Single-objective and multiobjective evolutionary optimization assisted by Gaussian random field metamodels

Emmerich, MTM; Giannakoglou, KC; Naujoks, B

dc.contributor.author	Emmerich, MTM	en
dc.contributor.author	Giannakoglou, KC	en
dc.contributor.author	Naujoks, B	en
dc.date.accessioned	2014-03-01T01:55:42Z
dc.date.available	2014-03-01T01:55:42Z
dc.date.issued	2006	en
dc.identifier.issn	1089-778X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27810
dc.subject	evolutionary optimization	en
dc.subject	Gaussian random field models	en
dc.subject	Kriging	en
dc.subject	metamodeling	en
dc.subject	multiobjective design optimization	en
dc.subject	uncertainty prediction	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	ALGORITHMS	en
dc.subject.other	MODELS	en
dc.title	Single-objective and multiobjective evolutionary optimization assisted by Gaussian random field metamodels	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	This paper presents and analyzes in detail an efficient 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 one (or many) computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information recorded during previous evaluations. Moreover, GRIM are able to provide estimates of the confidence of their predictions. Predictions and their confidence intervals predicted by GRFM are used by the metamodel assisted EA. It selects the promising members in each generation and carries out exact, costly evaluations only for them. The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA. This is adequately demonstrated in this paper by means of mathematical test cases and a multipoint air-foil design in aerodynamics.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION	en
dc.identifier.isi	ISI:000239475600005	en
dc.identifier.volume	10	en
dc.identifier.issue	4	en
dc.identifier.spage	421	en
dc.identifier.epage	439	en