Show simple item record

dc.contributor.authorLárraga, Giomara
dc.contributor.authorMiettinen, Kaisa
dc.contributor.editorRudolph, Günter
dc.contributor.editorKononova, Anna V.
dc.contributor.editorAguirre, Hernán
dc.contributor.editorKerschke, Pascal
dc.contributor.editorOchoa, Gabriela
dc.contributor.editorTušar, Tea
dc.date.accessioned2022-09-29T11:06:50Z
dc.date.available2022-09-29T11:06:50Z
dc.date.issued2022
dc.identifier.citationLárraga, G., & Miettinen, K. (2022). A General Architecture for Generating Interactive Decomposition-Based MOEAs. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), <i>Parallel Problem Solving from Nature – PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II</i> (pp. 81-95). Springer International Publishing. Lecture Notes in Computer Science, 13398. <a href="https://doi.org/10.1007/978-3-031-14721-0_6" target="_blank">https://doi.org/10.1007/978-3-031-14721-0_6</a>
dc.identifier.otherCONVID_151666606
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/83384
dc.description.abstractEvolutionary algorithms have been widely applied for solving multiobjective optimization problems. Such methods can approximate many Pareto optimal solutions in a population. However, when solving real-world problems, a decision maker is usually involved, who may only be interested in a subset of solutions that meet their preferences. Several methods have been proposed to consider preference information during the solution process. Among them, interactive methods support the decision maker in learning about the trade-offs among objectives and the feasibility of solutions. Also, such methods allow the decision maker to provide preference information iteratively. Typically, interactive multiobjective evolutionary algorithms are modifications of existing a priori or a posteriori algorithms. However, they mainly focus on finding a region of interest and do not support the decision maker finding the most preferred solution. In addition, the cognitive load imposed on the decision maker is usually not considered. This article proposes an architecture for developing interactive decomposition-based evolutionary algorithms that can support the decision maker during the solution process. The proposed architecture aims to improve the applicability of interactive methods in solving real-world problems by considering the needs of a decision maker. We apply our proposal to generate an interactive decomposition-based algorithm utilizing a reference vector re-arrangement procedure and MOEA/D. We demonstrate the performance of our proposal with a real-world problem and multiple benchmark problems.en
dc.format.extent619
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofParallel Problem Solving from Nature – PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.othermultiobjective optimization
dc.subject.otherevolutionary algorithms
dc.subject.otherpreference information
dc.subject.otherdecision making
dc.subject.otherinteractive methods
dc.subject.otherinteractive preference incorporation
dc.titleA General Architecture for Generating Interactive Decomposition-Based MOEAs
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202209294737
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.isbn978-3-031-14720-3
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange81-95
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© 2022 the Authors
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Parallel Problem Solving From Nature
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysooptimointi
dc.subject.ysointeraktiivisuus
dc.subject.ysopäätöksentukijärjestelmät
dc.subject.ysoevoluutiolaskenta
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
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/p27803
jyx.subject.urihttp://www.yso.fi/onto/yso/p28071
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-031-14721-0_6
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