Effective Linear Genetic Programming
| 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.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.identifier.uri | http://hdl.handle.net/2003/5407 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-15250 | |
| 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 | |
| eldorado.dnb.deposit | true |