EVOLUTIONARY ALGORITHMS WITH SURROGATE MODELING FOR COMPUTATIONALLY EXPENSIVE OPTIMIZATION PROBLEMS

Giannakoglou, K; Karakasis, M; Kampolis, I

dc.contributor.author	Giannakoglou, K	en
dc.contributor.author	Karakasis, M	en
dc.contributor.author	Kampolis, I	en
dc.date.accessioned	2014-03-01T01:55:01Z
dc.date.available	2014-03-01T01:55:01Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27550
dc.relation.uri	http://147.102.55.162/research/pdfs/3_080.pdf	en
dc.relation.uri	http://velos0.ltt.mech.ntua.gr/research/pdfs/3_080.pdf	en
dc.subject	Approximation Error	en
dc.subject	Design Optimization	en
dc.subject	Evaluation Model	en
dc.subject	Evolutionary Algorithm	en
dc.subject	Industrial Application	en
dc.subject	Large Scale	en
dc.subject	Literature Survey	en
dc.subject	Optimization Problem	en
dc.title	EVOLUTIONARY ALGORITHMS WITH SURROGATE MODELING FOR COMPUTATIONALLY EXPENSIVE OPTIMIZATION PROBLEMS	en
heal.type	journalArticle	en
heal.publicationDate	2006	en
heal.abstract	optimization tools that utilize Evolutionary Algorithms (EAs) as the core search tool gained particular attention and reached a certain level of maturity. These tools enabled the extensive use of EAs in large-scale industrial applications, in which the analysis (evaluation) tool is computationally expensive. A literature survey reveals that the majority of new, promising variants of EAs are conceptually based on	en