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An empirical study of hybrid genetic algorithms for the set covering problem

Author

Listed:
  • F J Vasko

    (Kutztown University)

  • P J Knolle

    (Tulane University)

  • D S Spiegel

    (Kutztown University)

Abstract

The purpose of this paper is to explore the computational performance of several hybrid algorithms that are extensions of a basic genetic algorithm (GA) approach for solving the set covering problem (SCP). We start by making several enhancements to a GA for the SCP that was proposed by Beasley and Chu. Next, several hybrid solution approaches are introduced that combine the GA with various local neighbourhood search approaches, with a form of the greedy randomized adaptive search procedure, and with an estimation of distribution algorithms approach. Using Beasley's library of SCPs extensive computational results are generated for the hybrid solution approaches defined in this paper. Statistical analyses of the results are performed.

Suggested Citation

  • F J Vasko & P J Knolle & D S Spiegel, 2005. "An empirical study of hybrid genetic algorithms for the set covering problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(10), pages 1213-1223, October.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:10:d:10.1057_palgrave.jors.2601919
    DOI: 10.1057/palgrave.jors.2601919
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    References listed on IDEAS

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    1. Colin R. Reeves, 1997. "Feature Article---Genetic Algorithms for the Operations Researcher," INFORMS Journal on Computing, INFORMS, vol. 9(3), pages 231-250, August.
    2. U Aickelin, 2002. "An indirect genetic algorithm for set covering problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(10), pages 1118-1126, October.
    3. Beasley, J. E. & Chu, P. C., 1996. "A genetic algorithm for the set covering problem," European Journal of Operational Research, Elsevier, vol. 94(2), pages 392-404, October.
    4. Alberto Caprara & Matteo Fischetti & Paolo Toth, 1999. "A Heuristic Method for the Set Covering Problem," Operations Research, INFORMS, vol. 47(5), pages 730-743, October.
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    Cited by:

    1. Z P Fan & Y Chen & J Ma & S Zeng, 2011. "A hybrid genetic algorithmic approach to the maximally diverse grouping problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 92-99, January.
    2. Z P Fan & Y Chen & J Ma & S Zeng, 2011. "Erratum: A hybrid genetic algorithmic approach to the maximally diverse grouping problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1423-1430, July.

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