Comparison of traditional and evolutionary neural networks for classification

Classification refers to the assignment of a finite set of alternatives into predefined groups. The limitation of the statistical models applied to the classification is that they work well only when the underlying assumptions are satisfied. Neural networks are universal functional approximators so that they can adjust themselves to the data without any explicit specification of functional or distributional form for the underlying model. Because of the difficulty of designing the artificial neural networks; evolutionary algorithms are embedded into artificial neural networks that are robust and probabilistic search strategies excel in large and complex problem spaces. In this thesis, two datasets are classified using evolutionary neural networks. In order to generate an optimal evolutionary neural network of each given dataset, the parameters are optimized including; number of neurons in the hidden layer, stepsize and momentum which makes the classification with high accuracy. Research involving the application of evolutionary algorithms to neural networks for benchmarking the classification performance of training and testing of the datasets with cross validation has been carried out. Performance is benchmarked by mean squared error, normalized mean squared error, mean absolute error, correlation coefficient and true classification rate that is referred to each attribute which is subject to be classified and evaluated with backpropagation and evolutionary neural networks whose parameters are selected using evolutionary algorithms. As argued in the literature; evolutionary neural networks having optimized parameters, get better performance values in classification than the artificial neural networks using the backpropagation algorithm with the same architecture.