Hierarchical rank density genetic algorithm for radial-basis function neural network design

In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A hierarchical rank density genetic algorithm (HRDGA) is used to evolve both the neural network's topology and parameters. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the training performance and network complexity. Instead of producing a single optimal network, HRDGA provides a set of near-optimal neural networks to the designers or the decision makers so that they can have more flexibility for the final decision-making based on their preferences. In terms of searching for a near-complete set of candidate networks with high performances, the networks designed by the proposed algorithm prove to be competitive, or even superior, to three selected traditional radial-basis function networks for predicting Mackey-Glass chaotic time series.