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dc.contributor.authorAfsar, Bekir
dc.contributor.authorFieldsend, Jonathan E.
dc.contributor.authorGuerreiro, Andreia P.
dc.contributor.authorMiettinen, Kaisa
dc.contributor.authorRojas Gonzalez, Sebastian
dc.contributor.authorSato, Hiroyuki
dc.contributor.editorBrockhoff, Dimo
dc.contributor.editorEmmerich, Michael
dc.contributor.editorNaujoks, Boris
dc.contributor.editorPurshouse, Robin
dc.date.accessioned2023-09-14T08:53:48Z
dc.date.available2023-09-14T08:53:48Z
dc.date.issued2023
dc.identifier.citationAfsar, B., Fieldsend, J. E., Guerreiro, A. P., Miettinen, K., Rojas Gonzalez, S., & Sato, H. (2023). Many-Objective Quality Measures. In D. Brockhoff, M. Emmerich, B. Naujoks, & R. Purshouse (Eds.), <i>Many-Criteria Optimization and Decision Analysis : State-of-the-Art, Present Challenges, and Future Perspective</i> (pp. 113-148). Springer. Natural Computing Series. <a href="https://doi.org/10.1007/978-3-031-25263-1_5" target="_blank">https://doi.org/10.1007/978-3-031-25263-1_5</a>
dc.identifier.otherCONVID_184269258
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/89083
dc.description.abstractA key concern when undertaking any form of optimisation is how to characterise the quality of the putative solution returned. In many-objective optimisation an added complication is that such measures are on a set of trade-off solutions. We present and discuss the commonly used quality measures for many-objective optimisation, which are a subset of those used in multi-objective optimisation. We discuss the computational aspects and theoretical properties of these measures, highlighting measures for both a posteriori and a priori approaches, where the latter incorporate preference information from a decision maker (DM). We also discuss open areas in this field and forms of many-objective optimisation which are relatively under-explored, and where appropriate quality measures are much less developed including challenges related to developing measures for interactive methods.en
dc.format.extent360
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofMany-Criteria Optimization and Decision Analysis : State-of-the-Art, Present Challenges, and Future Perspective
dc.relation.ispartofseriesNatural Computing Series
dc.rightsIn Copyright
dc.titleMany-Objective Quality Measures
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-202309145104
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
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/BookItem
dc.relation.isbn978-3-031-25262-4
dc.type.coarhttp://purl.org/coar/resource_type/c_3248
dc.description.reviewstatuspeerReviewed
dc.format.pagerange113-148
dc.relation.issn1619-7127
dc.type.versionacceptedVersion
dc.rights.copyright© 2023 Springer Nature Switzerland AG
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber322221
dc.relation.grantnumber311877
dc.subject.ysopäätöksenteko
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysolaatu
dc.subject.ysoongelmanratkaisu
dc.subject.ysotoimenpiteet
dc.subject.ysomittarit (mittaus)
dc.subject.ysokehittäminen
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/p5029
jyx.subject.urihttp://www.yso.fi/onto/yso/p6006
jyx.subject.urihttp://www.yso.fi/onto/yso/p15923
jyx.subject.urihttp://www.yso.fi/onto/yso/p21210
jyx.subject.urihttp://www.yso.fi/onto/yso/p4230
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-031-25263-1_5
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundinginformationThis work was initiated during the MACODA: Many Criteria Optimisation and Decision Analysis Workshop at the Lorentz Center (Leiden, The Netherlands), 2019. We are grateful to the other participants of the workshop and the Lorentz Center for their support. Jonathan E. Fieldsend was supported in attending the MACODA workshop by Innovate UK [grant number 104400]. Bekir Afsar’s research was funded by the Academy of Finland [grant numbers 322221 and 311877]. The research is related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO), jyu.fi/demo, at the University of Jyvaskyla. Andreia P. Guerreiro acknowledges the financial support by national funds through the FCT – Foundation for Science and Technology, I.P. [within the scope of the project PTDC/CCI-COM/31198/2017]. Sebastian Rojas Gonzalez was supported by the Fonds Wetenschappelijk Onderzoek – Vlaanderen, grantnumber 1216021N.
dc.type.okmA3


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