The Convergence Analysis of Genetic Algorithm Based on Space Mating

This paper analyzes the convergence properties of the genetic algorithm based on space mating with mutation, crossover and proportional reproduction applied to static optimization on problems. It is proved by means of homogeneous finite Markov chain analysis that genetic algorithm based on space mating will converge to the global optimum. Each process is convergence to the global optimum, at least satisfactory solution under the best individual survives besides the last course. And illuminate a population converge with probability one in the no mutation operator conditions. By comparing the experiment, we can see that the algorithm have better convergence than SGA and consist with the theory.