Levy walk evolution for global optimization

Date
2008-07
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Source Title
GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
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Publisher
ACM
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Pages
537 - 538
Language
English
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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. Track: Evolution Strategies.

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