A new genetic local search algorithm for graph coloring

  • RaphaËl Dorne
  • Jin-Kao Hao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


This paper presents a new genetic local search algorithm for the graph coloring problem. The algorithm combines an original crossover based on the notion of union of independent sets and a powerful local search operator (tabu search). This new hybrid algorithm allows us to improve ou the best known results of some large instances of the famous Dimacs benchmarks.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    D. Brélaz. New methods to color vertices of a graph. Communications of ACM. 22: 251–256. 1979.zbMATHCrossRefGoogle Scholar
  2. 2.
    M. Chams, A. Hertz, and D. De Werra. Some experiments with simulated annealing for coloring graphs. European Journal of Operational Research. 32: 260–266, 1987.zbMATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    D. Costa, A. Hertz, and O. Dubuis. Embedding of a sequential procedure within an evolutionary algorithms for coloring problems in graphs. Journal of Heuristics. 1(1): 105–128, 1995.zbMATHCrossRefGoogle Scholar
  4. 4.
    R. Dorne and J.K. Hao Tabu search for graph coloring, T-coloring and set T-colorings. Presented at the 2nd Intl. Conf. on Metaheuristics, Sophia-Antopollis, France, July, 1997, Under review for publication.Google Scholar
  5. 5.
    E. Falkenauer A hybrid grouping genetic algorithm for bin-packing. Journal of Heuristics, 2(1): 5–30, 1996.CrossRefGoogle Scholar
  6. 6.
    C. Fleurent and J.A. Ferland. Genetic and hybrid algorithms for graph coloring. Annals of Operations Research, 63: 437–463, 1995.CrossRefGoogle Scholar
  7. 7.
    B. Freisleben and P. Merz. New genetic local search operators for the traveling salesman problem. Proc. of PPSN-96, Lecture Notes in Computer Science 1141, pp890–899, Springer-Verlag, 1996.Google Scholar
  8. 8.
    P. Galinier and J.K. Hao New crossover operators for graph coloring. Research Report, April 1998.Google Scholar
  9. 9.
    F. Glover and M. Laguna. Tabu Search. Kluwer Academic Publishers. 1997.Google Scholar
  10. 10.
    A. Hertz and D. De Werra. Using tabu search techniques for graph coloring. Computing, 39: 345–351, 1987.zbMATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    D.S. Johnson, C.R. Aragon, L.A. McGeoch, and C. Schevon. Optimization by simulated annealing: an experimental evaluation; part ii, graph coloring and number partitioning. Operations Research, 39(3): 378–406, 1991.zbMATHCrossRefGoogle Scholar
  12. 12.
    F.T. Leighton. A graph coloring algorithm for large scheduling problems. Journal of Research of the National Bureau Standard, 84: 79–100, 1979.MathSciNetGoogle Scholar
  13. 13.
    P. Merz and B. Freisleben. A genetic local search approach to the quadratic assignment problem. In Proc. of ICGA-97, pp 465–472, Morgan Kaufmann Publishers, 1997.Google Scholar
  14. 14.
    C. Morgenstern. Distributed coloration neighborhood search. Discrete Mathematics and Theoretical Computer Science, 26: 335–358. American Mathematical Society, 1996.Google Scholar
  15. 15.
    C.H. Papadimitriou and K. Steiglitz. Combinatorial Optimization — Algorithms and Complexity. Prentice Hall, 1982.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • RaphaËl Dorne
    • 1
  • Jin-Kao Hao
    • 1
  1. 1.LGI2P/EMA-EERIENÎmesFrance

Personalised recommendations