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dc.contributor.authorLárraga, Giomara
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2023-09-12T10:19:16Z
dc.date.available2023-09-12T10:19:16Z
dc.date.issued2023
dc.identifier.citationLárraga, G., & Miettinen, K. (2023). Component-based thinking in designing interactive multiobjective evolutionary methods. In <i>GECCO '23 Companion : Proceedings of the Companion Conference on Genetic and Evolutionary Computation</i> (pp. 1693-1702). ACM. <a href="https://doi.org/10.1145/3583133.3596307" target="_blank">https://doi.org/10.1145/3583133.3596307</a>
dc.identifier.otherCONVID_184210361
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/89049
dc.description.abstractMultiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. They have many Pareto optimal solutions representing different trade-offs, and a decision-maker needs to find the most preferred one. Although most multiobjective evolutionary algorithms approximate the Pareto optimal set, their variants incorporate preference information to focus on a subset of solutions that interest the decision-maker. Interactive methods allow decision-makers to provide preference information iteratively during the solution process, enabling them to learn about available solutions and their preferences' feasibility. Nevertheless, most interactive evolutionary methods do not sufficiently support the decision-maker in finding the most preferred solution and may be cognitively too demanding. We propose a framework for designing and implementing interactive evolutionary methods. It contains algorithmic components based on similarities in the structure of existing preference-based evolutionary algorithms and decision-makers' needs during interaction. The components can be combined in different ways to create new interactive methods or to instantiate the existing ones. We show an example of the implementation of the proposed framework composed of three elements: a graphical user interface, a database, and a set of algorithmic components. The resulting software can be utilized to develop new methods and increase their usability in real-world applications.en
dc.format.extent2469
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofGECCO '23 Companion : Proceedings of the Companion Conference on Genetic and Evolutionary Computation
dc.rightsCC BY 4.0
dc.titleComponent-based thinking in designing interactive multiobjective evolutionary methods
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202309125072
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineMultiobjective Optimization Groupfi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiainePäätöksen teko monitavoitteisestifi
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineMultiobjective Optimization Groupen
dc.contributor.oppiaineSchool of Wellbeingen
dc.contributor.oppiaineDecision analytics utilizing causal models and multiobjective optimizationen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn979-8-4007-0120-7
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1693-1702
dc.type.versionpublishedVersion
dc.rights.copyright© 2023 Copyright held by the owner/author(s).
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceGenetic and Evolutionary Computation Conference
dc.relation.grantnumber322221
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysointeraktiivisuus
dc.subject.ysoevoluutiolaskenta
dc.subject.ysopareto-tehokkuus
dc.subject.ysopäätöksentukijärjestelmät
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p10823
jyx.subject.urihttp://www.yso.fi/onto/yso/p28071
jyx.subject.urihttp://www.yso.fi/onto/yso/p28039
jyx.subject.urihttp://www.yso.fi/onto/yso/p27803
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1145/3583133.3596307
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis research was supported by the Academy of Finland (grant number 322221). 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
dc.type.okmA4


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