Abstract
Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator.