Levy walk evolution for global optimization
GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
537 - 538
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/26760
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.
- Conference Paper 2294
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