Please use this identifier to cite or link to this item:
Title: Self-adaptation of mutation distribution in evolutionary algorithms
Authors: Tinos, R
Yang, S
Keywords: Gaussian distribution;Evolutionary computation
Issue Date: 2007
Publisher: IEEE
Citation: IEEE Congress on Evolutionary Computation (CEC 2007), Singapore: 79 - 86, 25-28 Sep 2007
Abstract: This paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose, the isotropic g-Gaussian distribution is employed in the mutation operator. The g-Gaussian distribution allows to control the shape of the distribution by setting a real parameter g and can reproduce either finite second moment distributions or infinite second moment distributions. In the proposed method, the real parameter q of the g-Gaussian distribution is encoded in the chromosome of an individual and is allowed to evolve. An evolutionary programming algorithm with the proposed idea is presented. Experiments were carried out to study the performance of the proposed algorithm.
Description: This paper is posted here with permission from IEEE - Copyright @ 2007 IEEE
ISBN: 978-1-4244-1339-3
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf592.21 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.