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dc.contributor.authorCaraffini, Fabioen
dc.contributor.authorKononova, A.V.en
dc.date.accessioned2018-10-23T10:40:42Z
dc.date.available2018-10-23T10:40:42Z
dc.date.issued2018-09-18
dc.identifier.citationCaraffini, F. and Kononova, A.V. (2018) Structural Bias in Differential Evolution: a preliminary study. LeGO 2018 - 14th International Workshop on Global Optimization, Leiden, The Netherlands, 18-21 September 2018.en
dc.identifier.urihttp://hdl.handle.net/2086/16803
dc.descriptionThe file attached to this record is the author's final peer reviewed version
dc.description.abstractThis paper extends the study of structural bias in popular metaheuristic global optimisation methods. Previously, it has been shown that both Genetic Algorithms and Particle Swarm Optimisation suffer from such bias. This means that difficulties already posed for a structurally biased algorithm by the fitness landscape itself are further unnecessarily exacerbated by the unexpected oversampling of some regions of the search space and avoidance of the others, to potential great detriment of the overall optimisation performance. Such bias is inherent in the core design of the algorithm. After careful examination, the authors conclude that some variants of Differential Evolution are not free of the structural bias. However, investigation suggests that the mechanisms of the formation of structural bias in Differential Evolution is different and can be balanced through a more careful design.en
dc.language.isoen_USen
dc.subjectStructural Biasen
dc.subjectDifferential Evolutionen
dc.subjectPopulation Based Algorithmsen
dc.subjectOptimisationen
dc.titleStructural Bias in Differential Evolution: a preliminary studyen
dc.typeConferenceen
dc.identifier.doihttps://dx.doi.org/10.1063/1.5089972
dc.researchgroupInstitute of Artificial Intelligence (IAI)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2018-07-21en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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