Genetic algorithm with dual species

In this paper, a new genetic algorithm with two species is proposed. Our dual species genetic algorithm (DSGA) composes of two subpopulation that constitute of same size individuals. The subpopulations have different characteristics, such as crossover probability and mutation operator. In one subpopulation, the parents with higher similarity are cross with higher rate; mutate with general mutation operator. So that, the new algorithm can obtains good exploitation ability. In the other subpopulation, the parents with smaller similarity are cross with higher rate; mutate with big mutation rate. So that, the new algorithm can gets good exploration ability. The performance of our DSGA is compare to that of a single population genetic algorithm (SPGA) and Multi-population genetic algorithm with two populations (2PMGA). The experimental results show that the proposed method can gain higher global convergence rate and higher speed.