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Theory of Coevolutionary Genetic Algorithms

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Parallel and Distributed Processing and Applications (ISPA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2745))

Abstract

We discuss stochastic modeling of scaled coevolutionary genetic algorithms (coevGA) which converge asymptotically to global optima. In our setting, populations contain several types of interacting creatures such that for some types (appropriately defined) globally maximal creatures exist. These algorithms particularly demand parallel processing in view of the nature of the fitness function. It is shown that coevolutionary arms races yielding global optima can be implemented in a procedure similar to simulated annealing.

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Author information

Authors and Affiliations

  1. The University of Aizu, Aizu-Wakamatsu City, Fukushima Pref., 965-8580, Japan

    Lothar M. Schmitt

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  1. Lothar M. Schmitt
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Editor information

Editors and Affiliations

  1. Department of Computer Software, The University of Aizu, Aizu-Wakamatsu City, Fukushima, 965-8580, Japan

    Minyi Guo

  2. Department of Computer Science, St. Francis Xavier University, Antigonish, NS, B2G 2W5, Canada

    Laurence Tianruo Yang

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© 2003 Springer-Verlag Berlin Heidelberg

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Schmitt, L.M. (2003). Theory of Coevolutionary Genetic Algorithms. In: Guo, M., Yang, L.T. (eds) Parallel and Distributed Processing and Applications. ISPA 2003. Lecture Notes in Computer Science, vol 2745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-37619-4_29

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  • DOI: https://doi.org/10.1007/3-540-37619-4_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40523-8

  • Online ISBN: 978-3-540-37619-4

  • eBook Packages: Springer Book Archive

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