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dc.contributor.authorSaini, Bhupinder Singh
dc.contributor.authorHakanen, Jussi
dc.contributor.authorMiettinen, Kaisa
dc.contributor.editorBäck, Thomas
dc.contributor.editorPreuss, Mike
dc.contributor.editorDeutz, André
dc.contributor.editorWang, Hao
dc.contributor.editorDoerr, Carola
dc.contributor.editorEmmerich, Michael
dc.contributor.editorTrautmann, Heike
dc.date.accessioned2020-09-07T04:40:39Z
dc.date.available2020-09-07T04:40:39Z
dc.date.issued2020
dc.identifier.citationSaini, B. S., Hakanen, J., & Miettinen, K. (2020). A New Paradigm in Interactive Evolutionary Multiobjective Optimization. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), <i>PPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature</i> (pp. 243-256). Springer. Lecture Notes in Computer Science, 12270. <a href="https://doi.org/10.1007/978-3-030-58115-2_17" target="_blank">https://doi.org/10.1007/978-3-030-58115-2_17</a>
dc.identifier.otherCONVID_36258380
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/71637
dc.description.abstractOver the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving multiobjective optimization problems in an interactive manner by using multiple scalarization functions to map vectors in the objective space to a new, so-called preference incorporated space (PIS). In this way, the original problem is converted into a new multiobjective optimization problem with typically fewer objectives in the PIS. This mapping enables a modular incorporation of decision maker’s preferences to convert any evolutionary algorithm to an interactive one, where preference information is directing the solution process. Advantages of optimizing in this new space are discussed and the idea is demonstrated with two interactive evolutionary algorithms: IOPIS/RVEA and IOPIS/NSGA-III. According to the experiments conducted, the new algorithms provide solutions that are better in quality as compared to those of state-of-the-art evolutionary algorithms and their variants where preference information is incorporated in the original objective space. Furthermore, the promising results require fewer function evaluations.en
dc.format.extent717
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofPPSN 2020 : 16th International Conference on Parallel Problem Solving from Nature
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.otherinteractive methods
dc.subject.otherachievement scalarizing functions
dc.subject.otherevolutionary algorithms
dc.subject.otherpreference information
dc.subject.otherdecision maker
dc.titleA New Paradigm in Interactive Evolutionary Multiobjective Optimization
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202009075746
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.relation.isbn978-3-030-58114-5
dc.description.reviewstatuspeerReviewed
dc.format.pagerange243-256
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© Springer Nature Switzerland AG 2020
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Parallel Problem Solving From Nature
dc.relation.grantnumber322221
dc.relation.grantnumber311877
dc.subject.ysooptimointi
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysoalgoritmit
dc.subject.ysopäätöksentukijärjestelmät
dc.subject.ysoevoluutiolaskenta
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p27803
jyx.subject.urihttp://www.yso.fi/onto/yso/p28071
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-030-58115-2_17
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationThis research was supported by the Academy of Finland (grant numbers 322221 and 311877). The research is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä.


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