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Experimental analysis of crossover and mutation operators on the quadratic assignment problem

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  • Zakir Hussain Ahmed

    (Al Imam Mohammad Ibn Saud Islamic University (IMSIU))

Abstract

In genetic algorithms crossover is the most important operator where pair of chromosomes and crossover site along their common length are selected randomly. Then the information after the crossover site of the parent chromosomes is swapped. On the other hand, mutation operator randomly alters some genes of a chromosome, and thus diversifies the search space. We consider three crossover and ten mutation operators for the genetic algorithms which are then compared for the quadratic assignment problem on some benchmark QAPLIB instances. The experimental study shows the effectiveness of the sequential constructive crossover and the adaptive mutation operators for the problem.

Suggested Citation

  • Zakir Hussain Ahmed, 2016. "Experimental analysis of crossover and mutation operators on the quadratic assignment problem," Annals of Operations Research, Springer, vol. 247(2), pages 833-851, December.
    • Handle: RePEc:spr:annopr:v:247:y:2016:i:2:d:10.1007_s10479-015-1848-y
    DOI: 10.1007/s10479-015-1848-y
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    References listed on IDEAS

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    1. Zvi Drezner & Peter Hahn & Éeric Taillard, 2005. "Recent Advances for the Quadratic Assignment Problem with Special Emphasis on Instances that are Difficult for Meta-Heuristic Methods," Annals of Operations Research, Springer, vol. 139(1), pages 65-94, October.
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    4. Paul, G., 2011. "An efficient implementation of the robust tabu search heuristic for sparse quadratic assignment problems," European Journal of Operational Research, Elsevier, vol. 209(3), pages 215-218, March.
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    9. Huizhen Zhang & Cesar Beltran-Royo & Liang Ma, 2013. "Solving the quadratic assignment problem by means of general purpose mixed integer linear programming solvers," Annals of Operations Research, Springer, vol. 207(1), pages 261-278, August.
    10. Zvi Drezner, 2003. "A New Genetic Algorithm for the Quadratic Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 15(3), pages 320-330, August.
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    2. Zvi Drezner & Taly Dawn Drezner, 2020. "Biologically Inspired Parent Selection in Genetic Algorithms," Annals of Operations Research, Springer, vol. 287(1), pages 161-183, April.

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