Improving the Genetic Algorithm's Performance when using Transformation

Abstract. Transformation is a biologically inspired genetic operator that, when incorporated in the standard Genetic Algorithm can promote diversity in the population. Previous work using this genetic operator in the domain of function optimization and combinatorial optimization showed that the premature convergence of the population is avoided. Furthermore, the solutions obtained were, in general, superior to the solutions achieved by the GA with standard 1-point, 2-point and uniform crossover. In this paper we present an extensive empirical study carried to determine the best parameter setting to use with transformation in order to enhance the GA’s performance. These parameters include the gene segment length, the replacement rate (percentage of individuals of the previous population used to update the gene segment pool), and the mutation and transformation rates.