Effect of the number of parents on the performance of multi-parent genetic algorithm

Multi-parent genetic algorithm (MPGA) has become more interestingly algorithm, instead of using the number of parents more than the original genetic algorithm (GA) in reproduction process; the diversity of gene transformation from the parents literally affects the performance of genetic algorithms (GAs). In this paper, we compare binary coded multi-parent genetic algorithm with traditional genetic algorithm and analyze when one is better than the other as well as the effect of the number of parents by performing experiments on ten multimodal benchmark functions of up to 90 dimensions. The results show that using high number of parents yields better convergence without significantly increase in running time.