Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1121
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dc.contributor.authorGuan, SU-
dc.contributor.authorChen, Q-
dc.contributor.authorMo, W-
dc.date.accessioned2007-08-06T13:34:41Z-
dc.date.available2007-08-06T13:34:41Z-
dc.date.issued2005-
dc.identifier.citationArtificial Intelligence Review 23(3): 267-293, May 2005en
dc.identifier.issn0269-2821-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/1121-
dc.description.abstractThis paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.en
dc.format.extent1458758 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherSpringeren
dc.subjectMulti-objective genetic algorithmsen
dc.subjectMulti-objective problemsen
dc.subjectMulti-objective optimizationen
dc.subjectNon-stationary environmenten
dc.titleEvolving dynamic multiple-objective optimization problems with objective replacementen
dc.typeResearch Paperen
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Electrical Engineering Research Papers

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