Optimal population size for genetic algorithms: an investigation

Abstract

The performance of genetic algorithms (GAs) is affected by the parameters that are employed. In particular, the population size affects the performance and efficiency of GA-based systems. Grefenstette (1986) claimed that a population size between 60-110 is optimal for the convergence of GA-based systems to optimal solution. This paper presents studies that do not support this claim. GAPOLE, a GA-based program, is used to build self-learning self-adaptive self-optimising controllers for a dynamic multi-output unstable system using different population sizes. It is argued that population size may need to be tuned from one application to the other.< >