On the evolution of neural networks for pairwise classification using gene expression programming

Abstract

Neural networks are a common choice for solving classification problems, but require experimental adjustments of the topology, weights and thresholds to be effective. Success has been seen in the development of neural networks with evolutionary algorithms, making the extension of this work to classification problems a logical step. This paper presents the first known use of the Gene Expression Programming-based GEP-NN algorithm to design neural networks for classification purposes. The system uses pairwise decomposition to produce a series of binary classifiers for a given multi-class problem, with the results of the classifier set being combined by majority vote.