Abstract

This paper presents a model for predicting the convergence quality of genetic algorithms. The model incorporates previous knowledge about decision making in genetic algorithms and the initial supply of building blocks in a novel way. The result is an equation that accurately predicts the quality of the solution found by a GA using a given population size. Adjustments for different selection intensities are considered and computational experiments demonstrate the effectiveness of the model. I. Introduction The size of the population in a genetic algorithm (GA) is a major factor in determining the quality of convergence. The question of how to choose an adequate population size for a particular domain is difficult and has puzzled GA practitioners for a long time. Hard questions are better approached using a divide-and-conquer strategy and the population sizing issue is no exception. In this case, we can identify two factors that influence convergence quality: the initial supply of build...

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