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dc.contributor.authorBen-Romdhane, Hajer
dc.contributor.authorKrichen, Saoussen
dc.contributor.authorAlba, Enrique
dc.date.accessioned2017-05-17T12:58:10Z
dc.date.available2017-05-17T12:58:10Z
dc.date.issued2017
dc.identifier.citationJournal of Experimental & Theoretical Artificial Intelligence. 2017, vol. 29, issue 3, p. 453-479.cs
dc.identifier.issn0952-813X
dc.identifier.issn1362-3079
dc.identifier.urihttp://hdl.handle.net/10084/117066
dc.description.abstractOptimisation in changing environments is a challenging research topic since many real-world problems are inherently dynamic. Inspired by the natural evolution process, evolutionary algorithms (EAs) are among the most successful and promising approaches that have addressed dynamic optimisation problems. However, managing the exploration/exploitation trade-off in EAs is still a prevalent issue, and this is due to the difficulties associated with the control and measurement of such a behaviour. The proposal of this paper is to achieve a balance between exploration and exploitation in an explicit manner. The idea is to use two equally sized populations: the first one performs exploration while the second one is responsible for exploitation. These tasks are alternated from one generation to the next one in a regular pattern, so as to obtain a balanced search engine. Besides, we reinforce the ability of our algorithm to quickly adapt after cnhanges by means of a memory of past solutions. Such a combination aims to restrain the premature convergence, to broaden the search area, and to speed up the optimisation. We show through computational experiments, and based on a series of dynamic problems and many performance measures, that our approach improves the performance of EAs and outperforms competing algorithms.cs
dc.language.isoencs
dc.publisherTaylor & Franciscs
dc.relation.ispartofseriesJournal of Experimental & Theoretical Artificial Intelligencecs
dc.relation.urihttp://dx.doi.org/10.1080/0952813X.2016.1186230cs
dc.subjectdynamic optimisation problemscs
dc.subjectevolutionary algorithmscs
dc.subjectexploration-exploitation tradeoffcs
dc.subjectmulti-population schemecs
dc.subjectmemory schemecs
dc.titleA bi-population based scheme for an explicit exploration/exploitation trade-off in dynamic environmentscs
dc.typearticlecs
dc.identifier.doi10.1080/0952813X.2016.1186230
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume29cs
dc.description.issue3cs
dc.description.lastpage479cs
dc.description.firstpage453cs
dc.identifier.wos000399620500001


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