Genetic algorithms using low-discrepancy sequences

Authors:
Shuhei Kimura;Koki Matsumura
Affiliations:
Tottori University, Tottori, JAPAN;Tottori University, Tottori, JAPAN
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

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Abstract

The random number generator is one of the important components of evolutionary algorithms (EAs). Therefore, when we try to solve function optimization problems using EAs, we must carefully choose a good pseudo-random number generator. In EAs, the pseudo-random number generator is often used for creating uniformly distributed individuals. As the low-discrepancy sequences allow us to create individuals more uniformly than the random number sequences, we apply the low-discrepancy sequence generator, instead of the pseudo-random number generator, to EAs in this study. The numerical experiments show that the low-discrepancy sequence generator improves the search performances of EAs.