METAMODEL-ASSISTED MULTI-OBJECTIVE EVOLUTIONARY OPTIMIZATION

Karakasis, M; Giannakoglou, K

dc.contributor.author	Karakasis, M	en
dc.contributor.author	Giannakoglou, K	en
dc.date.accessioned	2014-03-01T01:54:16Z
dc.date.available	2014-03-01T01:54:16Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27282
dc.relation.uri	http://velos0.ltt.mech.ntua.gr/research/pdfs/3_075.pdf	en
dc.subject	Evaluation Model	en
dc.subject	Evolutionary Algorithm	en
dc.subject	Evolutionary Optimization	en
dc.subject	Generalization Capability	en
dc.subject	Multi Objective Evolutionary Algorithm	en
dc.subject	Multi Objective Optimization	en
dc.subject	Radial Basis Function Network	en
dc.subject	Self Organization	en
dc.title	METAMODEL-ASSISTED MULTI-OBJECTIVE EVOLUTIONARY OPTIMIZATION	en
heal.type	journalArticle	en
heal.publicationDate	2005	en
heal.abstract	The use of surrogate evaluation models or metamodels in multi-objective Evolutionary Algorithms with computationally expensive evaluations for the reduction of computational cost, through controlled approximate evaluations of generation members, is presented. The metamodels assist the Evolutionary Algorithm by filtering the poorly performing individuals within each generation and subsequently by allowing only the most promising among them to be exactly evaluated.	en