Use of Local Ranking in Cellular Genetic Algorithms with Two Neighborhood Structures

Authors:
Hisao Ishibuchi;Noritaka Tsukamoto;Yusuke Nojima
Affiliations:
Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan 599-8531;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan 599-8531;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan 599-8531
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

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Abstract

In our former study (Ishibuchi et al. 2006), we proposed the use of two neighborhood structures in a cellular genetic algorithm. One is for local selection where a pair of parents is selected from neighboring cells for mating. This neighborhood structure has been usually used in standard cellular algorithms. The other is for local competition, which is used to define local elitism and local ranking. We have already examined the effect of local elitism on the performance of our cellular genetic algorithm (Ishibuchi et al. 2008). In this paper, we examine the effect of using local ranking as the fitness of each individual. First we explain our cellular genetic algorithm with the two neighborhood structures. Then we examine its two variants with/without local ranking. In one variant, the local ranking of an individual among its neighbors is used as its fitness. Such a fitness redefinition scheme can be viewed as a kind of noise in parent selection. The other variant uses the original fitness value (instead of its local ranking). Through computational experiments, we demonstrate that the use of the local ranking improves the ability to escape from local optima.