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dc.contributor.authorHoroba, Christiande
dc.contributor.authorNeumann, Frankde
dc.date.accessioned2009-05-12T16:02:21Z-
dc.date.available2009-05-12T16:02:21Z-
dc.date.issued2008-12de
dc.identifier.urihttp://hdl.handle.net/2003/26164-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-9019-
dc.description.abstractOften the Pareto front of a multi-objective optimization problem grows exponentially with the problem size. In this case, it is not possible to compute the whole Pareto front efficiently and one is interested in good approximations. We consider how evolutionary algorithms can achieve such approximations by using different diversity mechanisms. We discuss some well-known approaches such as the density estimator and the e-dominance approach and point out how and when such mechanisms provably help to obtain good additive approximations of the Pareto-optimal set.en
dc.language.isoende
dc.relation.ispartofseriesReihe CI; 257-08de
dc.subject.ddc004de
dc.titleAdditive approximations of pareto-optimal sets by evolutionary multi-objective algorithmsen
dc.typeTextde
dc.type.publicationtypereportde
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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