@Book{fogel:66,
  author = 	 {L. Fogel and A. Owens and M. Walsh},
  ALTeditor = 	 {},
  title = 	 {Intelligence Through Simulated Evolution},
  publisher = 	 {Wiley},
  year = 	 {1966},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {New York NY},
  OPTedition = 	 {},  
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@article{ sutton88learning,
    author = "R. S. Sutton",
    title = "Learning to Predict by the Methods of Temporal Differences",
    journal = "Machine Learning",
    volume = "3",
    pages = "9-44",
    year = "1988",
    url = "citeseer.ist.psu.edu/sutton88learning.html" }

@Book{Dreyfus:79,
  author = 	 {H. L. Dreyfus},
  ALTeditor = 	 {},
  title = 	 {What Computers Can't Do: The Limits of Artificial Intelligence},
  publisher = 	 {NY: Harper \& Row},
  year = 	 {1979},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {New York},
  edition = 	 {revised, edition},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Book{Dreyfus:92,
  author = 	 {H. L. Dreyfus},
  ALTeditor = 	 {},
  title = 	 {What Computers Still Can't Do: A Critique of Artificial Reason},
  publisher = 	 {USA: MIT Press},
  year = 	 {1993},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {Cambridge, MA},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@inproceedings{AlbrechtAGW04,
  author    = {K. Albrecht and
               R. Arnold and
               M. G{\"a}hwiler and
               R. Wattenhofer},
  title     = {Aggregating Information in Peer-to-Peer Systems for Improved
               Join and Leave.},
  booktitle = {Peer-to-Peer Computing},
  year      = {2004},
  pages     = {227-234},
  ee        = {http://csdl.computer.org/comp/proceedings/p2p/2004/2156/00/21560227abs.htm}, 
  bibsource = {DBLP, http://dblp.uni-trier.de}
}


@InProceedings{ga94aGathercole,
  author =       "C. Gathercole and P. 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",
}

@inproceedings{olmer96evolving,
    author = "M. Olmer and W. Banzhaf and P. Nordin",
    title = "Evolving Real-time Behavior Modules for a real Robot with Genetic Programming",
    booktitle = "Proceedings of the international symposium on robotics and manufacturing",
    address = "Montpellier, France",
    year = "1996",
    url = "citeseer.ist.psu.edu/olmer96evolving.html" }
   
@Article{lasarDittBanz2004, 
  author =   {C. W. G. Lasarczyk and P. Dittrich 
              and W. Banzhaf}, 
  title =    {Dynamic subset selection 
              based on a fitness case topology}, 
  journal =  {Evolutionary Computation}, 
  year =     2004, 
  volume =   12, 
  number =   2 
}


@article{friedberg,
   author    = {R. M. Friedberg},
   title     = {A Learning Machine - Part I},
   journal   = {IBM Journal of Research and Development},
   year      = {1958},
   pages     = {2-11}
}


@article{cramer,
   author    = {R. L. Cramer},
   title     = {A representation for adaptive generation of simple
		  sequential programs},
   journal   = {Proceedings of an International Conference on Genetic
		  Algorithms and Their Applications},
   year      = {1985},
   pages     = {183-187}
}


@Book{Spies:93,
  author = 	 {M. Spies},
  ALTeditor = 	 {},
  title = 	 {Unsicheres Wissen},
  publisher = 	 {Spektrum Verlag},
  year = 	 {1993},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {Heidelberg, Berlin, Oxford},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  note = 	 {ISBN 3-86025-006-X},
  OPTannote = 	 {}
}

@Book{Hertzberg:89,
  author = 	 {J. Hertzberg},
  editor = 	 {k. H. Böhling and U. Kulisch and H. Maurer},
  title = 	 {Planen: Einführung in die Planerstellungsmethoden der
künstlichen Intelligenz},
  publisher = 	 {BI Wissenschaftsverlag},
  year = 	 {1989},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  number = 	 {Band 65},
  series = 	 {Informatik},
  address = 	 {Mannheim, Wien, Zürich},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  note = 	 {ISBN 3-411-03223-5},
  OPTannote = 	 {}
}

%
%
%
@Book{book:disComp1,
  author = 	 {G.F. Coulouris, J. Dollimore and T. Kindberg},
  title = 	 {Distributed Systems, Concepts and Design},
  publisher = 	 {Addison-Wesley},
  year = 	 {1994},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  edition = 	 {2},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@Book{book:disComp2,
  author = 	 {J. Farley},
  title = 	 {Java Distributed Computing},
  publisher = 	 {O'Reilly},
  year = 	 {1998},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}



@incollection{Thruns:1995,
    author = "S. Thrun",
    title = "Learning To Play the Game of Chess",
    booktitle = "Advances in Neural Information Processing Systems 7",
    publisher = "The MIT Press",
    address = "Cambridge, MA",
    editor = "G. Tesauro and D. Touretzky and T. Leen",
    pages = "1069--1076",
    year = "1995",
    url = "citeseer.ist.psu.edu/article/thrun95learning.html" 
    }


@Book{Marsland:1990,
  editor = 	 {T.A. Marsland and J. Schaeffer},
  title = 	 {Computers, Chess, and Cognition},
  publisher = 	 {Springer-Verlag},
  year = 	 {1990},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {New York},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


%
% Angeline
%

@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.",
}

@InCollection{angeline:94,
  title =        "Genetic Programming and Emergent Intelligence",
  author =       "P. J. 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.",
}
  
@InCollection{Ang96,
  author = 	 {P.J. Angeline},
  title = 	 {Two Self-Adaptive Crossover Operations for Genetic Programming},
  booktitle = 	 { Genetic Programming: Volume 2},
  OPTcrossref =  {},
  OPTkey = 	 {},
  publisher =	  {MIT Press},
  year =	 {1996},
  OPTeditor = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTchapter = 	 {},
  OPTtype = 	 {},
  OPTaddress = 	 {},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTpages = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@InProceedings{Ang97,
  author = 	 {P.J. Angeline},
  title = 	 {Subtree Crossover: Building Block Engine or MacroMutation?},
  booktitle = 	 {Genetic Programming 1997: Proceedings of the
Second Annual Conference},
  OPTcrossref =  {},
  OPTkey = 	 {},
  pages = 	 {9-17},
  year = 	 {1997},
  editor = 	 {J. Koza and K. Deb and M. Dorigo and D. Fogel and M. Garzon and H. Iba and R.
Riolo},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {San Francisco, CA},
  OPTmonth = 	 {},
  OPTorganization = {},
  publisher = {Morgan Kaufmann},
  OPTnote = 	 {},
  OPTannote = 	 {}
}



@Article{Ang98,
    author = "P. J. Angeline",
    title = "Multiple Interacting Programs: {A} Representation for Evolving Complex Behaviors",
    journal = "Cybernetics and Systems",
    volume = "29",
    number = "8",
    pages = "779--806",
    year = "1998",
    url = "citeseer.ist.psu.edu/angeline98multiple.html" }


@InProceedings{Pol97,
  author =       "R. 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",
  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",
}


@TechReport{Edm98,
  author = 	 {B. Edmonds},
  title = 	 {Meta-Genetic Programming: Co-evolving the Operators of Variation},
  institution =  {Manchester Metropolitan University},
  year = 	 {1998},
  OPTkey = 	 {},
  type =	 {CPM Report},
  number =	 {98-32},
  OPTaddress = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {},
  www = {http://www.cpm.mmu.ac.uk/cpmrep32.htm}
}



%
% Astro Teller
%

@InCollection{teller1,
   author      = {A. Teller and M. Veloso},
   title       = {PADO: A new learning architecture for object recognition},
    booktitle  = {Symbolic Visual Learning},
   year        = {1996},
   publisher   = {Oxford University Press},
   pages       = {81 -116}
}

		 

@InCollection{teller2,
  author = 	{A. Teller},
  title = 	 {Evolving programmers: The co-evolution of intelligent
		  recombination operators},
  booktitle = 	 {Advances in Genetic Programming II},
  publisher = {MIT Press },
  year = 	 {1996},
  editor = 	 {P. Angeline and K. Kinnear}

}



@techreport{Astro3,
   author    = {A. Teller and M. Veloso},
   title     = {SMART Mutation},
   institution = {Department of Computer Science, Carnegie Mellon University},
   year      = {1996},
   number    = {Currently unpublished technical report (Homepage)}
}
		 
@techreport{Astro5,
   author    = {A. Teller},
   title     = {Evolving programmers: The co-evolution of intelligent
		  recombination operators},
   institution = {Department of Computer Science, Carnegie Mellon University},
   year      = {1996},
   number    = {Currently unpublished technical report (Homepage)}
}




@book{koza1,
   author    = {J. Koza},
   title     = {Genetic Programming},
   year      = {1992},
   publisher = {MIT Press},
   address   = {Cambridge, MA}
}


@book{koza2,
   author    = {J. Koza},
   title     = {Genetic Programming II},
   year      = {1994},
   publisher = {MIT Press},
   address   = {Cambridge, MA}
}


@book{schwefel1,
   author    = {H-P. Schwefel},
   title     = {Evolution and Optimum Seeking},
   year      = {1996},
   publisher = {John Wiley \& Sons, Inc.}
}

@book{gold,
   author    = {D. Goldberg},
   title     = {Genetic Algorithms: In search, optimization,
   		and machine learning},
   year      = {1989},
   publisher = {Addison-Wesley Press.}
}
@book{holland,
   author    = {J. Holland},
   title     = {Adaption in Natural and Artifical Systems},
   year      = {1975},
   publisher = {MI: The University of Michigan Press},
}

 
		 
 	 
		 
@InCollection{iba:1996:aigp2,
  author =       "H. Iba and H. {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.",
  size =         "pages",
}
     

%
% Banzhaf
%
%Introns

@TechReport{banzhaf:mrl:tech,
  author =       "W. 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",
}


@Book{banzhaf:1997:book,
  author =       "W. Banzhaf and P. Nordin and R. E. Keller
                 and F. D. Francone",
  title =        "Genetic Programming -- An Introduction On the
                 Automatic Evolution of Computer Programs and its
                 Applications",
  publisher =    "Morgan Kaufmann, San Francisco und dpunkt verlag, Heidelberg",
  year =         "1998",
  keywords =     "genetic algorithms, genetic programming",
}


@InProceedings{olmer:1996:ertbm,
   author =       "M. Olmer and W. Banzhaf and P. 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{banzhaf:1996:mutatation,
  author =       "W. Banzhaf and F. D. Francone and P.
                 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",
}

@InProceedings{banzhaf:1997:gabrrfr,
  author =       "W. Banzhaf and P. Nordin and M. 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",
}

@InProceedings{BBD98iccs,
  author =       {W. Banzhaf and D. Banscherus and P. Dittrich},
  title =        {Hierarchical Genetic Programming using Local Modules},
  booktitle =    {Proc. Intl. Conference on Complex Systems, Nashua, NH, Oct 1998 (in press)},
  OPTcrossref =  {},
  OPTkey =       {},
  OPTeditor =    {},
  OPTvolume =    {},
  OPTnumber =    {},
  OPTseries =    {},
  OPTyear =      {},
  OPTorganization = {},
  OPTpublisher = {},
  OPTaddress =   {},
  OPTmonth =     {},
  OPTpages =     {},
  OPTnote =      {},
  OPTannote =    {}
}


%
% Bennet
%
@inbook{bennet1,
   author    = {H. C. Bennet},
   title     = {The Universal Turing Machine},
   chapter   = {Logical Depth  and Physical Complexity},
   year      = {1988},
   OPTeditor    = {Rolf Herken},
   publisher = {Hamburg/Berlin (Kammerer \& Unverzagt)},
   pages     = {227 - 257}
}

%
% Brameier
% 

 @TechReport{brameier:2000:TR,
  author = 	 {M. Brameier and W. Banzhaf},
  title = 	 {Evolving Teams of Mutiple Predictors with {G}enetic {P}rogramming},
  institution =  {Universität Dortmund SFB 531},
  year = 	 {2001},
  OPTkey = 	 {},
  OPTtype = 	 {},
  OPTnumber = 	 {},
  OPTaddress = 	 {},
  OPTmonth = 	 {},
  note = 	 {Data available from the authors.},
  OPTannote = 	 {}
}


@InProceedings{brameier:1999:PMCGP,
  author =       "M. Brameier and F. Hoffmann and P. Nordin
                 and W. Banzhaf and F. 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,
  author =       "M. Brameier and W. 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 =       "M. Brameier and W. 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 =       "M. Brameier and W. 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",
}


%
%	Conor Ryan
%
@InProceedings{ryan:1998:geepal,
  author =       "C. Ryan and J. J. Collins and M. 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, grammatical
                 evolution",
  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+azad:2003:gecco:workshop,
  title =        "Sensible Initialisation in Grammatical Evolution",
  author =       "C. Ryan and R. Muhammad Atif Azad",
  pages =        "142--145",
  booktitle =    "{GECCO 2003}: Proceedings of the Bird of a Feather
                 Workshops, Genetic and Evolutionary Computation
                 Conference",
  editor =       "Alwyn M. Barry",
  year =         "2003",
  month =        "11 " # jul,
  publisher =    "AAAI",
  address =      "Chigaco",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
  notes =        "Bird-of-a-feather Workshops, GECCO-2003. A joint
                 meeting of the twelth International Conference on
                 Genetic Algorithms (ICGA-2003) and the eigth Annual
                 Genetic Programming Conference (GP-2003) part of
                 barry:2003:GECCO:workshop",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
}


@inproceedings{oneill99geneticCode,
      author={M. O'Neill and C. Ryan},
      title={Genetic Code Degeneracy: Implications for Grammatical Evolution and Beyond},
      year={1999},
      pages={149-153},
      address={Berlin},
      editor={D. Floreano, J. Nicoud and F. Mondada},
      url={http://www.isrl.uiuc.edu/~amag/langev/paper/oneill99geneticCode.html},
      publisher={Springer-Verlag},
      booktitle={ECAL99}
}


@inproceedings{oneill01crossoverIn,
      author={M. O'Neill and C. Ryan and M. Keijzer and M. Cattolico},
      title={Crossover in Grammatical Evolution: The Search Continues},
      year={2001},
      pages={337-347},
      url={http://www.isrl.uiuc.edu/~amag/langev/paper/oneill01crossoverIn.html},
      booktitle={EuroGP 2001}
}



%
% dittrich
%
@InProceedings{DBB:1998:EvoRob,
  author = 	 {P. Dittrich and A. Buergel and W. Banzhaf},
  title = 	 {Evolutionary Robotics, First European Workshop, EvoRob98},
  booktitle = 	 {Learning to Move a Robot with Random Morphology},
  OPTcrossref =  {},
  OPTkey = 	 {},
  pages = 	 {165-178},
  year = 	 {April 1998},
  editor = 	 {Phil Husbands and Jean-Arcady Meyer (eds.)},
  OPTvolume = 	 {},
  OPTnumber = 	 {LNCS 1468},
  series = 	 {LNCS 1468},
  OPTaddress = 	 {},
  OPTmonth = 	 {},
  OPTorganization = {},
  OPTpublisher = {Springer, Berlin},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@inproceedings{DZB98,
        AUTHOR          = {P. Dittrich and J. Ziegler and
                         W. Banzhaf},
        TITLE           = {{Mesoscopic analysis of self-evolution in an
                        artificial chemistry}},
        EDITOR          = {C.~Adami and R.~K.~Belew and H.~Kitano and
                        C.~E.~Taylor},
        BOOKTITLE       = {Artificial Life VI, Los Angeles CA,
                        June~26--29,~1998, Proc.},
        PUBLISHER       = {The MIT Press, Cambridge MA},
        YEAR            = {1998},
        ISBN            = {0-262-51099-5},
        PAGES           = {95--103}
}

@inproceedings{DZB98b,
        AUTHOR          = {P. Dittrich and J. Ziegler and
                         W. Banzhaf},
        TITLE           = {{Mesoscopic analysis of an artificial chemistry
                        with self-organizing topology}},
        EDITOR          = {C.~Wilke and S.~Altmeyer and T.~Martinetz},
        BOOKTITLE       = {Third German Workshop on Artificial Life:
                        Abstracting and Synthesizing the Principles of
                        Living Systems},
        PUBLISHER       = {Verlag Harri Deutsch, Frankfurt a.M.},
        YEAR            = {1998},
        ISBN            = {3-8171-1591-1},
        PAGES           = {155--170}
}


%
% Droste
%

@InProceedings{pnfl:99,
  author = 	 {S. Droste and T. Jansen},
  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 =       "2",
   pages =        "833--839",
   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)"
}

%
% Feldt
%
@inproceedings{feldt00gpbeagle,
    author = "R. Feldt and M. O'Neill and C. Ryan and P. Nordin and W. B.
Langdon",
    title = "{GP-Beagle:} {A} Benchmarking Problem Repository for the Genetic Programming
Community",
    booktitle = "Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference",
    month = "8",
    address = "Las Vegas, Nevada, USA",
    editor = "Darrell Whitley",
    pages = "90--97",
    year = "2000",
    url = "citeseer.nj.nec.com/302050.html"
}



%
%Forrest
%
@incollection{forrest93relative,
    author = "S. Forrest and M. Mitchell",
    title = "Relative Building-Block Fitness and the Building-Block Hypothesis",
    booktitle = "Foundations of Genetic Algorithms 2",
    publisher = "Morgan Kaufmann",
    address = "San Mateo, CA",
    editor = "L. Darrell Whitley",
    pages = "109--126",
    year = "1993",
    url = "citeseer.ist.psu.edu/forrest93relative.html" }
   
%
% Francone
%

@InProceedings{francone:1996:bench,
  author =       "F. D. Francone and P. Nordin and W.
                 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{francone:1999:HCGP,
   author =       "F. D. Francone and M. Conrads and W.
                  Banzhaf and P. 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)",
 }

%
% Huelsbergen
%



@InProceedings{huelsbergen96,
  author = 	 {L. Huelsbergen},
  title = 	 {Toward simulated evolution of machine language iteration},
  booktitle = 	 {Genetic Programming 1996: Proceedings of the First Annual Conference},
  OPTcrossref =  {},
  OPTkey = 	 {},
  pages = 	 {315 -- 320},
  year = 	 {1996},
  editor = 	 {J. R. Koza and D. E. Goldberg and D. B. Fogle and R. L. Riolo},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {Stanford University CA.},
  OPTmonth = 	 {},
  OPTorganization = {},
  publisher =    {MIT Press Cambridge},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

%
% Kantschik
%

@InProceedings{kantschik:1999:EuroGP,
  author =       "W. Kantschik and P. Dittrich and M.
                 Brameier and W. 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:1999:CEC,
  author = 	 {W. Kantschik and P. Dittrich and M.
                 Brameier and W. Banzhaf},
  title = 	 {Empirical analysis of different levels of meta-evolution},
  booktitle = 	 {1999 Congress on Evolutionary Computation (CEC'99)},
  OPTcrossref =  {},
  OPTkey = 	 {},
  pages = 	 {2086--2093},
  year = 	 {1999},
  editor = 	 {P. J. Angeline and V. W. Porto},
  volume = 	 {3},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {Washington D.C.},
  month = 	 {July 6-9},
  OPTorganization = {},
  publisher = {IEEE Press, Piscataway NJ},
  OPTnote = 	 {},
  OPTannote = 	 {}
}



@TechReport{kantschik:2000:TechRep,
  author = 	 {J. Busch and W. Kantschik and H. Aburaya and K. Albrecht and R. Groß and
P. Gundlach and M. Kleefeld and A. Skusa and M. Villwock and T. Vogd and W. Banzhaf},
  title = 	 {Evolution von {GP}-{A}genten mit {S}chachwissen sowie deren
{I}ntegration in ein {C}omputerschachsystem},
  institution =  {Systems Analysis Research Group},
  year = 	 {2000},
  OPTkey = 	 {},
  type = 	 {SYS Report},
  number = 	 {SYS-01/00, ISSN 0941-4568},
  address = 	 {Univ. Dortmund, Informatik},
  month = 	 {10},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@InProceedings{kantschik:2001:EuroGP,
  author =       "W. Kantschik and W. Banzhaf",
  title =        "Linear-{T}ree {GP} and its comparison with other {GP}
                 structures",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "J. F. Miller and M. Tomassini and P. Luca
                 Lanzi and C. Ryan and A. G. B. Tettamanzi and
                 W. 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,
  author =       "W. Kantschik and W. Banzhaf",
  title =        "Linear-{G}raph {GP} - {A} new {GP} {S}tructure",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2002",
  year =         "2002",
  editor =       "J. F. Miller and M. Tomassini and P. Luca
                 Lanzi and C. Ryan and A. G. B. Tettamanzi and
                 W. B. Langdon",
  volume =       "2278",
  series =       "LNCS",
  pages =        "83--92",
  address =      "Kinsale, Irland",
  publisher_address = "Berlin",
  month =        "3-5" # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Linear tree
                 structure, GP representation, Crossover",
  ISBN =         "3-540-43378-3",
  size =         "10 pages",

}
@InProceedings{kantschik: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{CiRep:sysgp,
   author    = {M. Brameier and P. Dittrich and  W. Kantschik
   		and W. Banzhaf},
   title     = { {SYSGP} - {A} {C}++ library of different {GP} variants},
   institution = {Fachbereich Informatik, Universit\"at Dortmund},
   year      = {1998},
   number    = {Internal Report of SFB 531,ISSN 1433-3325}
}


@InProceedings{DSKB:1999:Gecco,
  author = 	 {P. Dittrich and A. Skusa and W. Kantschik and W. Banzhaf},
  title = 	 {Dynamical Properties of the Fitness Landscape of a GP Controlled Random Morphology
Robot},
  booktitle = 	 {Proceedings of the Genetic and Evolutionary Computation Conference},
  OPTcrossref =  {},
  OPTkey = 	 {},
  pages = 	 {1002-1008},
  year = 	 {1999},
  editor = 	 {W. Banzhaf and Jason Daida and A. E. Eiben and M. H. Garzon and V. Honavar and Mark
Jakiela and Robert E. Smith (Eds.)},
  volume = 	 {2},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  OPTmonth = 	 {},
  OPTorganization = {},
  publisher = {Morgan Kaufmann, San Francisco, CA,},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


%
% Kazadi
%

@InProceedings{kazadi:1997:ICGA,
  author = 	 {S. T. Kazadi},
  title = 	 {Conjugate Schema in Genetic Search},
  booktitle = 	 {Proceedings of The Seventh International Conference on of Genetic Algorithms (ICGA
97)},
  OPTcrossref =  {},
  OPTkey = 	 {},
  pages = 	 {10-17},
  year = 	 {1997},
  editor = 	 {Thomas Bäck},
  OPTvolume = 	 {},
  number = 	 {7},
  OPTseries = 	 {},
  address = 	 {Michigan State University, East Lansing, MI},
  month = 	 {july},
  OPTorganization = {},
  publisher = {Morgan Kaufmann},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


%
% Keller
%
@InProceedings{keller:1999:TEGCGP,
  author =       "R. E. Keller and W. 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 algorithms, genetic programming and evolvable
                 hardware",
  ISBN =         "1-55860-611-4",
  URL =          "http://ls11-www.informatik.uni-dortmund.de/people/banzhaf/t.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/244531.html",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-438.pdf",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-438.ps",
  abstract =     "In most Genetic Programming (GP) approaches, the space
                 of genotypes, that is the search space, is identical to
                 the space of phenotypes, that is the solution space.
                 Developmental approaches, like Developmental Genetic
                 Programming (DGP), distinguish between genotypes and
                 phenotypes and use a genotypephenotype mapping prior to
                 fitness evaluation of a phenotype. To perform this
                 mapping, DGP uses a problem-specific manually designed
                 genetic code, that is a mapping from genotype
                 components to phenotype components. The employed
                 genetic code is critical for the performance of the
                 underlying search process. Here, the evolution of
                 genetic code is introduced as a novel approach for
                 enhancing the search process. It is hypothesized that
                 code evolution improves the performance of
                 developmental approaches by enabling them to
                 beneficially adapt the fitness landscape during search.
                 As the first step of investigation, this article
                 empirically shows the operativeness of code evol...",
  notes =        "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 =       "R. E. Keller and W. 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",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.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 \cite{spector:2001:GECCO}",
}




@InProceedings{keller:2002:bnaic,
  author =       "R. E. Keller and W. A. Kosters and 
                 M. van der Vaart and M. 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 Universite 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{"}.

                 Summarised in \cite{langdon:2002:bnaic02ec2}"
}
%
% Langdon
%


@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://ftp.cwi.nl/pub/W.B.Langdon/papers/WBL.euro98_bloatm.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",
}


%
% Levenick
%
@inproceedings{levenick91inserting,
    author = "J. R. Levenick",
    title = "Inserting introns improves genetic algorithm success rate: {T}aking a cue from
biology",
    booktitle = "Proceedings of the Fourth International Conference on Genetic Algorithms",
    publisher = "Morgan Kaufman",
    address = "San Mateo, CA",
    editor = "Rick Belew and Lashon Booker",
    pages = "123--127",
    year = "1991",
    url = "citeseer.ist.psu.edu/levenick91inserting.html" }

%
% McPhee
%

@InProceedings{McPhee:1995:acrep,
  author =       "N. McPhee and J. D. 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:1999:A,
  author =       "N. F. McPhee and N. 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)",
}



%
% Niehaus
%

@InProceedings{niehaus:2001:EuroGP,
  author =       "J. Niehaus and W. 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{niehaus03,
  author =       "J. Niehaus and W. Banzhaf",
  title =        "More on Computational Effort Statistics for Genetic
                 Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2003",
  year =         "2003",
  editor =       "Conor Ryan and Terence Soule and Maarten Keijzer and
                 Edward Tsang and Riccardo Poli and Ernesto Costa",
  volume =       "2610",
  series =       "LNCS",
  pages =        "168--177",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-00971-X",
  abstract =     "In this contribution we take a look at the
                 computational effort statistics as described by KOZA.
                 We transfer the notion from generational genetic
                 programming to tournament-selection (steady-state) GP
                 and show why, in both cases, the measured value of the
                 effort often differs from its theoretical counterpart.
                 It is discussed how systematic estimation errors are
                 introduced by a low number of experiments. Two reasons
                 examined are the number of unsuccessful experiments and
                 the variation in the number of fitness evaluations
                 necessary to find a solution among the successful
                 experiments.",
  notes =        "EuroGP'2003 held in conjunction with EvoWorkshops
                 2003",
}


@PhdThesis{niehaus03:Diss,
  author = 	 {J. Niehaus},
  title = 	 {Graphbasierte Genetische Programmierung},
  school = 	 {Universität Dortmund},
  year = 	 {2003},
  key = 	 {DissDo 2003/192},
  OPTtype = 	 {},
  OPTaddress = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}



%
% Nordin
%
@InProceedings{nordin:linGP,
   author    = {J. P. Nordin},
   title     = {A Compiling Genetic Programming System that Directly
		  Manipulates  the Machine code},
   year      = {1994},
   editor    = {K. Kinnear, Jr.},
   publisher = {MIT Press},
   address   = {Cambridge}
}
		 
@InProceedings{nordin2,
   author    = {J. P. Nordin and W. Banzhaf},
   title     = {Evolving Turing complete Programs for a Register
		  Machine with Self Modifying Code},
   organization = {Proceedings of the Sixth International conference
		  of Genetic Algorithms},
   year      = {1995},
   publisher = {Morgan Kaufmann Publishers},
   address   = {Pittsburgh, Penn., USA}
}

@InProceedings{nordin3,
   author    = {J. P. Nordin and W. Banzhaf},
   title     = {Complexity Compression and Evolution},
   organization = {Proceedings of the Sixth International Conference
		  of Genetic Algorithms},
   year      = {1995},
   publisher = {Morgan Kaufmann Publishers},
   address   = {Pittsburgh, Penn., USA}
}

@techreport{NoFrBa95,
    author  = {P. Nordin and F. Francone and W. Banzhaf},
    title = {Explicitly Defined Introns and Destructive Crossover in Genetic Programming},
    institution = {Universit\"at Fachbereich Informatik Lehrstuhl XI},
    year    = {1995},
    month   = {May},
    address = {Dortmund},
    number  = {SYS-3/95},
    keywords = {genetic programming, crossover},
    rubrik  = {4.06},
    standort = {2.59},
    besitzer = {ls11},
    status  = {da},
    einstelldatum = {1995, sep}
}	

@InProceedings{Nordin:1995:cce,
  author =       "P. Nordin and W. 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{nordin95explicitly,
    author = "P. Nordin and F. Francone and W. 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",
    month = "9",
    address = "Tahoe City, California, USA",
    editor = "Justinian P. Rosca",
    pages = "6--22",
    year = "1995",
    url = "citeseer.nj.nec.com/349513.html"
}

@InProceedings{nordin:1996:pcis,
  author =       "P. Nordin and W. 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",
}

@misc{nordin97real,
    author = "P. Nordin and W. Banzhaf",
    title = "Real time control of a khepera robot using genetic programming",
    text = "Nordin, P., and Banzhaf, W. 1997. Real time control of a khepera robot
      using genetic programming. Cybernetics and Control 26(3).",
    year = "1997",
    url = "citeseer.nj.nec.com/nordin97real.html"
}

@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",
}



@InProceedings{nordin:1998:Rob,
  author = 	 {P. Nordin and W. Banzhaf and M. Brameier},
  title = 	 {Evolution of a world model for a miniature robot using genetic programming},
  booktitle = 	 {Robotics and Autonomous Systems},
  OPTcrossref =  {},
  OPTkey = 	 {},
  pages = 	 {105-116},
  year = 	 {1998},
  OPTeditor = 	 {},
  OPTvolume = 	 {},
  number = 	 {25},
  series = 	 {1-2},
  OPTaddress = 	 {},
  OPTmonth = 	 {},
  OPTorganization = {},
  OPTpublisher = {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Inproceedings{nordin:1999:AP,
   author =      {P. Nordin and F. Hoffmann and F. D. Francone and M. Brameier and W.
 Banzhaf},
   title =       {AIM-GP and Parallelism},
   booktitle =   {Proceedings of the Congress of 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 July},
   organisation ={Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary
 Programming Society, Galesia, IEE},
   publisher =   {IEEE Press},
   note =        {},
   keywords =    {genetic algorithms, genetic programming, parallel and distributed processing},
   ISBN =        {0-7803-5536-9 (softbound)},
   ISBN =        {0-7803-5537-7 (Microfiche)},
   url =         {},
   size =        {},
   abstract =    {},
   notes =       {CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia,
and
 the IEE.

   Library of Congress Number      = 99-61143},
 }



 @InProceedings{brameier:1999:PMCGP,
   author =       "M. Brameier and F. Hoffmann and P. Nordin
                  and W. Banzhaf and F. 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)",
 }



@PhdThesis{nordin:thesis,
  author =       "P. 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
                 Visa-Card owners:

                 - order should arrive via FAX (+49 2501 261550) or
                 PGP-signed (and optionally encrypted, our PGP-key is
                 available upon request) email (with PGP public key
                 trustfully available). In the moment, these ways are
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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
                 date, as well as card holders name and address.

                 - 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
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                 total amount is drawn from the credit card in german
                 currency.",

  publisher =    "Krehl Verlag",
  publisher_address = "Postfach 51 01 42, D-48163 Muenster, GERMANY",
  ISBN =         "3-931546-07-1",
}

%
% Teller
%
@incollection{teller96pado,
    author = "A. Teller and M. Veloso",
    title = "{PADO}: {A} New Learning Architecture for Object Recognition",
    booktitle = "Symbolic Visual Learning",
    publisher = "Oxford University Press",
    editor = "Katsushi Ikeuchi and Manuela Veloso",
    pages = "81--116",
    year = "1996",
    url = "citeseer.nj.nec.com/article/teller95pado.html"
}


%
% Thornton
%
@misc{thornton97bbh,
  author = "C. Thornton",
  title = "The building block fallacy",
  text = "Thornton, C.: The building block fallacy. Complexity International 4 (1997)
    URL: http://www.csu.edu.au/ci/vol04/thornton/building.htm.",
  year = "1997",
  url = "citeseer.ist.psu.edu/article/thornton97building.html" }

%
% Sprave
%
@PhdThesis{Diss:Joe,
  author =       "J. Sprave",
  title =        "Ein einheitliches Modell für Populationsstrukturen in Evolutionären Algorithmen",
  school =       "der Universitat Dortmund am Fachereich Informatik",
  year =         "1999",
  keywords =     "genetic algorithms",

  ISBN =         "DissDo 99/0189",
}



%
% Wong
% Man Leung Wong and Kwong Sak Leung
@InCollection{wong:1996:aigp2,
  author =       "M. L. Wong and K. S. 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.",
}


@Article{ManLeungWong:1997:epidlg,
  author =       "M. L. Wong and K. S. 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",
}