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Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm

Listed author(s):
  • Iztok Fister


  • Marjan Mernik


  • Bogdan Filipič


Registered author(s):

    This paper proposes a hybrid self-adaptive evolutionary algorithm for graph coloring that is hybridized with the following novel elements: heuristic genotype-phenotype mapping, a swap local search heuristic, and a neutral survivor selection operator. This algorithm was compared with the evolutionary algorithm with the SAW method of Eiben et al., the Tabucol algorithm of Hertz and de Werra, and the hybrid evolutionary algorithm of Galinier and Hao. The performance of these algorithms were tested on a test suite consisting of randomly generated 3-colorable graphs of various structural features, such as graph size, type, edge density, and variability in sizes of color classes. Furthermore, the test graphs were generated including the phase transition where the graphs are hard to color. The purpose of the extensive experimental work was threefold: to investigate the behavior of the tested algorithms in the phase transition, to identify what impact hybridization with the DSatur traditional heuristic has on the evolutionary algorithm, and to show how graph structural features influence the performance of the graph-coloring algorithms. The results indicate that the performance of the hybrid self-adaptive evolutionary algorithm is comparable with, or better than, the performance of the hybrid evolutionary algorithm which is one of the best graph-coloring algorithms today. Moreover, the fact that all the considered algorithms performed poorly on flat graphs confirms that graphs of this type are really the hardest to color. Copyright Springer Science+Business Media, LLC 2013

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    Article provided by Springer in its journal Computational Optimization and Applications.

    Volume (Year): 54 (2013)
    Issue (Month): 3 (April)
    Pages: 741-770

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    Handle: RePEc:spr:coopap:v:54:y:2013:i:3:p:741-770
    DOI: 10.1007/s10589-012-9496-5
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    1. Smith, D. H. & Hurley, S. & Thiel, S. U., 1998. "Improving heuristics for the frequency assignment problem," European Journal of Operational Research, Elsevier, vol. 107(1), pages 76-86, May.
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    3. Chams, M. & Hertz, A. & de Werra, D., 1987. "Some experiments with simulated annealing for coloring graphs," European Journal of Operational Research, Elsevier, vol. 32(2), pages 260-266, November.
    4. Avanthay, Cedric & Hertz, Alain & Zufferey, Nicolas, 2003. "A variable neighborhood search for graph coloring," European Journal of Operational Research, Elsevier, vol. 151(2), pages 379-388, December.
    5. J. Randall Brown, 1972. "Chromatic Scheduling and the Chromatic Number Problem," Management Science, INFORMS, vol. 19(4-Part-1), pages 456-463, December.
    6. Burke, Edmund K. & McCollum, Barry & Meisels, Amnon & Petrovic, Sanja & Qu, Rong, 2007. "A graph-based hyper-heuristic for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 176(1), pages 177-192, January.
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