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Evolutionary Design of Multilayer and Radial Basis Function Neural Network Classifiers: an Empirical Comparison


Njah Mohamed, El Hamdi Ridha


Vol. 16  No. 6  pp. 86-93


Artificial neural networks have been recognized as a powerful tool for pattern classification problems and have attracted a lot of research effort in the field of machine learning. However, optimal design of such models is known to be a notoriously hard problem. In this paper we investigate the effectiveness of a new hybrid evolutionary approach to address the optimal design of neural network based classifiers. The particularity of this approach lies in the use of two major techniques, the first one is to carry out an elite-based-reproduction strategy using either the compact genetic algorithm or a learning automata-based algorithm and the second one is the evolution itself driven by the differential evolution algorithm. The proposed approach is applied to both multilayer and radial basis function neural network classifiers. Different testing and training scenarios are presented using two classification benchmark problems, obtained from the UCI repository. Those scenarios are designed to provide an empirical comparison of performances of the two-classifier models and the most suitable elite-based-reproduction strategy used.


Neural Network classifier, Learning, Hybrid Evolutionary Algorithm, Differential Evolution, learning automata, Compact Genetic Algorithm.