Evolution strategies with q-Gaussian mutation for dynamic optimization problems.
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 four experiments generated from the simulation of evolutionary robots.
Citation : Tinos, R. and Yang, S. (2010) Evolution strategies with q-Gaussian mutation for dynamic optimization problems. In: 2010 Eleventh Brazilian Symposium on Neural Networks (SBRN), Sao Paulo, October 2010. New York: IEEE, pp.
ISBN : 978-0-7695-4210-2
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes