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Linkage Identification by Nonlinearity Check for Real-Coded Genetic Algorithms
Masaru Tezuka1,2, Masaharu Munetomo1, and Kiyoshi Akama1
1Hokkaido University, Kita 11, Nishi 5, Kita-ku, Sapporo, 060-0811, JAPAN
tezuka@uva.cims.hokudai.ac.jp
munetomo@cims.hokudai.ac.jp
akama@cims.hokudai.ac.jp
2Hitachi East Japan Solutions, Ltd., 2-16-10, Honcho, Aoba-ku, Sendai, 980-0014, JAPAN
tezuka@hitachi-to.co.jp
Abstract. Linkage identification is a technique to recognize decomposable or quasi-decomposable sub-problems. Accurate linkage identification improves GA’s search capability. We introduce a new linkage identification method for Real-Coded GAs called LINC-R (Linkage Identification by Nonlinearity Check for Real-Coded GAs). It tests nonlinearity by random perturbations on each locus in a real value domain. For the problem on which the proportion of nonlinear region in the domain is smaller, more perturbations are required to ensure LINC-R to detect nonlinearity successfully. If the proportion is known, the population size which ensures a certain success rate of LINC-R can be calculated. Computational experiments on benchmark problems showed that the GA with LINC-R outperforms conventional Real-Coded GAs and those with linkage identification by a correlation model.
LNCS 3103, p. 222 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004