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dc.contributor.authorTari, Saraen_UK
dc.contributor.authorOchoa, Gabrielaen_UK
dc.contributor.editorChicano, Franciscoen_UK
dc.description.abstractIn local search algorithms, the pivoting rule determines which neighboring solution to select and thus strongly influences the behavior of the algorithm and its capacity to sample good-quality local optima. The classical pivoting rules are first and best improvement, with alternative rules such as worst improvement and maximum expansion recently studied on hill-climbing algorithms. This article conducts a thorough empirical comparison of five pivoting rules (best, first, worst, approximated worst and maximum expansion) on two benchmark combinatorial problems, NK landscapes and the unconstrained binary quadratic problem (UBQP), with varied sizes and ruggedness. We present both a performance analysis of the alternative pivoting rules within an iterated local search (ILS) framework and a fitness landscape analysis and visualization using local optima networks. Our results reveal that the performance of the pivoting rules within an ILS framework may differ from their performance as single climbers and that worst improvement and maximum expansion can outperform classical pivoting rules.en_UK
dc.publisherAssociation for Computing Machinery, Incen_UK
dc.relationTari S & Ochoa G (2021) Local search pivoting rules and the landscape global structure. In: Chicano F (ed.) GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, Lille, France, 10.07.2021-14.07.2021. New York: Association for Computing Machinery, Inc, pp. 278-286.
dc.rights© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO ’21, July 10–14, 2021, Lille, France 2021. ACM ISBN 978-1-4503-8350-9/21/07.
dc.subjectLocal Searchen_UK
dc.subjectIterated Local Searchen_UK
dc.subjectPivoting Rulesen_UK
dc.subjectLocal Optima Networksen_UK
dc.titleLocal search pivoting rules and the landscape global structureen_UK
dc.typeConference Paperen_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.citation.btitleGECCO '21: Proceedings of the Genetic and Evolutionary Computation Conferenceen_UK
dc.citation.conferencedates2021-07-10 - 2021-07-14en_UK
dc.citation.conferencelocationLille, Franceen_UK
dc.citation.conferencename2021 Genetic and Evolutionary Computation Conference, GECCO 2021en_UK
dc.publisher.addressNew Yorken_UK
dc.contributor.affiliationUniversity of Littoral Côte d'Opaleen_UK
dc.contributor.affiliationComputing Scienceen_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
local.rioxx.authorTari, Sara|en_UK
local.rioxx.authorOchoa, Gabriela|0000-0001-7649-5669en_UK
local.rioxx.projectInternal Project|University of Stirling|
local.rioxx.contributorChicano, Francisco|en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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