Solving Large N-Bit Parity Problems with the Evolutionary ANN Ensemble

Abstract

Artificial neural networks (ANNs) have been successfully applied to many areas due to its powerful ability both for classification and regression problems. For some difficult problems, ANN ensemble classifiers are considered, instead of a single ANN classifier. In the previous study, the authors presented the systematic trajectory search algorithm (STSA) to train the ANN. The STSA utilizes the orthogonal array (OA) to uniformly generate the initial population to globally explore the solution space, and then applies a novel trajectory search method to exploit the promising areas thoroughly. In this paper, an evolutionary constructing algorithm, called the ESTSA, of the ANN ensemble is proposed. Based on the STSA, the authors introduce a penalty term to the error function in order to guarantee the diversity of ensemble members. The performance of the proposed algorithm is evaluated by applying it to train a class of feedforward neural networks to solve the large n-bit parity problems. By comparing with the previous studies, the experimental results revealed that the neural network ensemble classifiers trained by the ESTSA have very good classification ability.