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Genetic and Evolutionary Computation Conference

GECCO 2003: Genetic and Evolutionary Computation — GECCO 2003 pp 171–182Cite as

A Hybrid Immune Algorithm with Information Gain for the Graph Coloring Problem

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2723))

Abstract

We present a new Immune Algorithm that incorporates a simple local search procedure to improve the overall performances to tackle the graph coloring problem instances. We characterize the algorithm and set its parameters in terms of Information Gain. Experiments will show that the IA we propose is very competitive with the best evolutionary algorithms.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, 95125, Catania, Italy

    Vincenzo Cutello, Giuseppe Nicosia & Mario Pavone

Authors
  1. Vincenzo Cutello
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  2. Giuseppe Nicosia
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  3. Mario Pavone
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Cutello, V., Nicosia, G., Pavone, M. (2003). A Hybrid Immune Algorithm with Information Gain for the Graph Coloring Problem. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_23

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  • DOI: https://doi.org/10.1007/3-540-45105-6_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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