Population sizing to go: Online adaptation using noise and substructural measurements

Abstract

This chapter presents an online population size adjustment scheme for genetic algorithms. It utilizes substructural identification techniques to calculate the parameters used in facetwise population-sizing models. The methodology is demonstrated using the dependency structure matrix genetic algorithm on a set of boundedly-difficult problems. Empirical results indicate that the proposed method is both efficient and robust. If the initial population size is too large, the proposed method automatically decreases the population size, and thereby yields significant savings in the number of function evaluations required to obtain high-quality solutions; on the other hand, if the initial population size is too small, the proposed scheme increases the population size on-the-fly, thereby avoiding premature convergence. © Springer-Verlag Berlin Heidelberg 2007.