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    A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem 
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    • A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem
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    A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem

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    Submitted version (309.5Kb)
    Volume
    24
    Pagination
    113 - 141
    Publisher
    MIT Press
    DOI
    10.1162/EVCO_a_00145
    Issue
    1
    ISSN
    1063-6560
    Metadata
    Show full item record
    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
    Authors
    Drake, JH; Özcan, E; Burke, EK
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/14974
    Collections
    • Theoretical Computer Science Group [25]
    Licence information
    “Original publication is available at http://www.mitpressjournals.org/doi/abs/10.1162/EVCO_a_00145#.V82KqPkrKUk”
    Copyright statements
    © 2016 The MIT Press
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