Evolutionary programming using mutations based on the Levy probability distribution

Abstract

This paper studies evolutionary programming with mutations based on the Lévy probability distribution. The Lévy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter in the Lévy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Lévy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.

DOI: 10.1109/TEVC.2003.816583

Extracted Key Phrases

13 Figures and Tables

02040'05'06'07'08'09'10'11'12'13'14'15'16'17
Citations per Year

279 Citations

Semantic Scholar estimates that this publication has 279 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Lee2004EvolutionaryPU, title={Evolutionary programming using mutations based on the Levy probability distribution}, author={Chang-Yong Lee and Xinsheng Yao}, journal={IEEE Trans. Evolutionary Computation}, year={2004}, volume={8}, pages={1-13} }