Construction and Training of Artificial Neural Networks using Evolution Strategy with Parallel Populations

Application of artificial neural networks (ANN) in areas such as classification of images and audio signals shows the ability of this artificial intelligence technique for solving practical problems. Construction and training of ANNs is usually a time-consuming and hard process. A suitable neural model must be able to learn the training data and also have the generalization ability. In this paper, multiple parallel populations are used for construction of ANN and evolution strategy for its training, so that in each population a particular ANN architecture is evolved. By using a bi-criteria selection method based on error and complexity of ANNs, the proposed algorithm can produce simple ANNs that have high generalization ability. To assess the performance of the algorithm, 7 benchmark classification problems have been used. It has then been compared against the existing evolutionary algorithms that train and/or construct ANNs. Experimental results show the efficiency and robustness of the proposed algorithm compared to the other methods. In this paper, the impact of parallel populations, the bi-criteria selection method, and the crossover operator on the algorithm performance has been analyzed. A key advantage of the proposed algorithm is the use of parallel computing by means of multiple populations.