In this chapter, we present a multiobjective evolutionary algorithm based design procedure for radial-basis function neural networks. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is proposed to evolve the neural network’s topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to tradeoff between the training performance and network complexity. Instead of producing a single
solution, HRDGA provides a set of near-optimal neural networks to the designers so that they can have more flexibility for the final decision-making based on certain 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 state-of-the-art designs for radial-basis function neural networks to predict Mackey-Glass chaotic time series.