Training feed-forward neural networks with ant colony optimization: an application to pattern classification

Abstract

Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Research efforts led to the development of algorithms for the application to continuous optimization problems. In this paper we extend and apply one of the most successful variants for the training of feed-forward neural networks. For evaluating our algorithm we apply it to pattern classification problems from the medical field. The results show that our algorithm is comparable to specialized algorithms for neural network training, and that it has advantages over other general purpose optimizers.