Adaptation of expansion rate for real-coded crossovers

Authors:
Youhei Akimoto;Jun Sakuma;Isao Ono;Shigenobu Kobayashi
Affiliations:
Tokyo Institute of Technology, Yokohama, Kanagawa, Japan;Tokyo Institute of Technology, Yokohama, Kanagawa, Japan;Tokyo Institute of Technology, Yokohama, Kanagawa, Japan;Tokyo Institute of Technology, Yokohama, Kanagawa, Japan
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

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Abstract

Premature convergence is one of the most notable obstacles that GAs face with. Once it happens, GAs cannot generate candidate solutions globally and the solutions are finally captured by local minima. To overcome it, we propose a mechanism that indirectly controls the variety of the population. It is realized by adapting the expansion rate parameter of crossovers, which determines the variance of the crossover distribution. The resulting algorithm is called adaptation of expansion rate (AER). The performance of the proposed methods is compared to an existing GA on several benchmark functions including functions whose landscape have ridge or multimodal structure. On these functions, existing GAs are likely to lead to premature convergence. The experimental result shows our approach outperforms the existing one on deceptive functions without disturbing the performance on comparatively easy problems.