Crowding and Preselection Revisited


This paper considers the related algorithms, crowding and preselection, as potential multimodal function optimizers. It examines the ability of the two algorithms to preserve diversity, especially multimodal diversity. Crowding is analyzed in terms of the number of replacement errors it makes. Different strategies for reducing or eliminating error are proposed and examined. Finally, a variation of preselection is presented which approximates crowding, virtually eliminates replacement error, and restores selection pressure.