Comparing performances of backpropagation and genetic algorithms in the data classification

Artificial neural networks (ANN) have a wide ranging usage area in the data classification problems. Backpropagation algorithm is classical technique used in the training of the artificial neural networks. Since this algorithm has many disadvantages, the training of the neural networks has been implemented with the binary and real-coded genetic algorithms. These algorithms can be used for the solutions of the classification problems. The real-coded genetic algorithm has been compared with other training methods in the few works. It is known that the comparison of the approaches is as important as proposing a new classification approach. For this reason, in this study, a large-scale comparison of performances of the neural network training methods is examined on the data classification datasets. The experimental comparison contains different real classification data taken from the literature and a simulation study. A comparative analysis on the real data sets and simulation data shows that the real-coded genetic algorithm may offer efficient alternative to traditional training methods for the classification problem.