Reliable classification using neural networks: a genetic algorithm and backpropagation comparison

Abstract

Although, the genetic algorithm (GA) has been shown to be a superior neural network (NN) training method on computer-generated problems, its performance — on real world classification data sets is untested. To gain confidence that this alternative training technique is suitable for classification problems, a collection of 10 benchmark real world data sets were used in an extensive Monte Carlo study that compares backpropagation (BP) with the GA for NN training. We find that the GA reliably outperforms the commonly used BP algorithm as an alternative NN training technique. While this does not prove that the GA will always dominate BP, this demonstrated reliability with real world problems enables managers to use NNs trained with GAs as decision support tools with a greater degree of confidence.