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dc.contributor.authorSchwefel, H.-P.de
dc.date.accessioned2004-12-07T08:20:08Z-
dc.date.available2004-12-07T08:20:08Z-
dc.date.created1999de
dc.date.issued2001-10-16de
dc.identifier.urihttp://hdl.handle.net/2003/5376-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15258-
dc.description.abstractUninitiated may find it strange that artificial evolution resides among a class of problem solving methods belonging to a field named computational intelligence. Some people still believe that nature's trial-and-error way to adapt subsystems to their environment is a prodigal game at dice that has led to admirable results only due to vast resources of time and space. A rather simple gedankenexperiment, however, reveals that all 10E80 most elementary particles in the universe together with 10E60 tiniest time steps since the begin of time cannot explain the development of even simplest bacterial genomes by pure random sampling. Organic evolution must have found a more efficient way to develop clever individuals and manage complex systems. Since about forty years now, a couple of scientists have tried to mimic this process, and they have learned to exploit some tricks of life for solving an amazing variety of design and management tasks. This paper tries to give an overview of some recent applications as well as a summary of what we know about the general behavior of evolutionary algorithms.en
dc.format.extent100360 bytes-
dc.format.extent108210 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 71de
dc.subject.ddc004de
dc.titleEvolving Solutions for Design and Management Tasks on Computersen
dc.typeTextde
dc.type.publicationtypereport-
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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