Selective Crossover in Genetic Algorithms: An Empirical Study

The performance of a genetic algorithm (GA) is dependent on many factors: the type of crossover operator, the rate of crossover, the rate of mutation, population size, and the encoding used are just a few examples. Currently, GA practitioners pick and choose GA parameters empirically until they achieve adequate performance for a given problem. In this paper we have isolated one such parameter: the crossover operator. The motivation for this study is to provide an adaptive crossover operator that gives best overall performance on a large set of problems. A new adaptive crossover operator “selective crossover” is proposed and is compared with two-point and uniform crossover on a problem generator where epistasis can be varied and on trap functions where deception can be varied. We provide empirical results which show that selective crossover is more efficient than two-point and uniform crossover across a representative set of search problems containing epistasis.