Competitive co-evolution of multi-layer perceptron classifiers

This paper analyses the competitive approach to the co-evolutionary training of multi-layer perceptron classifiers. Two algorithms were tested: the first opposes a population of classifiers to a population of training patterns; the second pits a population of classifiers against a population of subsets of training patterns. The classifiers are regarded as predators that need to ‘capture’ (correctly categorise) the prey (training patterns). Success for the predators is measured on their ability to capture prey. Success for the prey is measured on their ability to escape predation (be misclassified). The aim of the procedure is to create an evolutionary tug-of-war between the best classifiers and the most difficult data samples, increasing the efficiency and accuracy of the learning process. The two co-evolutionary algorithms were tested on a number of well-known benchmarks and on several artificial data sets modelling different kinds of common classification problems such as overlapping data categories, noisy training inputs, and unbalanced data classes. The performance of the co-evolutionary methods was compared with that of two traditional training techniques: the standard backpropagation rule and a conventional evolutionary algorithm. The co-evolutionary procedures achieved top accuracy in all classification problems. They particularly excelled on data sets containing noisy training inputs, where they outperformed the backpropagation rule, and on tasks involving unbalanced data classes, where they outperformed both backpropagation and the conventional evolutionary algorithm. Compared to the standard evolutionary algorithm, the co-evolutionary procedures were able to obtain similar or superior learning accuracies, whilst needing considerably less presentations of the training patterns. This economy in the use of training patterns translated into significant savings in computational overheads and algorithms running time