Single and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

Emmerich, M; Giannakoglou, K; Naujoks, B

dc.contributor.author	Emmerich, M	en
dc.contributor.author	Giannakoglou, K	en
dc.contributor.author	Naujoks, B	en
dc.date.accessioned	2014-03-01T01:55:16Z
dc.date.available	2014-03-01T01:55:16Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27659
dc.subject	Design Optimization	en
dc.subject	Evaluation Function	en
dc.subject	Evolutionary Algorithm	en
dc.subject	Evolutionary Optimization	en
dc.subject	Gaussian Random Field	en
dc.subject	Objective Function	en
dc.subject	Optimization Problem	en
dc.subject	Search Method	en
dc.title	Single and multiobjective evolutionary optimization assisted by Gaussian random field metamodels	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TEVC.2005.859463	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TEVC.2005.859463	en
heal.publicationDate	2006	en
heal.abstract	Abstract: This paper presents and analyzes in detail anefficient search method based on Evolutionary Algorithms (EA)assisted by local Gaussian Random Field Metamodels (GRFM).It is created for the use in optimization problems with computationallyexpensive evaluation function(s). The role of GRFM isto predict objective function values for new candidate solutionsby exploiting information recorded during previous evaluations.Moreover, GRFM are able to provide estimates	en
heal.journalName	IEEE Transactions on Evolutionary Computation	en
dc.identifier.doi	10.1109/TEVC.2005.859463	en