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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional Knapsack problem
Author(s): Drake, John
Ozcan, Ender
Burke, Edmund
Keywords: Combinatorial optimisation
local search
multidimensional knapsack problem
Issue Date: Apr-2016
Citation: Drake J, Ozcan E & Burke E (2016) A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional Knapsack problem, Evolutionary Computation, 24 (1), pp. 113-141.
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 lowlevel 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 hyperheuristics 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 by the Massachusetts Institute of Technology.
DOI Link:
Rights: © 2016 Massachusetts Institute of Technology Evolutionary Computation, Volume 24, Issue 1, Spring 2016, p.113-141.

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