An Empirical Study of the Effect of Parameter Combination on the Performance of Genetic Algorithms

Abstract

Performance of genetic algorithms is affected not only by each genetic operator, but also by the interaction among genetic operators. Research on this issue still fails to converge to any conclusion. In this paper, the author focuses mainly on investigating, through a series of systematic experiments, the effects of different combinations of parameter settings for genetic operators on the performance of the author’s GA-based batch selection system, and compare the research results with the claims made by previous research. One of the major findings of the author’s research is that the crossover rate is not as a determinant factor as the population size or the mutation rate in affecting a GA’s performance. This paper intends to serve as an inquiry into the research of useful design guidelines for parameterizing GA-based systems. An Empirical Study of the Effect of Parameter Combination on the Performance of Genetic Algorithms