The performance of evolutionary algorithms (EAs) may be enhanced whether the choice of some parameters, as mutation rate and crossover method, is made appropriately. Several methods to adjust those parameters have been developed in order to enhance EAs performance. For this reason, it is important to understand EA dynamics. This paper presents a new population dynamics model to describe and predict the diversity at one generation. The formulation is based on the selection probability density function of each individual. The proposed population dynamics is modeled for an infinite population with generational evolution method. The model was tested in several case studies of different population sizes. The results suggest that the prediction error decreases with the population size increasement.
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