Online Population Size Adjusting Using Noise and Substructural Measurements

Abstract:
This paper proposes an online population size adjustment scheme for genetic algorithms. It utilizes linkage-model-building 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; if the initial population size is too small, the proposed scheme increases the population size on-the-fly and thereby avoiding premature convergence.

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