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dc.contributor.authorGiel, Oliver-
dc.contributor.authorLehre, Per Kristian-
dc.date.accessioned2007-06-04T16:20:03Z-
dc.date.available2007-06-04T16:20:03Z-
dc.date.issued2007-06-04T16:20:03Z-
dc.identifier.urihttp://hdl.handle.net/2003/24343-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-9000-
dc.description.abstractMulti-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.language.isoende
dc.relation.ispartofseriesReihe CI;202/06de
dc.subject.ddc004-
dc.titleOn the effect of populations in evolutionary multi-objective optimizationen
dc.typeTextde
dc.type.publicationtypereport-
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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