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dc.contributor.authorAghaei Pour, Pouya
dc.contributor.authorBandaru, Sunith
dc.contributor.authorAfsar, Bekir
dc.contributor.authorMiettinen, Kaisa
dc.contributor.editorFieldsend, Jonathan E.
dc.date.accessioned2022-11-02T10:06:16Z
dc.date.available2022-11-02T10:06:16Z
dc.date.issued2022
dc.identifier.citationAghaei Pour, P., Bandaru, S., Afsar, B., & Miettinen, K. (2022). Desirable properties of performance indicators for assessing interactive evolutionary multiobjective optimization methods. In J. E. Fieldsend (Ed.), <i>GECCO '22 : Proceedings of the Genetic and Evolutionary Computation Conference Companion</i> (pp. 1803-1811). ACM. <a href="https://doi.org/10.1145/3520304.3533955" target="_blank">https://doi.org/10.1145/3520304.3533955</a>
dc.identifier.otherCONVID_150896047
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/83772
dc.description.abstractInteractive methods support decision makers in finding the most preferred solution in multiobjective optimization problems. They iteratively incorporate the decision maker's preference information to find the best balance among conflicting objectives. Several interactive methods have been developed in the literature. However, choosing the most suitable interactive method for a given problem can prove challenging and appropriate indicators are needed to compare interactive methods. Some indicators exist for a priori methods, where preferences are provided at the beginning of the solution process. We present some numerical experiments that illustrate why these indicators are not suitable for interactive methods. As the main contribution of this paper, we propose a set of desirable properties of indicators for assessing interactive methods as the first step of filling a gap in the literature. We discuss each property in detail and provide simple examples to illustrate their behavior.en
dc.format.extent2345
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofGECCO '22 : Proceedings of the Genetic and Evolutionary Computation Conference Companion
dc.rightsIn Copyright
dc.subject.othermultiple criteria optimization
dc.subject.otherperformance evaluation
dc.subject.otherperformance assessment
dc.subject.otherinteractive methods
dc.subject.othermetrics
dc.subject.otherperformance
dc.subject.othermulti-criterion optimization and decision-making
dc.titleDesirable properties of performance indicators for assessing interactive evolutionary multiobjective optimization methods
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202211025077
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-1-4503-9268-6
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1803-1811
dc.type.versionacceptedVersion
dc.rights.copyright© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceGenetic and Evolutionary Computation Conference
dc.relation.grantnumber322221
dc.relation.grantnumber311877
dc.subject.ysopäätöksentukijärjestelmät
dc.subject.ysooptimointi
dc.subject.ysointeraktiivisuus
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysoindikaattorit
dc.subject.ysopäätöksenteko
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p27803
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p10823
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p8365
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1145/3520304.3533955
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 partly supported by the Academy of Finland (grant no 311877 and 322221) and is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multi-objective Optimization.


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