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dc.contributor.authorChugh, Tinkle
dc.contributor.authorJin, Yaochu
dc.contributor.authorMiettinen, Kaisa
dc.contributor.authorHakanen, Jussi
dc.contributor.authorSindhya, Karthik
dc.date.accessioned2018-02-07T06:39:18Z
dc.date.available2018-02-07T06:39:18Z
dc.date.issued2018
dc.identifier.citationChugh, T., Jin, Y., Miettinen, K., Hakanen, J., & Sindhya, K. (2018). A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization. <i>IEEE Transactions on Evolutionary Computation</i>, <i>22</i>(1), 129-142. <a href="https://doi.org/10.1109/TEVC.2016.2622301" target="_blank">https://doi.org/10.1109/TEVC.2016.2622301</a>
dc.identifier.otherCONVID_26550136
dc.identifier.otherTUTKAID_72995
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57019
dc.description.abstractWe propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogate-assisted EA (SAEA) uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the reference vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art SAEAs on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.ispartofseriesIEEE Transactions on Evolutionary Computation
dc.subject.othermultiobjective optimization
dc.subject.otherreference vectors
dc.subject.othersurrogate-assisted evolutionary algorithms
dc.subject.othermodel management
dc.subject.otherKriging
dc.subject.othercomputational cost
dc.subject.otherPareto optimality
dc.subject.otherBayesian optimization
dc.titleA Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201802061435
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/JournalArticle
dc.date.updated2018-02-06T13:15:17Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange129-142
dc.relation.issn1089-778X
dc.relation.numberinseries1
dc.relation.volume22
dc.type.versionacceptedVersion
dc.rights.copyright© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accesslevelopenAccessfi
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysovektorit (matematiikka)
dc.subject.ysoalgoritmit
dc.subject.ysopareto-tehokkuus
dc.subject.ysobayesilainen menetelmä
dc.subject.ysopäätöksenteko
dc.subject.ysokoneoppiminen
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p12298
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p28039
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.relation.doi10.1109/TEVC.2016.2622301
dc.type.okmA1


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