Journal of Heuristics

, Volume 4, Issue 1, pp 25–46 | Cite as

Graph Coloring with Adaptive Evolutionary Algorithms

  • A.E. Eiben
  • J.K. van der Hauw
  • J.I. van Hemert

Abstract

This paper presents the results of an experimental investigation on solving graph coloring problems with Evolutionary Algorithms (EAs). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based representation and an adaptation mechanism that periodically changes the fitness function during the evolution. This adaptive EA is general, using no domain specific knowledge, except, of course, from the decoder (fitness function). We compare this adaptive EA to a powerful traditional graph coloring technique DSatur and the Grouping Genetic Algorithm (GGA) on a wide range of problem instances with different size, topology and edge density. The results show that the adaptive EA is superior to the Grouping (GA) and outperforms DSatur on the hardest problem instances. Furthermore, it scales up better with the problem size than the other two algorithms and indicates a linear computational complexity.

evolutionary algorithms genetic algorithms constraint satisfaction graph coloring grouping problem penalty functions adaptive parameters 

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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • A.E. Eiben
    • 1
  • J.K. van der Hauw
    • 1
  • J.I. van Hemert
    • 1
  1. 1.Leiden UniversityThe Netherlands

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