On Multi-class Classification by Way of Niching

Abstract

In recent literature, the niche enabling effects of crowding and the sharing algorithms have been systematically investigated in the context of Genetic Algorithms and are now established evolutionary methods for identifying optima in multi-modal problem domains. In this work, the niching metaphor is methodically explored in the context of a simultaneous multi-population GP classifier in order to investigate which (if any) properties of traditional sharing and crowding algorithms may be portable in arriving at a naturally motivated niching GP. For this study, the niching mechanisms are implemented in Grammatical Evolution to provide multi-category solutions from the same population in the same trial. Each member of the population belongs to a different niche in the GE search space corresponding to the data classes. The set of best individuals from each niche are combined hierarchically and used for multi-class classification on the familiar multi-class UCI data sets of Iris and Wine. A distinct preference for Sharing as opposed to Crowding is demonstrated with respect to population diversity during evolution and niche classification accuracy.