Efficient evolutionary optimization algorithm : filtered differential evolution

dc.contributor.author	Aittokoski, Timo
dc.date.accessioned	2022-02-07T09:57:52Z
dc.date.available	2022-02-07T09:57:52Z
dc.date.issued	2008
dc.identifier.isbn	978-951-39-9036-7
dc.identifier.uri	https://jyx.jyu.fi/handle/123456789/79653
dc.description.abstract	Solving many real-life engineering problems requires often global and efficient (in terms of objective function evaluations) treatment, because function values involved are produced via time consuming simulations. In this study, we consider optimization problems of this type by discussing some drawbacks of the current surrogate assisted methods and then introduce a new population based optimization algorithm, which borrows features of the well-known Differential Evolution algorithm, but improves its efficiency by filtering away ineffective trial points.	en
dc.format.mimetype	application/pdf
dc.language.iso	eng
dc.relation.ispartofseries	Jyväskylän yliopisto. Reports of the Department of Mathematical Information Technology. Series B. Scientific computing
dc.rights	In Copyright
dc.title	Efficient evolutionary optimization algorithm : filtered differential evolution
dc.type	book
dc.identifier.urn	URN:ISBN:978-951-39-9036-7
dc.rights.accesslevel	openAccess
dc.format.content	fulltext
dc.rights.url	https://rightsstatements.org/page/InC/1.0/
dc.date.digitised	2022

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