A statistical study of the Differential Evolution based on continuous generation model

Differentiation Evolution (DE) is an Evolutionary Algorithm (EA) for solving function optimization problems. In order to renew the population in EA, there are two generation models. The first one is “discrete generation model”, and the second one is “continuous generation model”. Conventional DEs have been based on the discrete generation model in which the current generation's population is replaced by the next generation's population at a time. In this paper, a novel DE based on the continuous generation model is described. Because a newborn excellent individual is added to an only population and can be used immediately to generate offspring in the continuous generation model, it can be expected that the novel DE converges faster than the conventional ones. Furthermore, by employing the continuous generation model, it becomes easy to introduce various survival selection methods into DE. Therefore, three survival selection methods are contrived for the novel DE based on the continuous generation model. Finally, the effects of the generation model, the survival selection method, the reproduction selection method, the population size and their interactions on the performance of DE are evaluated statistically by using the analysis of variance (ANOVA).