A population dynamics model to describe gene frequencies in evolutionary algorithms

The performance of evolutionary algorithms (EAs) may heavily depend severely on a suitable choice of parameters such as mutation and crossover rates. Several methods to adjust those parameters have been developed in order to enhance EA performance. For this purpose, it is important to understand the EA dynamics, i.e., to appreciate the behavior of the population. Hence, this paper presents a new model of population dynamics to describe and predict the diversity in any particular generation. The formulation is based on selecting the probability density function of each individual. The population dynamics proposed is modeled for a generational population. The model was tested in several case studies of different population sizes. The results suggest that the prediction error decreases as the population size increases.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Performance of evolutionary algorithms may heavily depend on a choice of parameters. ► Methods to adjust evolutionary algorithms parameters have been developed. ► It is important to understand the evolutionary algorithm dynamics. ► We present a model to describe and predict the evolutionary algorithm diversity. ► Our model is based on selecting the probability density function of each individual.