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dc.contributor.authorDrake, JHen_US
dc.contributor.authorÖzcan, Een_US
dc.contributor.authorBurke, EKen_US
dc.date.accessioned2016-09-05T15:13:31Z
dc.date.accessioned2016-09-05T15:22:20Z
dc.date.issued2016en_US
dc.date.submitted2016-07-28T14:25:00.643Z
dc.date.submitted2016-09-05T16:20:18.400Z
dc.identifier.other10.1162/EVCO_a_00145
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/14975
dc.description.abstractHyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain.en_US
dc.format.extent113 - 141en_US
dc.languageengen_US
dc.language.isoenen_US
dc.relation.ispartofEvol Computen_US
dc.relation.replaceshttp://qmro.qmul.ac.uk/xmlui/handle/123456789/14974
dc.relation.replaces123456789/14974
dc.rights“Original publication is available at http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00145#.V82KqPkrKUk”
dc.subjectCombinatorial optimisationen_US
dc.subjecthyper-heuristicsen_US
dc.subjectlocal searchen_US
dc.subjectmetaheuristic.en_US
dc.subjectmultidimensional knapsack problemen_US
dc.subjectAlgorithmsen_US
dc.subjectBiological Evolutionen_US
dc.subjectComputer Simulationen_US
dc.subjectHeuristicsen_US
dc.subjectHumansen_US
dc.subjectProblem Solvingen_US
dc.titleA Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem.en_US
dc.typeArticle
dc.rights.holder© 2016 The MIT Press
dc.identifier.doi10.1162/EVCO_a_00145en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/25635698en_US
pubs.issue1en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume24en_US
qmul.funderDAASE: Dynamic Adaptive Automated Software Engineering::Engineering and Physical Sciences Research Councilen_US


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