抄録
The random number generator is one of the important components of evolutionary algorithms. Therefore, when we try to solve function optimization problems using the evolutionary algorithms, we must carefully choose a good pseudo-random number generator. In the evolutionary algorithms, the pseudo-random number generator is often used for creating uniformly distributed individuals. In this study, 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 the evolutionary algorithms. Since it was difficult for some evolutionary algorithms, such as binary-coded genetic algorithms, to utilize the uniformity of the sequences, the low-discrepancy sequence generator was applied to real-coded genetic algorithms. The numerical experiments show that the low-discrepancy sequence generator improves the search performances of the real-coded genetic algorithms.
