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dc.contributor.authorBurke, Edmund-
dc.contributor.authorGendreau, Michel-
dc.contributor.authorOchoa, Gabriela-
dc.contributor.authorWalker, James-
dc.contributor.editorKrasnogor, N-
dc.contributor.editorLanzi, PL-
dc.description.abstractWe propose two adaptive variants of a multiple neighborhood iterated local search algorithm. These variants employ online learning techniques, also called adaptive operation selection, in order to select which perturbation to apply at each iteration step from a set of available move operators. Using a common software interface (the HyFlex framework), the proposed algorithms are tested across four hard combinatorial optimisation problems: permutation flow shop, 1D bin packing, maximum satisfiability, and personnel scheduling (including instance data from real-world industrial applications). Using the HyFlex framework, exactly the same high level search strategy can be applied to all the domains and instances. Our results confirm that the adaptive variants outperform a baseline iterated local search with uniform random selection of the move operators. We argue that the adaptive algorithms proposed are general yet powerful, and contribute to the goal of increasing the generality and applicability of heuristic search.en_UK
dc.relationBurke E, Gendreau M, Ochoa G & Walker J (2011) Adaptive iterated local search for cross-domain optimisation In: Krasnogor N, Lanzi PL (ed.) GECCO'11 Proceedings of the 13th annual conference on Genetic and Evolutionary Computation, New York, NY: ACM. 13th annual conference on Genetic and evolutionary computation, 12.7.2011 - 16.7.2011, Dublin, Ireland, pp. 1987-1994.-
dc.rightsThe publisher has not yet responded to our queries therefore this work cannot be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.-
dc.subject.lcshComputation by Abstract Devices-
dc.subject.lcshAlgorithm Analysis and Problem Complexity-
dc.subject.lcshArtificial Intelligence (incl. Robotics)-
dc.subject.lcshProcessor Architectures-
dc.subject.lcshDiscrete Mathematics in Computer Science-
dc.titleAdaptive iterated local search for cross-domain optimisationen_UK
dc.typeConference Paperen_UK
dc.rights.embargoreasonThe publisher has not yet responded to our queries. This work cannot be made publicly available in this Repository therefore there is an embargo on the full text of the work.-
dc.type.statusPublisher version-
dc.citation.btitleGECCO'11 Proceedings of the 13th annual conference on Genetic and Evolutionary Computation-
dc.citation.conferencelocationDublin, Ireland-
dc.citation.conferencename13th annual conference on Genetic and evolutionary computation-
dc.publisher.addressNew York, NY-
dc.contributor.affiliationComputing Science and Mathematics-
dc.contributor.affiliationUniversity of Montreal-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.contributor.affiliationUniversity of Nottingham-
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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