Effective Linear Genetic Programming

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DC Field	Value	Language
dc.contributor.author	Banzhaf, Wolfgang	de
dc.contributor.author	Brameier, Markus	de
dc.date.accessioned	2004-12-07T08:20:47Z	-
dc.date.available	2004-12-07T08:20:47Z	-
dc.date.created	2001	de
dc.date.issued	2001-10-29	de
dc.identifier.uri	http://hdl.handle.net/2003/5407	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15250	-
dc.description.abstract	Different 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.extent	363958 bytes	-
dc.format.extent	399412 bytes	-
dc.format.mimetype	application/pdf	-
dc.format.mimetype	application/postscript	-
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 108	de
dc.subject.ddc	004	de
dc.title	Effective Linear Genetic Programming	en
dc.type	Text	de
dc.type.publicationtype	report	-
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 531

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