{JessenYu:2000:CSCSD,
  author =       "Jessen Yu and Martin A. Keane and John R. Koza",
  title =        "Automatic design of both topology andtuning of a
                 common parameterized controller for two families of
                 plants using genetic programming",
  booktitle =    "Proceedings of Eleventh IEEE International Symposium
                 on Computer-Aided Control SystemDesign (CACSD)
                 Conference and Ninth IEEE International Conference on
                 Control Applications(CCA) Conference",
  year =         "2000",
  pages =        "CACSD-234--242",
  address =      "Anchorage, Alaska",
  month =        sep # " 25-27",
  organization = "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/cacsd2000.ps",
  abstract =     "This paper demonstrates that a technique of
                 evolutionary computation can be used to automatically
                 create the design for both the topology and parameter
                 values (tuning) for a common controller (containing
                 various parameters representing the overall
                 characteristics of the plant) for two families of
                 plants. The automatically designed controller is
                 created by means of genetic programming using a fitness
                 measure that attempts to optimize step response and
                 disturbance rejection while simultaneously imposing
                 constraints on maximum sensitivity and sensor noise
                 attenuation. The automatically designed controller
                 outperforms the controller designed with conventional
                 techniques. In particular, the automatically designed
                 controller is superior to the Astrom and Hagglund
                 controller for all plants of both families for the
                 integral of the time-weighted absolute error (ITAE) for
                 a step input, the ITAE for disturbance rejection, and
                 maximum sensitivity. Averaged over all plants of both
                 families, the ITAE for the step input for the
                 automatically designed controller is only 58% of the
                 value for the conventional controller; the ITAE for
                 disturbance rejection is 91% of the value for the
                 conventional controller; and the maximum sensitivity,
                 Ms. for the automatically designed controller is only
                 85% of the value for the conventional controller. The
                 automatically designed controller is {"}general{"} in
                 the sense that it contains free variables and therefore
                 provides a solution to an entire category of problems
                 (i.e., all the plants in the two families) ¾ not merely
                 a single instance of the problem (i.e., a particular
                 single plant).",
}

@InProceedings{Yu:2000:GECCOlb,
  author =       "Tina Yu",
  title =        "Polymorphism and Genetic Programming",
  pages =        "437--444",
  booktitle =    "Late Breaking Papers at the 2000 Genetic and
                 Evolutionary Computation Conference",
  year =         "2000",
  editor =       "Darrell Whitley",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www9.addr.com/~tinayu/gecco.pdf",
  abstract =     "Types have been introduced to Genetic Program-ming
                 (GP) by researchers with different motivation. We
                 present the concept of types in GP and introduce a
                 particular implementation of typed GP, polymorphism,
                 that can enhance GP applicability to problems that are
                 very difficult for standard GP to solve. Through the
                 analysis of a series of experimental results, we
                 demonstrate that the combination of polymorphism and GP
                 evolutionary search has enabled two polymorphic
                 programs to be generated.",
  notes =        "Part of whitley:2000:GECCOlb Slides
                 http://www9.addr.com/~tinayu/gecco.slide.pdf",
}

@InProceedings{yu:2001:EuroGP_neutrality,
  author =       "Tina Yu and Julian Miller",
  title =        "Neutrality and the Evolvability of Boolean Function
                 Landscape",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "204--217",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Neutrality,
                 Evolvability, Boolean function landscape, Neutral
                 mutation, Exploration vs. Exploitation, Graph-based
                 Genetic Programming",
  ISBN =         "3-540-41899-7",
  size =         "14 pages",
  abstract =     "This work is a study of neutrality in the context of
                 Evolutionary Computation systems. In particular, we
                 introduce the use of explicit neutrality with an
                 integer string coding scheme to allow neutrality to be
                 measured during evolution. We tested this method on a
                 Boolean benchmark problem. The experimental results
                 indicate that there is a positive relationship between
                 neutrality and evolvability: neutrality improves
                 evolvability. We also identify four characteristics of
                 adaptive/neutral mutations that are associated with
                 high evolvability. They may be the ingredients in
                 designing effective Evolutionary Computation systems
                 for the Boolean class problem.",
  notes =        "EuroGP'2001, part of miller:2001:gp",
}

@InProceedings{yu:2001:EuroGP_poly,
  author =       "Tina Yu",
  title =        "Polymorphism and Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and Pier Luca
                 Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "218--233",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Polymorphism,
                 Strongly Typed GP, STGP, Multi-objective optimisation,
                 Typed GP, Constraint handling, PolyGP",
  ISBN =         "3-540-41899-7",
  size =         "16 pages",
  abstract =     "Types have been introduced to Genetic Programming (GP)
                 by researchers with different motivation. We present
                 the concept of types in GP and introduce a typed GP
                 system, PolyGP, that supports polymorphism through the
                 use of three different kinds of type variable. We
                 demonstrate the usefulness of this kind of polymorphism
                 in GP by evolving two polymorphic programs (nth and
                 map) using the system. Based on the analysis of a
                 series of experimental results, we conclude that this
                 implementation of polymorphism is effective in
                 assisting GP evolutionary search to generate these two
                 programs. PolyGP may enhance the applicability of GP to
                 a new class of problems that are difficult for other
                 polymorphic GP systems to solve.",
  notes =        "EuroGP'2001, part of miller:2001:gp. Best
                 presentation",
}

@InProceedings{yu:2001:msetdugp,
  author =       "Tina Yu and Jim Rutherford",
  title =        "Modeling Sparse Engine Test Data Using Genetic
                 Programming",
  booktitle =    "2001 Genetic and Evolutionary Computation Conference
                 Late Breaking Papers",
  year =         "2001",
  editor =       "Erik D. Goodman",
  pages =        "499",
  address =      "San Francisco, California, USA",
  month =        "9-11 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www9.addr.com/~tinayu/GECCO2001.pdf",
  notes =        "GECCO-2001LB",
}

@InProceedings{yu:2002:EuroGP,
  title =        "Needles in Haystacks Are Not Hard to Find with
                 Neutrality",
  author =       "Tina Yu and Julian Miller",
  editor =       "James A. Foster and Evelyne Lutton and Julian Miller
                 and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "13--25",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  abstract =     "We propose building neutral networks in
                 needle-in-haystack fitness landscapes to assist an
                 evolutionary algorithm to perform search. The
                 experimental results on four different problems show
                 that this approach improves the search success rates in
                 most cases. In situations where neutral networks do not
                 give performance improvement, no impairment occurs
                 either. We also tested a hypothesis proposed in our
                 previous work. The results support the hypothesis: when
                 the ratio of adaptive/neutral mutations during neutral
                 walk is close to that of fitness improvement step, the
                 evolutionary search has a high success rate. Moreover,
                 the ratio magnitudes indicate that more neutral
                 mutations (than adaptive mutations) are required for
                 the algorithms to find a solution in this type of
                 search space.",
  notes =        "EuroGP'2002, part of lutton:2002:GP",
}

@InProceedings{TinaYu:2002:eh,
  author =       "Tina Yu and Seong Lee",
  title =        "Evolving Cellular Automata to Model Fluid Flow in
                 Porous Media",
  booktitle =    "The Fourth NASA/DoD workshop on Evolvable Hardware",
  year =         "2002",
  keywords =     "genetic algorithms",
  URL =          "http://www.improvise.ws/yu-fluid.pdf",
  size =         "8 pages",
  abstract =     "Fluid flow in porous media is a dynamic process that
                 is traditionally modeled using PDE (Partial
                 Differential Equations). In this approach, physical
                 properties related to fluid flow are inferred from rock
                 sample data. However, due to the limitations posed in
                 the sample data (sparseness and noise), this method
                 often yields inaccurate results. Consequently,
                 production information is normally used to improve the
                 accuracy of property estimation. This style of modeling
                 is equivalent to solving inverse problems. We propose
                 using a Genetic Algorithm (GA) as an inverse method to
                 model fluid flow in a pore network Cellular Automaton
                 (CA). This GA evolves the CA to produce specified flow
                 dynamic responses. We apply this method to a rock
                 sample data set. The results are presented and
                 discussed. Additionally, the prospect of building the
                 pore network CA machine is discussed.",
  notes =        "EH2002 On my printer, some parts of figures in
                 yu-fluid.pdf came out funny 9.may.2002

                 ChevronTexaco Information Technology Company
                 ChevronTexaco Exploration & Production Technology
                 Company 6001 Bollinger Canyon Road 6001 Bollinger
                 Canyon Road San Ramon, CA 94583 San Ramon, CA 94583",
}

@InProceedings{yu2:2002:gecco:lbp,
  title =        "The Role of Neutral and Adaptive Mutation in an
                 Evolutionary Search on the OneMax Problem",
  author =       "Tina Yu and Julian F. Miller",
  booktitle =    "Late Breaking Papers at the Genetic and Evolutionary
                 Computation Conference ({GECCO-2002})",
  editor =       "Erick Cant{\'u}-Paz",
  year =         "2002",
  month =        jul,
  pages =        "512--519",
  address =      "New York, NY",
  publisher =    "AAAI",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  keywords =     "genetic algorithm, genetic programming, Cartesian
                 genetic programming, neutrality",
  notes =        "Late Breaking Papers, {GECCO-2002}. A joint meeting of
                 the eleventh International Conference on Genetic
                 Algorithms ({ICGA-2002}) and the seventh Annual Genetic
                 Programming Conference ({GP-2002}) part of
                 cantu-paz:2002:GECCO:lbp

                 OneMax, explicit versus implicit neutrality, analysis.
                 Variable mutation rate and neutrality. Success rate
                 increases with neutrality (Hamming distance)",
}

@InCollection{Yun:1997:fobsGA,
  author =       "Yeogirl Yun",
  title =        "Finding an Optimal Blackjack Strategy using Genetic
                 Algorithm",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1997",
  publisher =    "Stanford Bookstore",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "226--235",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "17 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-205981-2",
  abstract =     "we find a strategy that performs sumilarly or even
                 better than a book strategy found in the Blackjack
                 literature",
  notes =        "part of koza:1997:GAGPs",
}

@InCollection{yurovitsky:1995:PTUGP,
  author =       "Michael Yurovitsky",
  title =        "Playing Tetris Using Genetic Programming",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "309--319",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{zahran:1999:AGAMS,
  author =       "Mohamed M. Zahran and Ashraf H. Abdel Wahab and Samir
                 I. Shaheen",
  title =        "Adaptive Genetic Algorithm for Multiprocessor
                 Scheduling",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "1",
  pages =        "814",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms and classifier systems, poster
                 papers",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{zalzala:1999:MAMJTGPA,
  author =       "A. M. S. Zalzala and D. Green",
  title =        "{MTGP}: {A} Multithreaded Java Tool for Genetic
                 Programming Applications",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and Marc
                 Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "904--912",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, algorithms",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

@Article{zannoni:1997:lcpepca,
  author =       "Elena Zannoni and Robert G. Reynolds",
  title =        "Learning to Control the Program Evolution Process with
                 Cultural Algorithms",
  journal =      "Evolutionary Computation",
  year =         "1997",
  volume =       "5",
  number =       "2",
  pages =        "181--211",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming, cultural
                 algorithms, software design methodologies, software
                 metrics, machine learning of software design concepts,
                 design concept reuse",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=5&issue=2",
  abstract =     "Traditional software engineering dictates the use of
                 modular and structured programming and top-down
                 stepwise refinement techniques that reduce the amount
                 of variability arising in the development process by
                 establishing standard procedures to be followed while
                 writing software. This focusing leads to reduced
                 variability in the resulting products, due to the use
                 of standardized constructs. Genetic programming (GP)
                 performs heuristic search in the space of programs.
                 Programs produced through the GP paradigm emerge as the
                 result of simulated evolution and are built through a
                 bottom-up process, incrementally augmenting their
                 functionality until a satisfactory level of performance
                 is reached. Can we automatically extract knowledge from
                 the GP programming process that can be useful to focus
                 the search and reduce product variability, thus leading
                 to a more effective use of the available resources? An
                 answer to this question is investigated with the aid of
                 cultural algorithms. A new system has two levels. The
                 first is the pool of genetic programs (population
                 level), and the second is a knowledge repository
                 (belief set) that is built during the GP run and is
                 used to guide the search process. The microevolution
                 within the population brings about potentially
                 meaningful characteristics of the programs for the
                 achievement of the given task, such as properties
                 exhibited by the best performers in the population.
                 CAGP extracts these features and represents them as the
                 set of the current beliefs. Beliefs correspond to
                 constraints that all the genetic operators and programs
                 must follow. Interaction between the two levels occurs
                 in one direction through the extraction process and, in
                 the other, through the modulation of an individual's
                 program parameters according to which, and how many, of
                 the constraints it follows. CAGP is applied to solve an
                 instance of the symbolic regression problem, in which a
                 function of one variable needs to be discovered. The
                 results of the experiments show an overall improvement
                 on the average performance of CAGP over GP alone and a
                 significant reduction of the complexity of the produced
                 solution. Moreover, the execution time required by CAGP
                 is comparable with the time required by GP alone.",
  notes =        "Special Issue: Trends in Evolutionary Methods for
                 Program Induction",
}

@InCollection{zaric:1995:GASALBP,
  author =       "Greg Zaric",
  title =        "Genetic Algorithms in the Solution of Assembly Line
                 Balancing Problems",
  booktitle =    "Genetic Algorithms and Genetic Programming at Stanford
                 1995",
  year =         "1995",
  editor =       "John R. Koza",
  pages =        "320--329",
  address =      "Stanford, California, 94305-3079 USA",
  month =        "11 " # dec,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-195720-5",
  notes =        "part of koza:1995:gagp",
}

@InProceedings{zavanella:1998:dnpnGA,
  author =       "A. Zavanella and A. Giani and F. Baiardi",
  title =        "On Dropping Niches in Parallel Niching Genetic
                 Algorithms",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "618--620",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms",
  URL =          "http://www.di.unipi.it/~zavanell/Poster.ps",
  notes =        "SGA-98",
}

@Unpublished{zebulum:1997:ilgee,
  author =       "Ricardo Salem Zebulum and Marco Aurelio Pacheco and
                 Marley Vellasco",
  title =        "Increasing Length Genotypes in Evolutionary
                 Electronics",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, Evoluationary Hardware, variable
                 size representation",
  URL =          "http://www.cogs.susx.ac.uk/users/ricardoz/wsk.ps",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
  size =         "5/3pages",
}

@InProceedings{zebulum:1998:cdemaefd,
  author =       "Ricardo S. Zebulum and Marco Aurelio Pacheco and
                 Marley Vellasco",
  title =        "Comparison of Different Evolutionary Methodologies
                 Applied to Electronic Filter Design",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "434--439",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, variable length representation",
  file =         "c075.pdf",
  size =         "6 pages",
  abstract =     "We present in this work the application of a set of
                 different evoZutionary methodologies in the problem of
                 electronic filter design. The main objectives are to
                 find out which constraints in the filter topologies, if
                 any, must be observed along the evolutionary process
                 and to study the problem of convergence to parsimonious
                 circuits. The new area of Evolutionary Electronics is
                 introduced, an evolutionary methodology based on
                 variable length representation is presented and the
                 results on the evolution of low-pass and band-pass
                 filters are described.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence. In answer
                 to a question at WCCI-98, this approach was stated to
                 require fewer function evalutations than work by
                 Koza.",
}

@InProceedings{icga93:zhang,
  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "Genetic Programming of Minimal Neural Nets Using
                 {O}ccam's Razor",
  year =         "1993",
  booktitle =    "Proceedings of the 5th International Conference on
                 Genetic Algorithms, ICGA-93",
  publisher =    "Morgan Kaufmann",
  editor =       "Stephanie Forrest",
  pages =        "342--349",
  address =      "University of Illinois at Urbana-Champaign",
  month =        "17-21 " # jul,
  URL =          "ftp://borneo.gmd.de/pub/as/ga/gmd_as_ga-93_04.ps",
  keywords =     "genetic algorithms, genetic programming",
  size =         "8 pages",
  abstract =     "A genetic programming method is investigated for
                 optimizing both the architecture and the connection
                 weights of multilayer feedforward neural networks. The
                 genotype of each network is represented as a tree whose
                 depth and width are dynamically adapted to the
                 particular application by specifically defined genetic
                 operators. The weights are trained by a next-ascent
                 hillclimbing search. A new fitness function is proposed
                 that quantifies the principle of Occam's razor. It
                 makes an optimal trade-off between the error fitting
                 ability and the parsimony of the network. We discuss
                 the results for two problems of differing complexity
                 and study the convergence and scaling properties of the
                 algorithm.",
  notes =        "GP feedforward binary ANN",
}

@InProceedings{zhang:1999:GPIDI,
  author =       "Byoung-Tak Zhang and Je-Gun Joung",
  title =        "Genetic Programming with Incremental Data
                 Inheritance",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1217--1224",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic programming and evolvable hardware",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference (GP-99)",
}

@InProceedings{zhang:1999:C,
  author =       "Zhiming Zhang and T. Warren Liao",
  title =        "Combining case-based reasoning with genetic
                 algorithms",
  booktitle =    "Late Breaking Papers at the 1999 Genetic and
                 Evolutionary Computation Conference",
  year =         "1999",
  editor =       "Scott Brave and Annie S. Wu",
  pages =        "305--310",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  notes =        "GECCO-99LB",
}

@InProceedings{Zhang-Muehlenbein-94-WCCI-EC,
  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "Synthesis of Sigma-Pi Neural Networks by the Breeder
                 Genetic Programming",
  booktitle =    "Proceedings of IEEE International Conference on
                 Evolutionary Computation (ICEC-94), World Congress on
                 Computational Intelligence",
  publisher =    "IEEE Computer Society Press",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher_address = "New York, USA",
  year =         "1994",
  pages =        "318--323",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic programming has been successfully applied to
                 evolve computer programs for solving a variety of
                 interesting problems. In the previous work we
                 introduced the breeder genetic programming (BGP) method
                 that has Occam's razor in its fitness measure to evolve
                 minimal size multilayer perceptrons. In this paper we
                 apply the method to synthesis of sigma-pi neural
                 networks. Unlike perceptron architectures, sigma-pi
                 networks use product units as well as summation units
                 to build higher-order terms. The effectiveness of the
                 method is demonstrated on benchmark problems.
                 Simulation results on noisy data suggest that BGP not
                 only improves the generalization performance, it can
                 also accelerate the convergence speed.",
  notes =        "Tests GP/Sigma-pi/NN on parity problems. On clean data
                 was able to produce S/P Neural Networks with high
                 performance >98% correct. Also ~90% on noisy
                 data.

                 Fitness function sums NN error and NN size/complexity
                 penalty terms. Shows size/complexity penalty beneficial
                 in that better NN are produced and the GP is twice as
                 fast.",
}

@Article{Zhang-Muehlenbein-94-JCS,
  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "Evolving Optimal Neural Networks Using Genetic
                 Algorithms with {O}ccam's Razor",
  journal =      "Complex Systems",
  volume =       "7",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "199--220",
  year =         "1993",
  URL =          "ftp://borneo.gmd.de/pub/as/ga/gmd_as_ga-93_05.ps",
  abstract =     "Genetic algorithms have had two primary applications
                 for neural networks: optimization of network
                 architecture, and training weights of a fixed
                 architecture. While most previous work focuses on one
                 or the other of these options, this paper investigates
                 an alternative evolutionary approach --- breeder
                 genetic programming (BGP) --- in which the architecture
                 and the weights are optimized simultaneously. In this
                 method, the genotype of each network is represented as
                 a tree whose depth and width are dynamically adapted to
                 the particular application by specifically defined
                 genetic operators. The weights are trained by a
                 next-ascent hillclimbing search. A new fitness function
                 is proposed that quantifies the principle of Occam's
                 razor; it makes an optimal trade-off between the error
                 fitting ability and the parsimony of the network.
                 Simulation results on two benchmark problems of
                 differing complexity suggest that the method finds
                 minimal networks on clean data. The experiments on
                 noisy data show that using Occam's razor not only
                 improves the generalization performance, it also
                 accelerates convergence.",
}

@InProceedings{Zhang-94-PPSN,
  author =       "Byoung-Tak Zhang",
  title =        "Effects of {O}ccam's Razor in Evolving Sigma-Pi Neural
                 Networks",
  booktitle =    "Lecture Notes in Computer Science 866: Parallel
                 Problem Solving from Nature III",
  address =      "Jerusalem",
  publisher_address = "Berlin, Germany",
  publisher =    "Springer-Verlag",
  editor =       "Y. Davidor and H.-P. Schwefel and R. M{\"a}nner",
  year =         "1994",
  pages =        "462--471",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://borneo.gmd.de/pub/as/ga/gmd_as_ga-94_07.ps",
  abstract =     "Several evolutionary algorithms make use of
                 hierarchical representations of variable size rather
                 than linear strings of fixed length. Variable
                 complexity of the structures provides an additional
                 representational power which may widen the application
                 domain of evolutionary algorithms. The price for this
                 is, however, that the search space is open-ended and
                 solutions may grow to arbitrarily large size. In this
                 paper we study the effects of structural complexity of
                 the solutions on their generalization performance by
                 analyzing the fitness landscape of sigma-pi neural
                 networks. The analysis suggests that smaller networks
                 achieve, on average, better generalization accuracy
                 than larger ones, thus confirming the usefulness of
                 Occam's razor. A simple method for implementing the
                 Occam's razor principle is described and shown to be
                 effective in improving the generalization accuracy
                 without limiting their learning capacity.",
}

@InProceedings{zhang:1995:wppent,
  author =       "B. T. Zhang and P. Ohm and H. Muehlenbein",
  title =        "Water Pollution Prediction with Evolutionary Neural
                 Trees",
  booktitle =    "Proccedings 1995 IJCAI Workshop on AI and the
                 Environment",
  year =         "1995",
  editor =       "Cindy Mason",
  address =      "Montreal, Canada",
  month =        aug,
  publisher =    "AAAI and MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "IJCAI-95-AI-Environment",
}

@Article{Zhang-Muehlenbein-95-ECJ,
  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "Balancing Accuracy and Parsimony in Genetic
                 Programming",
  journal =      "Evolutionary Computation",
  volume =       "3",
  number =       "1",
  pages =        "17--38",
  year =         "1995",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Tree induction, Minimum description length
                 principle, Bayesian model comparison, Evolving neural
                 networks.",
  URL =          "ftp://borneo.gmd.de/pub/as/ga/gmd_as_ga-94_09.ps",
  abstract =     "Genetic programming is distinguished from other
                 evolutionary algorithms in that it uses tree
                 representations of variable size instead of linear
                 strings of fixed length. The flexible representation
                 scheme is very important because it allows the
                 underlying structure of the data to be discovered
                 automatically. One primary difficulty, however, is that
                 the solutions may grow too big without any improvement
                 of their generalization ability. In this paper we
                 investigate the fundamental relationship between the
                 performance and complexity of the evolved structures.
                 The essence of the parsimony problem is demonstrated
                 empirically by analyzing error landscapes of programs
                 evolved for neural network synthesis. We consider
                 genetic programming as a statistical inference problem
                 and apply the Bayesian model-comparison framework to
                 introduce a class of fitness functions with error and
                 complexity terms. An adaptive learning method is then
                 presented that automatically balances the
                 model-complexity factor to evolve parsimonious programs
                 without losing the diversity of the population needed
                 for achieving the desired training accuracy. The
                 effectiveness of this approach is empirically shown on
                 the induction of sigma-pi neural networks for solving a
                 real-world medical diagnosis problem as well as
                 benchmark tasks.",
}

@InProceedings{zhang:1995:bimdl,
  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "Bayesian Inference, Minimum Description Length
                 Principle, and Learning by Genetic Programming",
  booktitle =    "Proceedings of the Workshop on Genetic Programming:
                 From Theory to Real-World Applications",
  year =         "1995",
  editor =       "Justinian P. Rosca",
  pages =        "1--5",
  address =      "Tahoe City, California, USA",
  month =        "9 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  size =         "5 pages",
  abstract =     "adaptive search technique which dynamically balances
                 the ratio of training accuracy to complexity of
                 programs to achieve parsimonious solutions without
                 loosing population diversity.",
  notes =        "part of rosca:1995:ml",
}

@InProceedings{zhang:1995:MDLbff,
  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "{MDL}-Based Fitness Functions for Learning
                 Parsimonious Programs",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "122--126",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP, {\em Telephone:} 415-328-3123 {\em Fax:}
                 415-321-4457 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@InCollection{zhang:1996:aigp2,
  author =       "Byoung-Tak Zhang and Heinz M{\"u}hlenbein",
  title =        "Adaptive Fitness Functions for Dynamic Growing/Pruning
                 of Program Trees",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "241--256",
  chapter =      "12",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming, Occam's
                 Razor, minimum description length (MDL), neural trees,
                 adaptive fitness functions",
  ISBN =         "0-262-01158-1",
}

@InProceedings{zhang:1996:bsaif,
  author =       "Byoung-Tak Zhang and Ju-Hyun Kwak and Chang-Hoon Lee",
  title =        "Building Software Agents for Information Filtering on
                 the Internet: {A} Genetic Programming Approach",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "196",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@Article{zhang:1996:els-pntacp,
  author =       "B. T. Zhang",
  title =        "Evolutionary Learning of Sigma-Pi Neural Trees and Its
                 Application to Classification and Prediction",
  journal =      "Journal of Fuzzy Logic and Intelligent Systems",
  year =         "1996",
  volume =       "6",
  number =       "2",
  pages =        "13--21",
  keywords =     "genetic algorithms, genetic programming",
}

@InProceedings{Zhang:1997:erGPsl,
  author =       "Byoung-Tak Zhang and Je-Gun Joung",
  title =        "Enhancing Robustness of Genetic Programming at the
                 Species Level",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "336--342",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{zhang:1997:WSC2,
  author =       "Byoung-Tak Zhang and Je-Gun Joung",
  title =        "Evolutionary Design of Neural Trees for Heart Rate
                 Prediction",
  booktitle =    "Soft Computing in Engineering Design and
                 Manufacturing",
  year =         "1997",
  editor =       "P. K. Chawdhry and R. Roy and R. K. Pant",
  pages =        "93--101",
  publisher_address = "Godalming, GU7 3DJ, UK",
  month =        "23-27 " # jun,
  publisher =    "Springer-Verlag London",
  keywords =     "genetic algorithms, genetic programming, ANN",
  ISBN =         "3-540-76214-0",
  URL =          "http://www.bath.ac.uk/Departments/Eng/wsc2/ind_paper/d_zhang.html",
  notes =        "WSC2 Second On-line World Conference on Soft Computing
                 in Engineering Design and Manufacturing. feed forward
                 artificial neural networks comprised of sigma and pi
                 nodes evolved using GP. {"}Between generations the
                 network weights are adapted by a stochastics
                 gill-climbing search.{"} Fitness based on error between
                 prediction and measurement on training examples plus
                 term related to complexity (ie size) of network and the
                 complexity of the best network in the previous
                 generation (ie fitness prefers parsimony). {"}Without
                 (the parsimony term) the network size usually grows
                 without bound{"}.",
  size =         "9 pages",
}

@Unpublished{zhang:1997:tcsgcg,
  author =       "Byoung-Tak Zhang",
  title =        "A Taxonomy of Control Schemes for Genetic Code
                 Growth",
  note =         "Position paper at the Workshop on Evolutionary
                 Computation with Variable Size Representation at
                 ICGA-97",
  month =        "20 " # jul,
  year =         "1997",
  address =      "East Lansing, MI, USA",
  keywords =     "genetic algorithms, genetic programming, bloat,
                 variable size representation",
  notes =        "http://www.ai.mit.edu/people/unamay/icga-ws.html",
}

@Article{zhang:1997:entmpcs,
  author =       "B. T. Zhang and P. Ohm and H. Muehlenbein",
  title =        "Evolutionary Neural Trees for Modeling and Predicting
                 Complex Systems",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "1997",
  volume =       "10",
  number =       "5",
  pages =        "473--483",
  keywords =     "genetic algorithms, genetic programming",
}

@Article{Zhang:1998:eisnt,
  author =       "Byoung-Tak Zhang and Peter Ohm and Heinz
                 M{\"u}hlenbein",
  title =        "Evolutionary Induction of Sparse Neural Trees",
  journal =      "Evolutionary Computation",
  volume =       "5",
  number =       "2",
  pages =        "213--236",
  year =         "1997",
  keywords =     "genetic algorithms, genetic programming, program
                 induction, higher-order neural networks, neural tree
                 representation, Minimum description length principle,
                 time series prediction, breeder genetic algorithm",
  URL =          "http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=10636560&volume=5&issue=2",
  abstract =     "This paper is concerned with the automatic induction
                 of parsimonious neural networks. In contrast to other
                 program induction situations, network induction entails
                 parametric learning as well as structural adaptation.
                 We present a novel representation scheme called neural
                 trees that allows efficient learning of both network
                 architectures and parameters by genetic search. A
                 hybrid evolutionary method is developed for neural tree
                 induction that combines genetic programming and the
                 breeder genetic algorithm under the unified framework
                 of the minimum description length principle. The method
                 is successfully applied to the induction of higher
                 order neural trees while still keeping the resulting
                 structures sparse to ensure good generalization
                 performance. Empirical results are provided on two
                 chaotic time series prediction problems of practical
                 interest.",
  notes =        "Special Issue: Trends in Evolutionary Methods for
                 Program Induction

                 Referenced in zhang:1997:WSC2

                 Demonstrated on Mackey-Glass and chaotic fluctuations
                 in a far-infaread ammonia NH3 laser.

                 Libraries of building blocks (selected by their local
                 fitness), local fitness-based crossover, injection and
                 pruning of submodules (subtree replaced by its
                 descendent subtree, if the descendent is fitter than
                 the subtree itself), scheduling of genetic operators.
                 Parsimony fitness bias. Local search to optimise
                 sigma+pi? neural net weights using exponetial noise.

                 ",
  size =         "31 pages",
}

@InProceedings{zhang:1998:fs:ecgbGP,
  author =       "Byoung-Tak Zhang and Dong-Yeon Cho",
  title =        "Fitness Switching: Evolving Complex Group Behaviors
                 Using Genetic Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "431--439",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{zhang:1998:maerDNAc,
  author =       "Byoung-Tak Zhang and Soo-Yong Shin",
  title =        "Molecular Algorithms for Efficient and Reliable {DNA}
                 Computing",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "735--744",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "DNA Computing",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{zhang:1998:GPads,
  author =       "Byoung-Tak Zhang and Dong-Yeon Cho",
  title =        "Genetic Programming with Active Data Selection",
  booktitle =    "Second Asia-Pacific Conference on Simulated Evolution
                 and Learning",
  year =         "1998",
  editor =       "Charles Newton",
  address =      "Australian Defence Force Academy, Canberra,
                 Australia",
  month =        "24-27 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "SEAL'98 Possible publication in springer-verlag LNAI
                 series SEAL98#058",
}

@InCollection{zhang:1999:aigp3,
  author =       "Byoung-Tak Zhang and Dong-Yeon Cho",
  title =        "Coevolutionary Fitness Switching: Learning Complex
                 Collective Behaviors Using Genetic Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and Una-May
                 O'Reilly and Peter J. Angeline",
  chapter =      "18",
  pages =        "425--445",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-19423-6",
  notes =        "AiGP3",
}

@InProceedings{zhang:1999:fogp,
  author =       "Byoung-Tak Zhang",
  title =        "Bayesian Genetic Programming",
  booktitle =    "Foundations of Genetic Programming",
  year =         "1999",
  editor =       "Thomas Haynes and William B. Langdon and Una-May
                 O'Reilly and Riccardo Poli and Justinian Rosca",
  address =      "Orlando, Florida, USA",
  month =        "13 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/fogp99zhang.ps.gz",
  size =         "3 pages",
  notes =        "GECCO'99 WKSHOP, part of haynes:1999:fogp",
}

@InProceedings{zhang:2000:B,
  author =       "Byoung-Tak Zhang",
  title =        "Bayesian evolutionary algorithms for learning and
                 optimization",
  booktitle =    "Optimization By Building and Using Probabilistic",
  year =         "2000",
  pages =        "220--223",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS",
}

@Article{Zhang:2000:bmeGP,
  author =       "Byoung-Tak Zhang",
  title =        "Bayesian Methods for Efficient Genetic Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2000",
  volume =       "1",
  number =       "3",
  pages =        "217--242",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Bayesian
                 genetic programming, probabilistic evolution, adaptive
                 Occam's razor, incremental data inheritance, parsimony
                 pressure, data subset selection",
  ISSN =         "1389-2576",
  abstract =     "A Bayesian framework for genetic programming (GP) is
                 presented. This is motivated by the observation that
                 genetic programming iteratively searches populations of
                 fitter programs and thus the information gained in the
                 previous generation can be used in the next generation.
                 The Bayesian GP makes use of Bayes theorem to estimate
                 the posterior distribution of programs from their prior
                 distribution and likelihood for the fitness data
                 observed. Offspring programs are then generated by
                 sampling from the posterior distribution by genetic
                 variation operators. We present two GP algorithms
                 derived from the Bayesian GP framework. One is the
                 genetic programming with the adaptive Occam?s razor
                 (AOR) designed to evolve parsimonious programs. The
                 other is the genetic programming with incremental data
                 inheritance (IDI) designed to accelerate evolution by
                 active selection of fitness cases. A multiagent
                 learning task is used to demonstrate the effectiveness
                 of the presented methods. In a series of experiments,
                 AOR reduced solution complexity by 20% and IDI doubled
                 evolution speed, both without loss of solution
                 accuracy.",
}

@InProceedings{Zhang-JoungPPSN2000,
  author =       "Byoung-Tak Zhang and Je-Gun Joung",
  title =        "Building Optimal Commitees of Genetic Programs",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VI 6th
                 International Conference",
  editor =       "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
                 Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
                 Merelo and Hans-Paul Schwefel",
  year =         "2000",
  publisher =    "Springer Verlag",
  address =      "Paris, France",
  month =        "16-20 " # sep,
  note =         "LNCS 1917",
  keywords =     "genetic algorithms, genetic programming",
}

@Article{Zhang:2001:RCIM,
  author =       "Fengdong Zhang and Deyi Xue",
  title =        "Optimal concurrent design based upon distributed
                 product development life-cycle modeling",
  year =         "2001",
  journal =      "Robotics and Computer-Integrated Manufacturing",
  volume =       "17",
  pages =        "469--486",
  number =       "6",
  email =        "xue@enme.ucalgary.ca",
  keywords =     "genetic algorithms, genetic programming, Concurrent
                 design, Particle swarm optimization (PSO), Distributed
                 computing",
  URL =          "http://www.sciencedirect.com/science/article/B6V4P-44HY42C-4/1/f2248f45ed58708fb7f455b343fa8cca",
  abstract =     "This research introduces an optimal concurrent design
                 approach based upon a previously developed distributed
                 product development life-cycle modeling method. In this
                 approach, the product realization process alternatives
                 and relevant activities are modeled at different
                 locations that are connected through the Internet.
                 Relations among these alternative activities are
                 described by an AND/OR graph. The optimal product
                 realization process alternative and its parameter
                 values are identified using a multi-level optimization
                 method. Genetic programming (GP) and particle swarm
                 optimization (PSO) are employed for identifying the
                 optimal product realization process alternative and the
                 optimal parameter values of the feasible alternatives,
                 respectively.",
}

@InProceedings{zhang:1999:GPmcod,
  author =       "Mengjie Zhang and Victor Ciesielski",
  title =        "Genetic Programming for Multiple Class Object
                 Detection",
  booktitle =    "12th Australian Joint Conference on Artificial
                 Intelligence",
  year =         "1999",
  editor =       "Norman Foo",
  volume =       "1747",
  series =       "LNAI",
  pages =        "180--192",
  address =      "Sydney, Australia",
  publisher_address = "Berlin",
  month =        "6-10 " # dec,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Neural networks, Vision",
  ISBN =         "3-540-66822-5",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66822-5",
  size =         "13 pages",
  abstract =     "We describe an approach to the use of genetic
                 programming for object detection problems in which the
                 locations of small objects of multiple classes in large
                 pictures must be found. The evolved programs use a
                 feature set computed from a square input field large
                 enough to contain each of objects of interest and are
                 applied, in moving window fashion, over the large
                 pictures in order to locate the objects of interest.
                 The fitness function is based on the detection rate and
                 the false alarm rate. We have tested the method on
                 three object detection problems of increasing
                 difficulty with four different classes of interest. On
                 pictures of easy and medium difficulty all objects are
                 detected with no false alarms. On difficult pictures
                 there are still significant numbers of errors, however
                 the results are considerably better than those of a
                 neural network based program for the same problems.",
  notes =        "http://www.cse.unsw.edu.au/~ai99/",
}

@InProceedings{zhao:1996:eec,
  author =       "Jun Zhao and Garrett Kearney and Alan Soper",
  title =        "Emotional Expression Classification by Genetic
                 Programming",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "197--202",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "GP-96LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{Zhao:1997:cprs,
  author =       "Kai Zhao and Jue Wang",
  title =        "{``}Chromosone-Protein{''}: {A} Representation
                 Scheme",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max Garzon and Hitoshi Iba and Rick
                 L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "Genetic Programming, Genetic Algorithms",
  pages =        "343",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  notes =        "GP-97",
}

@InProceedings{zhao:1998:ppcaecps,
  author =       "Kai Zhao and Jue Wang",
  title =        "Path Planning in Computer Animation Employing
                 Chromosome-Protein Scheme",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "439--447",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{zhao:2000:mrccGP,
  author =       "Kai Zhao and Jue Wang",
  title =        "Multi-robot cooperation and competition with genetic
                 programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and William B.
                 Langdon and Julian F. Miller and Peter Nordin and
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "349--360",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  abstract =     "In this paper, we apply Genetic Programming (GP) on
                 multi-robot cooperation and competition problem. GP is
                 taken as a real time planning method in stead of
                 learning method. Robot all use GP to make a plan and
                 then walk according to the plan. The environment is
                 composed of two parts, natural environment, which is
                 the obstacles, and social environment that refers to
                 other robots. The cooperation process is accomplished
                 by robot's adaptation to both of them. In spite of the
                 fact that there is no communication among robots and
                 little knowledge about how to cooperate well, the
                 adaptive capability in dynamic environment enable
                 robots to complete a common task or solve the
                 competition. Several experiments are taken and the
                 results are shown.",
  notes =        "EuroGP'2000, part of poli:2000:GP",
}

@InProceedings{Zhu:1997:mpdGAvs,
  author =       "Rixin Zhu and Steven J. Skerlos and Richard E. DeVor
                 and Shiv G. Kapoor",
  title =        "Application of Genetic Algorithm to Machining Process
                 Diagnostics with a {DOE}-Based {GA} Validation Scheme",
  booktitle =    "Late Breaking Papers at the 1997 Genetic Programming
                 Conference",
  year =         "1997",
  editor =       "John R. Koza",
  pages =        "273--279",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California,
                 94305-3079, USA",
  month =        "13--16 " # jul,
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms",
  ISBN =         "0-18-206995-8",
  notes =        "GP-97LB The email address for the bookstore for mail
                 orders is mailorder@bookstore.stanford.edu Phone no
                 415-329-1217 or 800-533-2670",
}

@InProceedings{ShininZhu:1998:dffcaEPfo,
  author =       "Shinin Zhu and Robert G. Reynolds",
  title =        "The Design of Fully Fuzzy Cultural Algorithms with
                 Evolutionary Programming for Function Optimization",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "795--800",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolutionary programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{ShininZhu:1998:ifkrpscaEP,
  author =       "Shinin Zhu and Robert G. Reynolds",
  title =        "The Impact of Fuzzy Knowledge Representation on
                 Problem Solving in Cultural Algorithms with
                 Evolutionary Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and Kumar
                 Chellapilla and Kalyanmoy Deb and Marco Dorigo and
                 David B. Fogel and Max H. Garzon and David E. Goldberg
                 and Hitoshi Iba and Rick Riolo",
  pages =        "801--806",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "evolutionary programming",
  ISBN =         "1-55860-548-7",
  notes =        "GP-98",
}

@InProceedings{zhu:2001:tesaec,
  author =       "Fangming Zhu and Sheng-Uei Guan",
  title =        "Towards Evolution of Software Agents in Electronic
                 Commerce",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "1303--1308",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, software
                 agents, agent evolution",
  ISBN =         "0-7803-6658-1",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 SAFER. Multi-Agent (modular) evolution. Factory
                 simulation. Java.",
}

@InProceedings{ziegler:2000:E,
  author =       "Jens Ziegler and Wolfgang Banzhaf",
  title =        "Evolving a {"}nose{"} for a robot",
  booktitle =    "Evolution of Sensors in Nature, Hardware, and",
  year =         "2000",
  pages =        "226--230",
  address =      "Las Vegas, Nevada, USA",
  month =        "8 " # jul,
  keywords =     "genetic algorithms, genetic programming, Artificial
                 Chemistry, Autonomous Robots, Khepera",
  notes =        "GECCO-2000WKS Part of wu:2000:GECCOWKS introns,
                 Khepera robot, chemotaxis",
}

@InProceedings{Ziemeck:1997:vcGENCODER,
  author =       "Patrick Ziemeck and Helge Ritter",
  title =        "Evolving low-level Vision Capabilities with the
                 {GENCODER} Genetic Programming Environment",
  booktitle =    "ICANNGA97",
  year =         "1997",
  editor =       "George D. Smith and Nigel C. Steele and Rudolf F.
                 Albrecht",
  pages =        "78--82",
  address =      "University of East Anglia, Norwich, UK",
  month =        "2-4 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-211-83087-1",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}

@InProceedings{zitzler:1999:EABESSDSP,
  author =       "Eckart Zitzler and Jurgen Teich and Shuvra S.
                 Bhattacharyya",
  title =        "Evolutionary Algorithm Based Exploration of Software
                 Schedules for Digital Signal Processors",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
                 and Max H. Garzon and Vasant Honavar and Mark Jakiela
                 and Robert E. Smith",
  volume =       "2",
  pages =        "1762--1770",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications",
  ISBN =         "1-55860-611-4",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference
                 (GP-99)

                 Total system modeling, programs represented only at
                 high level within essentially linear GA chromosome.
                 Motorola DSP 56k for example.

                 ",
}

@InCollection{zomorodian:1994:,
  author =       "Afra Zomorodian",
  title =        "Context-Free Language Induction by Evolution of
                 Deterministic Push-Down Automata Using Genetic
                 Programming",
  booktitle =    "Genetic Algorithms at Stanford 1994",
  year =         "1994",
  editor =       "John R. Koza",
  pages =        "184--193",
  address =      "Stanford, California, 94305-3079 USA",
  month =        dec,
  organisation = "Stanford University",
  publisher =    "Stanford Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-18-187263-3",
  notes =        "This volume contains 20 papers written and submitted
                 by students describing their term projects for the
                 course {"}Genetic Algorithms and Genetic Programming{"}
                 (Computer Science 426) at Stanford University offered
                 during the fall quarter 1994
                 http://www-cs-faculty.stanford.edu/~koza/cs426.html",
}

@InProceedings{zomorodian:1995:cfli,
  author =       "Afra Zomorodian",
  title =        "Context-Free Language Induction by Evolution of
                 Deterministic Push-Down Automata Using Genetic
                 Programming",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "127--133",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "AAAI-95f GP

                 {\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457
                 {\em email} info@aaai.org {\em URL:}
                 http://www.aaai.org/",
}

@TechReport{zonger:1996:lilgp,
  author =       "Douglas Zongker and Bill Punch",
  title =        "lilgp 1.01 User's Manual",
  institution =  "Michigan State University",
  year =         "1996",
  address =      "USA",
  month =        "26 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp://garage.cps.msu.edu/pub/GA/lilgp/lilgp1.02.ps",
  notes =        "lilgp sources and tools at same ftp site",
  size =         "62 pages",
}
