Multi-operator evolutionary programming: A preliminary study on function optimization

Abstract

Classical evolutionary programming uses Gaussian mutation as the primary search operator. Recent studies have shown that using a Cauchy random variable as the primary operator leads to faster convergence for certain function optimization problems. In this study we explore the use of both the Gaussian and the Cauchy operators along with a self-adaptive mechanism to select the appropriate operator for each individual in the population. Empirical studies of the dual-operator evolutionary programming are conducted using a limited set of test function optimization problems.