Näytä suppeat kuvailutiedot

dc.contributor.authorBarba-González, Cristóbal
dc.contributor.authorOjalehto, Vesa
dc.contributor.authorGarcía-Nieto, José
dc.contributor.authorNebro, Antonio J.
dc.contributor.authorMiettinen, Kaisa
dc.contributor.authorAldana-Montes, José F.
dc.contributor.editorAuger, Anne
dc.contributor.editorFonseca, Carlos M.
dc.contributor.editorLourenço, Nuno
dc.contributor.editorMachado, Penousal
dc.contributor.editorPaquete, Luís
dc.contributor.editorWhitley, Darrell
dc.date.accessioned2018-09-13T07:34:20Z
dc.date.available2018-09-13T07:34:20Z
dc.date.issued2018
dc.identifier.citationBarba-González, C., Ojalehto, V., García-Nieto, J., Nebro, A. J., Miettinen, K., & Aldana-Montes, J. F. (2018). Artificial Decision Maker Driven by PSO : An Approach for Testing Reference Point Based Interactive Methods. In A. Auger, C. M. Fonseca, N. Lourenço, P. Machado, L. Paquete, & D. Whitley (Eds.), <i>Parallel Problem Solving from Nature - PPSN XV : 15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part 1</i> (pp. 274-285). Springer International Publishing. Lecture Notes in Computer Science, 11101. <a href="https://doi.org/10.1007/978-3-319-99253-2_22" target="_blank">https://doi.org/10.1007/978-3-319-99253-2_22</a>
dc.identifier.otherCONVID_28240718
dc.identifier.otherTUTKAID_78693
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/59501
dc.description.abstractOver the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization for guiding the generation of consecutive reference points, hence, replacing the decision maker in preference articulation. We use the artificial decision maker to compare interactive methods. We demonstrate the artificial decision maker using the DTLZ benchmark problems with 3, 5 and 7 objectives to compare R-NSGA-II and WASF-GA as interactive methods. The experimental results show that the proposed artificial decision maker is useful and efficient. It offers an intuitive and flexible mechanism to capture the current context when testing interactive methods for decision making.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofParallel Problem Solving from Nature - PPSN XV : 15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part 1
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsIn Copyright
dc.subject.othermultiobjective optimization
dc.subject.otherpreference articulation
dc.subject.othermultiple criteria decision making
dc.subject.otherparticle swarm optimization
dc.titleArtificial Decision Maker Driven by PSO : An Approach for Testing Reference Point Based Interactive Methods
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201809124091
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2018-09-12T15:15:05Z
dc.relation.isbn978-3-319-99252-5
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange274-285
dc.relation.issn0302-9743
dc.relation.numberinseries11101
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International Publishing AG, part of Springer Nature 2018.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Parallel Problem Solving From Nature
dc.relation.grantnumber287496
dc.subject.ysopäätöksenteko
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysooptimointi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p8743
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-319-99253-2_22
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationThis work was partially funded by Grants TIN2017-86049-R (Spanish MICINN) and P12-TIC-1519 (PAIDI). C. Barba-González was supported by Grant BES-2015-072209 (Spanish MICINN) and University of Jyväskylä. J. García-Nieto is the recipient Post-Doct fellowship of “Plan Propio” at Universidad de Málaga. This work was supported on the part of V. Ojalehto by the Academy of Finland (grant number 287496).
dc.type.okmA4


Aineistoon kuuluvat tiedostot

Thumbnail

Aineisto kuuluu seuraaviin kokoelmiin

Näytä suppeat kuvailutiedot

In Copyright
Ellei muuten mainita, aineiston lisenssi on In Copyright