Variance as a Stopping Criterion for Genetic Algorithms with Elitist Model

Abstract

Genetic Algorithm (GA) has now become one of the leading mechanisms in providing solution to complex optimization problems. Although widely used, there are very few theoretical guidelines for determining when to stop the algorithm. This article establishes theoretically that the variance of the best fitness values obtained in the iterations can be considered as a measure to decide the termination criterion of a GA with elitist model (EGA). The criterion automatically takes into account the inherent characteristics of the objective function. Implementation issues of the proposed stopping criterion are explained. Its difference with some other stopping criteria is also critically analyzed.