Effects of String Length and Mutation Rate on Success Probability of Genetic Algorithm

Mutation plays an important role in the computing of Genetic Algorithm. In this study, the relationship between success probability and mutation rate is analyzed by using multiplicative landscape. The success probability is defined that there is at least one optimal solution in the stationary state. The effect of mutation was illustrated by studying the distribution of the first order schemata with Markov theory and diffusion equations. This result provides a useful direction on parameter selection.