Adaptive Crossover in Genetic Algorithms Using Pattern Based Method

Genetic Algorithms (GAs) emulate the natural evolution process and maintain a population of potential solutions to a given problem. Through the population, GA implicitly maintains statistics about the search space. This implicit statistics can be used explicitly to enhance GA's performance. Inspired by this idea, a pattern-based adaptive uniform crossover (PAUX) has been proposed. PAUX uses the statistical information of the alleles in each locus to adaptively calculate the swapping probability of that locus for crossover operation. In this paper PAUX is introduced and examined in some benchmark tests. Experimental results show that using PAUX improves the performance of traditional GAs.

کلیدواژه ها:

Genetic Algorithms ، Stochastic Genetic Algorithms ، Crossover Operators ، Adaptive Genetic Algorithms