Show simple item record

dc.contributor.authorLárraga, Giomara
dc.contributor.authorSaini, Bhupinder Singh
dc.contributor.authorMiettinen, Kaisa
dc.contributor.editorEmmerich, Michael
dc.contributor.editorDeutz, André
dc.contributor.editorWang, Hao
dc.contributor.editorKononova, Anna V.
dc.contributor.editorNaujoks, Boris
dc.contributor.editorLi, Ke
dc.contributor.editorMiettinen, Kaisa
dc.contributor.editorYevseyeva, Iryna
dc.date.accessioned2023-06-07T12:15:27Z
dc.date.available2023-06-07T12:15:27Z
dc.date.issued2023
dc.identifier.citationLárraga, G., Saini, B. S., & Miettinen, K. (2023). Incorporating Preference Information Interactively in NSGA-III by the Adaptation of Reference Vectors. In M. Emmerich, A. Deutz, H. Wang, A. V. Kononova, B. Naujoks, K. Li, K. Miettinen, & I. Yevseyeva (Eds.), <i>Evolutionary Multi-Criterion Optimization : 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20–24, 2023, Proceedings</i> (pp. 578-592). Springer. Lecture Notes in Computer Science, 13970. <a href="https://doi.org/10.1007/978-3-031-27250-9_41" target="_blank">https://doi.org/10.1007/978-3-031-27250-9_41</a>
dc.identifier.otherCONVID_178489266
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/87520
dc.description.abstractReal-world multiobjective optimization problems involve decision makers interested in a subset of solutions that meet their preferences. Decomposition-based multiobjective evolutionary algorithms (or MOEAs) have gained the research community’s attention because of their good performance in problems with many objectives. Some efforts have been made to propose variants of these methods that incorporate the decision maker’s preferences, directing the search toward regions of interest. Typically, such variants adapt the reference vectors according to the decision maker’s preferences. However, most of them can consider a single type of preference, the most common being reference points. Interactive MOEAs aim to let decision-makers provide preference information progressively, allowing them to learn about the trade-offs between objectives in each iteration. In such methods, decision makers can provide preferences in multiple ways, and it is desirable to allow them to select the type of preference for each iteration according to their knowledge. This article compares three interactive versions of NSGA-III utilizing multiple types of preferences. The first version incorporates a mechanism that adapts the reference vectors differently according to the type of preferences. The other two versions convert the preferences from the type selected by the decision maker to reference points, which are then utilized in two different reference vector adaptation techniques that have been used in a priori MOEAs. According to the results, we identify the advantages and drawbacks of the compared methods.en
dc.format.extent636
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofEvolutionary Multi-Criterion Optimization : 12th International Conference, EMO 2023, Leiden, The Netherlands, March 20–24, 2023, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.othermultiobjective optimization
dc.subject.otherinteractive methods
dc.subject.otherdecision making
dc.subject.othermultiobjective evolutionary algorithms
dc.subject.otherdecomposition-based MOEAs
dc.subject.otherNSGA-III
dc.titleIncorporating Preference Information Interactively in NSGA-III by the Adaptation of Reference Vectors
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202306073589
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineMultiobjective Optimization Groupfi
dc.contributor.oppiainePäätöksen teko monitavoitteisestifi
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineMultiobjective Optimization Groupen
dc.contributor.oppiaineDecision analytics utilizing causal models and multiobjective optimizationen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-3-031-27249-3
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange578-592
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© 2023 the Authors
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Evolutionary Multi-Criterion Optimization
dc.relation.grantnumber322221
dc.subject.ysopäätöksenteko
dc.subject.ysointeraktiivisuus
dc.subject.ysopäätöksentukijärjestelmät
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysoevoluutiolaskenta
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
jyx.subject.urihttp://www.yso.fi/onto/yso/p10823
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/p28071
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-031-27250-9_41
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


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

In Copyright
Except where otherwise noted, this item's license is described as In Copyright