Effectiveness of differential evolution in training radial basis function networks for classification

Building classification models often presents a significant problem that requires the selection of a classifier and a corresponding training approach. Radial basis function networks are a frequent choice among the classifiers for which a large spectre of training approaches exist. In that regard, an important role is played by bio-inspired methods, and differential evolution, as an representative example, has been applied for training such networks. This paper investigates the behaviour of differential evolution in training radial basis function networks primarily from the perspective of fitting the model to available (training) data rather than its performance on unknown (testing) data. This is believed to provide a clearer insight into optimiser efficiency. Another important issue considered is a steady emergence of new bio-inspired methods claiming superior performance that can be witnessed in the literature. It may raise the question whether differential evolution is still competitive to those approaches. In light of this, the canonical differential evolution algorithm has been compared to a couple of recently proposed and a well established swarm intelligence algorithm.