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Levy walk evolution for global optimization

Published:12 July 2008Publication History

ABSTRACT

A novel evolutionary global optimization approach based on adaptive covariance estimation is proposed. The proposed method samples from a multivariate Levy Skew Alpha-Stable distribution with the estimated covariance matrix to realize a random walk and so to generate new solution candidates in the mutation step. The proposed method is compared to the popular Differential Evolution method, which is one of the best general evolutionary global optimizers available. Experimental results indicate that the proposed approach yields a general improvement in the required number of function evaluations to solve global optimization problems. Especially, as shown in experiments, the underlying heavy tailed alpha-stable distribution enables a considerably more effective global search in more complex problems.

References

  1. Lagnoux, A. Rare event simulation, Probability in the Engineering and Informational Sciences, 20, 1, 43--66, 2006 Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Krzysztof Trojanowski, Clonal Selection Approach with Mutations Based on Symmetric alpha-Stable Distributions for Non-stationary Optimization Tasks, Springer Berlin / Heidelberg, 4431/2007, Adaptive and Natural Computing Algorithms, 978-3-540-71589-4, 184--193, July, 2007 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. K. V. Price, Differential evolution: a fast and simple numerical optimizer, Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS, IEEE Press, New York. ISBN: 0-7803-3225-3, 1996, Jun, 524--527Google ScholarGoogle ScholarCross RefCross Ref
  4. Nikolaus Hansen and Andreas Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation, Proc. of the 1996 IEEE Int. Conf. on Evolutionary Computation, IEEE Service Center, Piscataway, NJ, 312--317, 1996, citeseer.ist.psu.edu/hansen96adapting.htmlGoogle ScholarGoogle ScholarCross RefCross Ref
  5. Nikolaus Hansen and Sibylle D. Müller and Petros Koumoutsakos, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), Evol. Comput., 11, 1, 2003, 1063-6560, 1--18, http://dx.doi.org/10.1162/106365603321828970, MIT Press, Cambridge, MA, USA Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
        July 2008
        1814 pages
        ISBN:9781605581309
        DOI:10.1145/1389095
        • Conference Chair:
        • Conor Ryan,
        • Editor:
        • Maarten Keijzer

        Copyright © 2008 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 12 July 2008

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