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Title: Evolution strategies with q-Gaussian mutation for dynamic optimization problems
Authors: Tinos, R
Yang, S
Keywords: Evolution strategies;Dynamic environments;Evolutionary algorithm;q-Gaussian mutation;Robotics
Issue Date: 2010
Publisher: IEEE
Citation: The 11th Brazilian Syposium on Artificial Neural Networks, Sao Paulo, Brazil: 223 - 228, 23 - 28 Oct 2010
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 four experiments generated from the simulation of evolutionary robots.
Description: This article is posted here with permmission from IEEE - Copyright @ 2010 IEEE
ISBN: 978-1-4244-8391-4
ISSN: 1522-4899
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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