A framework for optimization of genetic programming evolved classifier expressions using particle swarm optimization

Abstract

Genetic Programming has emerged as an efficient algorithm for classification. It offers several prominent features like transparency, flexibility and efficient data modeling ability. However, GP requires long training times and suffers from increase in average population size during evolution. The aim of this paper is to introduce a framework to increase the accuracy of classifiers by performing a PSO based optimization approach. The proposed hybrid framework has been found efficient in increasing the accuracy of classifiers (expressed in the form of binary expression trees) in comparatively lesser number of function evaluations. The technique has been tested using five datasets from the UCI ML repository and found efficient. © 2010 Springer-Verlag.