Study on convergence of self-adaptive and multi-population composite Genetic Algorithm

In view of the slowness and the locality of convergence for Basic Genetic Algorithm (BGA for short) in solving complex optimization problems, we proposed an improved genetic algorithm named self-adpative and multi-population composite genetic algorithm (SM-CGA for short), and gave the structure and implementation steps of the algorithm; then we consider its global convergence under the elitist preserving strategy using Markov chain theory, and analyze its performance through three examples from different aspects. All of the results indicate that the new algorithm possess interesting advantages such as better convergence, less chance trapping into premature states, so it can be widely used in many large-scale and high-accuracy optimization problems.