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dc.contributor.authorChugh, Tinkle
dc.contributor.authorSun, Chaoli
dc.contributor.authorWang, Handing
dc.contributor.authorJin, Yaochu
dc.contributor.editorBartz-Beielstein, Thomas
dc.contributor.editorFilipič, Bogdan
dc.contributor.editorKorošec, Peter
dc.contributor.editorTalbi, El-Ghazali
dc.date.accessioned2019-06-19T09:18:10Z
dc.date.available2020-03-03T22:35:34Z
dc.date.issued2020
dc.identifier.citationChugh, T., Sun, C., Wang, H., & Jin, Y. (2020). Surrogate-Assisted Evolutionary Optimization of Large Problems. In T. Bartz-Beielstein, B. Filipič, P. Korošec, & E.-G. Talbi (Eds.), <i>High-Performance Simulation-Based Optimization</i> (pp. 165-187). Springer. Studies in Computational Intelligence, 833. <a href="https://doi.org/10.1007/978-3-030-18764-4_8" target="_blank">https://doi.org/10.1007/978-3-030-18764-4_8</a>
dc.identifier.otherCONVID_31219792
dc.identifier.otherTUTKAID_81691
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/64715
dc.description.abstractThis chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is large, or both. These problems pose challenges to evolutionary algorithms themselves, constructing surrogates and surrogate management. To address these challenges, we proposed two algorithms, one called kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) for many-objective optimization, and the other called cooperative swarm optimization algorithm (SA-COSO) for high-dimensional single-objective optimization. Empirical studies demonstrate that K-RVEA works well for many-objective problems having up to ten objectives, while SA-COSA outperforms the state-of-the-art algorithms on 200-dimensional single-objective test problems.fi
dc.format.extent291
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofHigh-Performance Simulation-Based Optimization
dc.relation.ispartofseriesStudies in Computational Intelligence
dc.rightsIn Copyright
dc.subject.otheroptimointifi
dc.subject.othermatemaattinen optimointifi
dc.subject.otherevoluutiolaskentafi
dc.subject.otheroptimisationfi
dc.subject.othermathematical optimisationfi
dc.subject.otherevolutionary computationfi
dc.titleSurrogate-Assisted Evolutionary Optimization of Large Problems
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-201906173258
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/BookItem
dc.date.updated2019-06-17T12:15:33Z
dc.relation.isbn978-3-030-18763-7
dc.description.reviewstatuspeerReviewed
dc.format.pagerange165-187
dc.relation.issn1860-949X
dc.relation.numberinseries833
dc.type.versionacceptedVersion
dc.rights.copyright© Springer Nature Switzerland AG 2020.
dc.rights.accesslevelopenAccessfi
dc.subject.ysooptimointi
dc.subject.ysomatemaattinen optimointi
dc.subject.ysoevoluutiolaskenta
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p17635
jyx.subject.urihttp://www.yso.fi/onto/yso/p28071
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-030-18764-4_8


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