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Levy walk evolution for global optimization

Authors:

Onay Urfalioglu,

Ahmet Enis Cetin,

Ercan Engin KuruogluAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 537 - 538

https://doi.org/10.1145/1389095.1389200

Published: 12 July 2008 Publication History

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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

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[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

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[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--527

Crossref

Google Scholar

[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.html

Crossref

Google Scholar

[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

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Aslan SDemirci S(2022)An improved immune plasma algorithm with a regional pandemic restrictionSignal, Image and Video Processing10.1007/s11760-022-02171-w16:8(2093-2101)Online publication date: 4-Mar-2022
https://doi.org/10.1007/s11760-022-02171-w
Yang XDeb S(2010)Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic OptimizationNature Inspired Cooperative Strategies for Optimization (NICSO 2010)10.1007/978-3-642-12538-6_9(101-111)Online publication date: 2010
https://doi.org/10.1007/978-3-642-12538-6_9

Index Terms

Levy walk evolution for global optimization
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

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
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Aslan SDemirci S(2022)An improved immune plasma algorithm with a regional pandemic restrictionSignal, Image and Video Processing10.1007/s11760-022-02171-w16:8(2093-2101)Online publication date: 4-Mar-2022
https://doi.org/10.1007/s11760-022-02171-w
Yang XDeb S(2010)Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic OptimizationNature Inspired Cooperative Strategies for Optimization (NICSO 2010)10.1007/978-3-642-12538-6_9(101-111)Online publication date: 2010
https://doi.org/10.1007/978-3-642-12538-6_9

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Crossover-first differential evolution for improved global optimization in non-uniform search landscapes

Multi-population differential evolution with adaptive parameter control for global optimization

A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization

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