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dc.contributor.authorJiang, Shouyongen
dc.contributor.authorKaiser, Marcusen
dc.contributor.authorGuo, Jingleien
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorKrasnogor, Natalioen
dc.date.accessioned2018-05-09T13:53:23Z
dc.date.available2018-05-09T13:53:23Z
dc.date.issued2018-05-04
dc.identifier.citationJiang, S., Kaiser, M., Guo, J., Yang, S. and N. Krasnogor. N. (2018) Less detectable environmental changes in dynamic multiobjective optimisation. Proceedings of the 2018 Genetic and Evolutionary Computation Conference, 2018en
dc.identifier.urihttp://hdl.handle.net/2086/16155
dc.description.abstractMultiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of evolutionary algorithms. This is not intentionally left blank but due to the unavailability of suitable test cases to study. To bridge the gap, this work presents several scenarios where environmental changes are less likely to be detected. Our experimental studies suggest that the less detectable environments pose a big challenge to evolutionary algorithms.en
dc.language.isoen_USen
dc.publisherACM Pressen
dc.subjectLess detectable environmenten
dc.subjectEnvironmental changesen
dc.subjectDynamic multiobjective optimisationen
dc.titleLess detectable environmental changes in dynamic multiobjective optimisationen
dc.typeConferenceen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/N031962/1en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2018-03-24en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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