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dc.contributor.authorFieldsend, Jonathan E.
dc.contributor.authorChugh, Tinkle
dc.contributor.authorAllmendinger, Richard
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2021-05-28T06:25:18Z
dc.date.available2021-05-28T06:25:18Z
dc.date.issued2022
dc.identifier.citationFieldsend, J. E., Chugh, T., Allmendinger, R., & Miettinen, K. (2022). A Visualizable Test Problem Generator for Many-Objective Optimization. <i>IEEE Transactions on Evolutionary Computation</i>, <i>26</i>(1), 1-11. <a href="https://doi.org/10.1109/TEVC.2021.3084119" target="_blank">https://doi.org/10.1109/TEVC.2021.3084119</a>
dc.identifier.otherCONVID_89720117
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/76011
dc.description.abstractVisualizing the search behavior of a series of points or populations in their native domain is critical in understanding biases and attractors in an optimization process. Distancebased many-objective optimization test problems have been developed to facilitate visualization of search behavior in a two-dimensional design space with arbitrarily many objective functions. Previous works have proposed a few commonly seen problem characteristics into this problem framework, such as the definition of disconnected Pareto sets and dominance resistant regions of the design space. The authors’ previous work has advanced this research further by providing a problem generator to automatically create user-defined problem instances featuring any combination of these problem features as well as newly introduced ones, such as landscape discontinuities, varying objective ranges, and neutrality. This work makes a number of additional contributions including the proposal of an enhanced, open-source feature-rich problem generator that can create user-defined problem instances exhibiting a range of problem features – some of which are newly introduced here or form extensions of existing features. A comprehensive validation of the problem generator is also provided using popular multiobjective optimization algorithms, and some problem generator settings to create instances exhibiting different challenges for an optimizer are identified.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Transactions on Evolutionary Computation
dc.rightsIn Copyright
dc.subject.othermulti-objective test problems
dc.subject.otherevolutionary optimization
dc.subject.otherbenchmarking
dc.subject.othertest suite
dc.subject.othervisualization
dc.titleA Visualizable Test Problem Generator for Many-Objective Optimization
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202105283258
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1-11
dc.relation.issn1089-778X
dc.relation.numberinseries1
dc.relation.volume26
dc.type.versionacceptedVersion
dc.rights.copyright© 2021 IEEE
dc.rights.accesslevelopenAccessfi
dc.subject.ysoavoin lähdekoodi
dc.subject.ysooptimointi
dc.subject.ysoongelmanratkaisu
dc.subject.ysomonitavoiteoptimointi
dc.subject.ysovisualisointi
dc.subject.ysobenchmarking
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p17089
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p6006
jyx.subject.urihttp://www.yso.fi/onto/yso/p32016
jyx.subject.urihttp://www.yso.fi/onto/yso/p7938
jyx.subject.urihttp://www.yso.fi/onto/yso/p9747
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/TEVC.2021.3084119
jyx.fundinginformationThis work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]. This research is related to the thematic research area DEMO (jyu.fi/demo) of the University of Jyväskylä.
dc.type.okmA1


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