Toward co-evolutionary training of a multi-class classifier

In this work the multi-class classification capabilities of genetic programming (GP) are explored in the context of a competitive co-evolutionary system, in which a population of GP classifiers is trained against an evolving population of trainers (exemplar selectors) with the goal of reducing GP training time for large multi-class classification problems. Moreover, the niche-enabling mechanisms established in the genetic algorithm (GA) literature, known as crowding and sharing, are implemented for the classifier population in order to provide multi-class solutions from a single population in the same trial. The results as presented in the paper indicate the appropriateness of the competitive co-evolutionary training approach under GP multi-class classification.