On the effect of populations in evolutionary multi-objective optimization
| dc.contributor.author | Giel, Oliver | |
| dc.contributor.author | Lehre, Per Kristian | |
| dc.date.accessioned | 2007-06-04T16:20:03Z | |
| dc.date.available | 2007-06-04T16:20:03Z | |
| dc.date.issued | 2007-06-04T16:20:03Z | |
| dc.description.abstract | Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. Most studies of MOEAs are empirical. Only recently, a few theoretical results have appeared. It is acknowledged that more theoretical research is needed. An important open problem is to understand the role of populations in MOEAs. We present a simple bi-objective problem which emphasizes when populations are needed. Rigorous runtime analysis point out an exponential runtime gap between a population-based algorithm (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the populationbased MOEA is successful and all other algorithms fail. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/24343 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-9000 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | Reihe CI;202/06 | de |
| dc.subject.ddc | 004 | |
| dc.title | On the effect of populations in evolutionary multi-objective optimization | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | |
| dcterms.accessRights | open access |