Designing a phenotypic distance index for radial basis function neural networks

Abstract

MultiObjective Evolutionary Algorithms (MOEAs) may cause a premature convergence if the selective pressure is too large, so, MOEAs usually incorporate a niche-formation procedure to distribute the population over the optimal solutions and let the population evolve until the Pareto-optimal region is completely explored. This niche-formation scheme is based on a distance index that measures the similarity between two solutions in order to decide if both may share the same niche or not. The similarity criterion is usually based on a Euclidean norm (given that the two solutions are represented with a vector), nevertheless, as this paper will explain, this kind of metric is not adequate for RBFNNs, thus being necessary a more suitable distance index. The experimental results obtained show that a MOEA including the proposed distance index is able to explore sufficiently the Pareto-optimal region and provide the user a wide variety of Pareto-optimal solutions. © Springer-Verlag Berlin Heidelberg 2003.