Evolutionary algorithms with adaptive Levy mutations

An evolutionary programming algorithm with adaptive mutation operators based on Levy probability distribution is studied. Levy stable distribution has an infinite second moment. Because of this, Levy mutation is more likely to generate an offspring that is farther away from its parent than Gaussian mutation, which is often used in evolutionary algorithms. Such likelihood depends on a parameter α in the distribution. Based on this, we propose an adaptive Levy mutation in which four different candidate offspring are generated by each parent, according to α=1.0, 1.3, 1.7, and 2.0, and the best one is chosen as the offspring for the next generation. The proposed algorithm was applied to several multivariate function optimization problems. We show empirically that the performance of the proposed algorithm was better than that of classical evolutionary algorithms using Gaussian mutation