Evolutionary programming with q-Gaussian mutation for evolutionary optimization problems.
The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q-Gaussian distribution is employed to generate new candidate solutions by mutation. A real parameter q, which defines the shape of the distribution, is encoded in the chromosome of individuals and is allowed to evolve. Algorithms with self-adapted mutation generated from isotropic and anisotropic distributions are presented. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on three dynamic optimization problems.
Citation : Tinos, R. and Yang, S. (2008) Evolutionary programming with q-Gaussian mutation for evolutionary optimization problems. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, Hong Kong, 1-6 June. New York: IEEE, pp. 1823-1830.
ISBN : 978-1-4244-1822-0
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes
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