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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5879

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
Publication 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
Sponsorship: This work was supported by FAPESP, Brazil, and by the Engineering and Physical Sciences Research Council(EP/E060722/1), UK.
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5715241&tag=1
http://bura.brunel.ac.uk/handle/2438/5879
DOI: http://dx.doi.org/10.1109/SBRN.2010.46
ISBN: 978-1-4244-8391-4
ISSN: 1522-4899
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers
Publications

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