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dc.contributor.authorFriedrich, T.-
dc.contributor.authorKroeger, T.-
dc.contributor.authorNeumann, F.-
dc.contributor.editorWang, D.H.-
dc.contributor.editorReynolds, M.-
dc.identifier.citationAI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings / D. Wang and M. Reynolds (eds.): pp.291-300-
dc.description.abstractEvolutionary algorithms have been widely used to tackle multi-objective optimization problems. Incorporating preference information into the search of evolutionary algorithms for multi-objective optimization is of great importance as it allows one to focus on interesting regions in the objective space. Zitzler et al. have shown how to use a weight distribution function on the objective space to incorporate preference information into hypervolume-based algorithms. We show that this weighted information can easily be used in other popular EMO algorithms as well. Our results for NSGA-II and SPEA2 show that this yields similar results to the hypervolume approach and requires less computational effort.-
dc.description.statementofresponsibilityTobias Friedrich, Trent Kroeger, and Frank Neumann-
dc.relation.ispartofseriesLecture notes in Computer Science ; 7106-
dc.rights© Springer-Verlag Berlin Heidelberg 2011-
dc.titleWeighted preferences in evolutionary multi-objective optimization-
dc.typeConference paper-
dc.contributor.conferenceAustralasian Joint Conference on Artificial Intelligence (24th : 2011 : Perth, Western Australia)-
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]-
Appears in Collections:Aurora harvest
Computer Science publications

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