Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
| dc.contributor.author | Beyer, Hans-Georg | de |
| dc.contributor.author | Deb, Kalyanmoy | de |
| dc.date.accessioned | 2004-12-07T08:20:02Z | |
| dc.date.available | 2004-12-07T08:20:02Z | |
| dc.date.created | 1999 | de |
| dc.date.issued | 2001-10-16 | de |
| dc.description.abstract | Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored only with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly-used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need of emphasizing further studies on self-adaptive GAs. | en |
| dc.format.extent | 577230 bytes | |
| dc.format.extent | 907680 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.format.mimetype | application/postscript | |
| dc.identifier.uri | http://hdl.handle.net/2003/5370 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-15294 | |
| dc.language.iso | en | de |
| dc.publisher | Universität Dortmund | de |
| dc.relation.ispartofseries | Reihe Computational Intelligence ; 61 | de |
| dc.subject.ddc | 004 | de |
| dc.title | Self-Adaptive Genetic Algorithms with Simulated Binary Crossover | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | |
| dcterms.accessRights | open access |