A dual population genetic algorithm with evolving diversity

We propose a dual population genetic algorithm inspired by the complementary and dominance mechanism prevalent in nature. The proposed algorithm has two distinct populations: a main population and a reserve population. The main population is similar to that of an ordinary genetic algorithm and evolves to find good solutions. The reserve population evolves to maintain and offer diversity to the main population. While most multi-population genetic algorithms use migration as a means of information ex-change between different populations, our algorithm uses crossbreeding and survival selection because the two populations have different evolutionary objectives. The experimental results on various multimodal optimization problems show that the proposed algorithm is better than not only ordinary genetic algorithms but also than the other algorithms based on similar idea.