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dc.contributor.authorMazumdar, Atanu
dc.contributor.authorChugh, Tinkle
dc.contributor.authorHakanen, Jussi
dc.contributor.authorMiettinen, Kaisa
dc.contributor.editorFilipic, Bogdan
dc.contributor.editorMinisci, Edmondo
dc.contributor.editorVasilei, Massimiliano
dc.date.accessioned2020-11-26T11:12:26Z
dc.date.available2020-11-26T11:12:26Z
dc.date.issued2020
dc.identifier.citationMazumdar, A., Chugh, T., Hakanen, J., & Miettinen, K. (2020). An Interactive Framework for Offline Data-Driven Multiobjective Optimization. In B. Filipic, E. Minisci, & M. Vasilei (Eds.), <i>BIOMA 2020 : 9th International Conference on Bioinspired Optimization Methods and Their Applications, Proceedings</i> (pp. 97-109). Springer. Lecture Notes in Computer Science, 12438. <a href="https://doi.org/10.1007/978-3-030-63710-1_8" target="_blank">https://doi.org/10.1007/978-3-030-63710-1_8</a>
dc.identifier.otherCONVID_47115709
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72822
dc.description.abstractWe propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to enable optimization, which introduces uncertainty. The framework incorporates preference information from a decision maker in two aspects to direct the solution process. Firstly, the decision maker can guide the optimization by providing preferences for objectives. Secondly, the framework features a novel technique for the decision maker to also express preferences related to maximum acceptable uncertainty in the solutions as preferred ranges of uncertainty. In this way, the decision maker can understand what uncertainty in solutions means and utilize this information for better decision making. We aim at keeping the cognitive load on the decision maker low and propose an interactive visualization that enables the decision maker to make decisions based on uncertainty. The interactive framework utilizes decomposition-based multiobjective evolutionary algorithms and can be extended to handle different types of preferences for objectives. Finally, we demonstrate the framework by solving a practical optimization problem with ten objectives.en
dc.format.extent322
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofBIOMA 2020 : 9th International Conference on Bioinspired Optimization Methods and Their Applications, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.otherdecision support
dc.subject.otherdecision making
dc.subject.otherdecomposition-based MOEA
dc.subject.othermetamodelling
dc.subject.othersurrogate
dc.subject.otherKriging
dc.subject.otherGaussian processes
dc.titleAn Interactive Framework for Offline Data-Driven Multiobjective Optimization
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202011266788
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMultiobjective Optimization Groupfi
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiainePäätöksen teko monitavoitteisestifi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineMultiobjective Optimization Groupen
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineDecision analytics utilizing causal models and multiobjective optimizationen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-3-030-63709-5
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange97-109
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© Springer Nature Switzerland AG 2020
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Bioinspired Optimization Methods and their Applications
dc.relation.grantnumber311877
dc.subject.ysopäätöksentukijärjestelmät
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysogaussiset prosessit
dc.subject.ysokriging-menetelmä
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p27803
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p38750
jyx.subject.urihttp://www.yso.fi/onto/yso/p3126
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-030-63710-1_8
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramProfilointi, SAfi
jyx.fundinginformationThis research was partly supported by the Academy of Finland (grant number 311877) and is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, http://www.jyu.fi/demo) of the University of Jyväskylä. This work was partially supported by the Natural Environment Research Council [NE/P017436/1].
dc.type.okmA4


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