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dc.contributor.authorChugh, Tinkle
dc.contributor.authorAllmendinger, Richard
dc.contributor.authorOjalehto, Vesa
dc.contributor.authorMiettinen, Kaisa
dc.contributor.editorAguirre, Hernan
dc.date.accessioned2018-08-16T05:15:11Z
dc.date.available2018-08-16T05:15:11Z
dc.date.issued2018
dc.identifier.citationChugh, T., Allmendinger, R., Ojalehto, V., & Miettinen, K. (2018). Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies. In H. Aguirre (Ed.), <i>GECCO '18 : Proceedings of the Genetic and Evolutionary Computation Conference</i> (pp. 609-616). Association for Computing Machinery (ACM). <a href="https://doi.org/10.1145/3205455.3205514" target="_blank">https://doi.org/10.1145/3205455.3205514</a>
dc.identifier.otherCONVID_28184398
dc.identifier.otherTUTKAID_78383
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/59258
dc.description.abstractWe consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary algorithm for selecting training data to train surrogates and K-RVEA's approach for updating the surrogates. HK-RVEA is validated on a set of biobjective benchmark problems varying in terms of latencies and correlations between the objectives. The results are also compared to those obtained by previously proposed strategies for such problems, which were embedded in a non-surrogate-assisted evolutionary algorithm. Our experimental study shows that, under certain conditions, such as short latencies between the two objectives, HK-RVEA can outperform the existing strategies as well as an optimizer operating in an environment without latencies.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofGECCO '18 : Proceedings of the Genetic and Evolutionary Computation Conference
dc.rightsIn Copyright
dc.subject.othermetamodelling
dc.subject.othermultiobjective optimization
dc.subject.otherPareto optimality
dc.subject.otherheterogeneous objectives
dc.subject.otherBayesian optimization
dc.titleSurrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201807243633
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/ConferencePaper
dc.date.updated2018-07-24T09:15:09Z
dc.relation.isbn978-1-4503-5618-3
dc.description.reviewstatuspeerReviewed
dc.format.pagerange609-616
dc.type.versionacceptedVersion
dc.rights.copyright© 2018 Association for Computing Machinery
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceGenetic and Evolutionary Computation Conference
dc.relation.grantnumber40147/14,1570/31/201
dc.relation.grantnumber287496
dc.subject.ysokoneoppiminen
dc.subject.ysooptimointi
dc.subject.ysobayesilainen menetelmä
dc.subject.ysopareto-tehokkuus
dc.subject.ysomonitavoiteoptimointi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p28039
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1145/3205455.3205514
dc.relation.funderTEKESfi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderTEKESen
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramMuut, TEKESfi
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramOthers, TEKESen
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThis work was partly supported by Tekes, the Finnish funding agency for innovation under the FiDiPro project DeCoMo (Chugh) and the Academy of Finland, grant 287496 (Ojalehto).


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