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dc.contributor.authorIacca, Giovannien
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorCaraffini, Fabioen
dc.contributor.authorSuganthan, Ponnuthurai Nagaratnamen
dc.date.accessioned2016-03-31T10:14:15Z
dc.date.available2016-03-31T10:14:15Z
dc.date.issued2014-11
dc.identifier.citationIacca, G., Neri, F., Caraffini, F. and Suganthan, P. N. (2014) A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms. In Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers, pp. 615-626en
dc.identifier.isbn9783662455234
dc.identifier.urihttp://hdl.handle.net/2086/11741
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The publisher's final version can be found by following the DOI link.en
dc.description.abstractThe ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms.en
dc.language.isoen_USen
dc.publisherSpringer Berlin Heidelbergen
dc.relation.ispartofseriesLecture Notes in Computer Science;
dc.subjectDifferential evolutionen
dc.subjectEnsembleen
dc.subjectAdaptive algorithmsen
dc.subjectOptimisationen
dc.subjectMeta-heuristicsen
dc.subjectMemetic Computingen
dc.titleA Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithmsen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-662-45523-4_50
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.fundern/aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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