Runtime analyses for using fairness in evolutionary multi-objective optimization

dc.contributor.author	Friedrich, Tobias	de
dc.contributor.author	Horoba, Christian	de
dc.contributor.author	Neumann, Frank	de
dc.date.accessioned	2009-05-12T16:02:08Z
dc.date.available	2009-05-12T16:02:08Z
dc.date.issued	2008-07	de
dc.description.abstract	It is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the Pareto front. In this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness introduced in [Laumanns, Thiele, Zitzler, IEEE TEC 2004]. This mechanism tries to balance the number of off-spring of all individuals in the current population. We rigorously analyze the runtime behavior of different fairness mechanisms and present showcase examples to point out situations, where the right mechanism can speed up the optimization process significantly. We also indicate drawbacks for the use of fairness by presenting instances, where the optimization process is slowed down drastically.	en
dc.identifier.uri	http://hdl.handle.net/2003/26158
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-9030
dc.language.iso	en	de
dc.relation.ispartofseries	Reihe CI; 251-08	de
dc.subject.ddc	004	de
dc.title	Runtime analyses for using fairness in evolutionary multi-objective optimization	en
dc.type	Text	de
dc.type.publicationtype	report	de
dcterms.accessRights	open access

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