Local convergence rate of evolutionary algorithm with combined mutation operator
Nam Geun Kim, Jin-Myung Won, J.S. Lee, S.W. Kim · 2003
An appropriate mutation operator of the evolutionary algorithm (EA) maintains a balance between exploration and exploitation. This balance is usually satisfied by using the combined mutation operators (CMOs) of the Gaussian and Cauchy random variables. This paper studies the convergence property of the CMO. As a good model of the CMO, it proposes to use the decision factor /spl alpha/, the probability of choosing the Gaussian random variable between the Gaussian and Cauchy random variables for a mutation operator. This paper shows that the optimal convergence rate and the associated optimal mutation step size are monotonically decreasing with respect to /spl alpha/.