dc.contributor.author | Drake, JH | |
dc.contributor.author | Özcan, E | |
dc.contributor.author | Burke, EK | |
dc.date.accessioned | 2016-09-05T15:13:31Z | |
dc.date.available | 2016-09-05T15:13:31Z | |
dc.date.issued | 2016-03 | |
dc.date.submitted | 2016-07-28T14:25:00.643Z | |
dc.identifier.citation | "MIT Press Journals - Evolutionary Computation - Abstract", Mitpressjournals.org, 2016 <http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00145#.V82KqPkrKUk> [accessed 5 September 2016] | en_US |
dc.identifier.issn | 1063-6560 | |
dc.identifier.other | 10.1162/EVCO_a_00145 | |
dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/14974 | |
dc.description.abstract | Hyper-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. © 2016 Massachusetts Institute of Technology | en_US |
dc.format.extent | 113 - 141 | |
dc.language.iso | en | en_US |
dc.publisher | MIT Press | en_US |
dc.relation.isreplacedby | 123456789/14975 | |
dc.relation.isreplacedby | http://qmro.qmul.ac.uk/xmlui/handle/123456789/14975 | |
dc.rights | “Original publication is available at http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00145#.V82KqPkrKUk” | |
dc.title | A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2016 The MIT Press | |
dc.identifier.doi | 10.1162/EVCO_a_00145 | |
dc.relation.isPartOf | Evolutionary Computation | |
pubs.issue | 1 | |
pubs.publication-status | Published | |
pubs.volume | 24 | |