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dc.contributor.authorHakanen, Jussi
dc.contributor.authorKnowles, Joshua D.
dc.contributor.editorTrautmann, Heike
dc.contributor.editorRudolph, Günter
dc.contributor.editorKlamroth, Kathrin
dc.contributor.editorSchütze, Oliver
dc.contributor.editorWiecek, Margaret
dc.contributor.editorJin, Yaochu
dc.contributor.editorGrimme, Christian
dc.date.accessioned2017-02-28T10:24:33Z
dc.date.available2017-02-28T10:24:33Z
dc.date.issued2017
dc.identifier.citationHakanen, J., & Knowles, J. D. (2017). On Using Decision Maker Preferences with ParEGO. In H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, & C. Grimme (Eds.), <i>Evolutionary Multi-Criterion Optimization : 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings</i> (pp. 282-297). Springer International Publishing. Lecture Notes in Computer Science, 10173. <a href="https://doi.org/10.1007/978-3-319-54157-0_20" target="_blank">https://doi.org/10.1007/978-3-319-54157-0_20</a>
dc.identifier.otherCONVID_26560837
dc.identifier.otherTUTKAID_73050
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/53144
dc.description.abstractIn this paper, an interactive version of the ParEGO algorithm is introduced for identifying most preferred solutions for computationally expensive multiobjective optimization problems. It enables a decision maker to guide the search with her preferences and change them in case new insight is gained about the feasibility of the preferences. At each interaction, the decision maker is shown a subset of non-dominated solutions and she is assumed to provide her preferences in the form of preferred ranges for each objective. Internally, the algorithm samples reference points within the hyperbox defined by the preferred ranges in the objective space and uses a DACE model to approximate an achievement (scalarizing) function as a single objective to scalarize the problem. The resulting solution is then evaluated with the real objective functions and used to improve the DACE model in further iterations. The potential of the proposed algorithm is illustrated via a four-objective optimization problem related to water management with promising results.
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofEvolutionary Multi-Criterion Optimization : 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.subject.othersurrogate-based optimization
dc.subject.otherinteractive multiobjective optimization
dc.subject.otherpreference information
dc.subject.othercomputational cost
dc.subject.othervisualization
dc.titleOn Using Decision Maker Preferences with ParEGO
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201702201489
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.updated2017-02-20T13:15:16Z
dc.relation.isbn978-3-319-54156-3
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange282-297
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© 2017 Springer International Publishing AG. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Evolutionary Multi-Criterion Optimization
dc.relation.doi10.1007/978-3-319-54157-0_20
dc.type.okmA4


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