Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5860
Title: Evolutionary programming with q-Gaussian mutation for dynamic optimization problems
Authors: Tinos, R
Yang, S
Keywords: Anisotropic magnetoresistance;Biological cells;Dynamic programming;Entropy;Gaussian distribution;Genetic mutations;Genetic programming;Probability distribution;Shape control;Testing
Issue Date: 2008
Publisher: IEEE
Citation: The 2008 IEEE Congress on Evolutionary Computation, Hong Kong: 1823 - 1830, 01 - 06 Jun 2008
Abstract: 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.
Description: This article is posted here with permission from IEEE - Copyright @ 2008 IEEE
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4631036
http://bura.brunel.ac.uk/handle/2438/5860
DOI: http://dx.doi.org/10.1109/CEC.2008.4631036
ISBN: 978-1-4244-1822-0
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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