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research
A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem
Authors
Battiti R.
Bilgin B.
+24 more
Burke E. K.
Burke E. K.
Burke E. K.
Burke E. K.
Cowling P.
Edmund K. Burke
Ender Özcan
Fisher M.
Forrest S.
Garey M. R.
Goldberg D. E
Hyde M.
John H. Drake
Jones T.
Koza J. R
Maniezzo V.
Nareyek A.
Onsem W. V.
Qian F.
Rendl A.
Sutton R.
Syswerda G
Vasquez M.
Özcan E.
Publication date
30 January 2015
Publisher
'MIT Press - Journals'
Doi
Cite
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 Technolog
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