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dc.contributor.authorRuiz, Ana B.
dc.contributor.authorLuque, Mariano
dc.contributor.authorMiettinen, Kaisa
dc.contributor.authorSaborido, Rubén
dc.contributor.editorGaspar-Cunha, António
dc.contributor.editorAntunes, Carlos Henggeler
dc.contributor.editorCoello, Carlos Coello
dc.date.accessioned2018-03-07T09:40:00Z
dc.date.available2018-03-07T09:40:00Z
dc.date.issued2015
dc.identifier.citationRuiz, A. B., Luque, M., Miettinen, K., & Saborido, R. (2015). An Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA. In A. Gaspar-Cunha, C. H. Antunes, & C. C. Coello (Eds.), <i>Evolutionary Multi-Criterion Optimization : 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part II</i> (pp. 249-263). Springer. Lecture Notes in Computer Science, 9019. <a href="https://doi.org/10.1007/978-3-319-15892-1_17" target="_blank">https://doi.org/10.1007/978-3-319-15892-1_17</a>
dc.identifier.otherCONVID_24644711
dc.identifier.otherTUTKAID_65783
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57264
dc.description.abstractIn this paper, we describe an interactive evolutionary algorithm called Interactive WASF-GA to solve multiobjective optimization problems. This algorithm is based on a preference-based evolutionary multiobjective optimization algorithm called WASF-GA. In Interactive WASF-GA, a decision maker (DM) provides preference information at each iteration simple as a reference point consisting of desirable objective function values and the number of solutions to be compared. Using this information, the desired number of solutions are generated to represent the region of interest of the Pareto optimal front associated to the reference point given. Interactive WASF-GA implies a much lower computational cost than the original WASF-GA because it generates a small number of solutions. This speeds up the convergence of the algorithm, making it suitable for many decision-making problems. Its e ciency and usefulness is demonstrated with a ve-objective optimization problem.
dc.format.extent591
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofEvolutionary Multi-Criterion Optimization : 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part II
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.subject.otherEvolutionary algorithms
dc.subject.otherInteractive methods
dc.subject.otherMultiobjective programming
dc.subject.otherPareto optimal solutions
dc.subject.otherReference point approach
dc.titleAn Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201803071668
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2018-03-07T07:15:03Z
dc.relation.isbn978-3-319-15891-4
dc.description.reviewstatuspeerReviewed
dc.format.pagerange249-263
dc.relation.issn0302-9743
dc.type.versionacceptedVersion
dc.rights.copyright© Springer International 2015. This is a final draft version of an article whose final and definitive form has been published by Springer International. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference on Evolutionary Multi-Criterion Optimization
dc.relation.doi10.1007/978-3-319-15892-1_17


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