Online population size adjusting using noise and substructural measurements

This paper proposes an online population size adjustment scheme for genetic algorithms. It utilizes linkage-model-building techniques to calculate the parameters used in facet-wise population-sizing models. The methodology is demonstrated using the dependency structure matrix genetic algorithm on 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 scheme decreases the population size and yields significant savings in the number of function evaluations required to obtain high-quality solutions; if the initial population size is too small, the scheme increases the population size and avoids premature convergence.

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