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dc.contributor.authorBanzhaf, Wolfgangde
dc.contributor.authorBrameier, Markusde
dc.date.accessioned2004-12-07T08:20:47Z-
dc.date.available2004-12-07T08:20:47Z-
dc.date.created2001de
dc.date.issued2001-10-29de
dc.identifier.urihttp://hdl.handle.net/2003/5407-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15250-
dc.description.abstractDifferent variants of genetic operators are introduced and compared for linear genetic programming including program induction without crossover. Variation strength of crossover and mutations is controlled based on the genetic code. Effectivity of genetic operations improves on code level and on fitness level. Thereby algorithms for creating code efficient solutions are presented.de
dc.format.extent363958 bytes-
dc.format.extent399412 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 108de
dc.subject.ddc004de
dc.titleEffective Linear Genetic Programmingen
dc.typeTextde
dc.type.publicationtypereport-
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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