Architecture optimization of radial basis function networks with a combination of hard- and soft-computing techniques

Feature selection and architecture optimization are two key tasks in many neural network applications. Appropriate input features must be selected from a given (and often large) set of possible features and architecture parameters of the network such as the number of hidden neurons or training parameters must be adapted with respect to the selected features and a data set given. This article describes an evolutionary algorithm (EA) that performs the two tasks simultaneously for radial basis functions (RBF) networks applied to classification problems. In order to reduce the optimization effort significantly these soft-computing techniques are focused with various hard-computing techniques (e.g., clustering, solution of a linear least-squares problem, local search). The feasibility and the benefits of the approach are demonstrated by means of a data mining and knowledge discovery problem in the area of customer relationship management. The algorithm, however, is independent from specific applications such that the ideas and solutions may easily be transferred to other applications and even other neural network paradigms.