Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach

Abstract

The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the coevolutionary aspect of the IPCA algorithm is utilized to simultaneously evolve a subset of the training data that provides distinctions between candidate classifiers. Empirical results indicate that such a scheme significantly reduces the computational overhead of fitness evaluation on large binary classification data sets. Moreover, unlike the performance of GP classifiers trained using alternative subset selection algorithms, the proposed Pareto-coevolutionary approach is able to match or better the classification performance of GP trained over all training exemplars. Finally, problem decomposition appears as a natural consequence of assuming a Pareto model for coevolution. In order to make use of this property a voting scheme is used to integrate the results of all classifiers from the Pareto front, post training.