An evolution-oriented learning algorithm for the optimal interpolative net

An evolution-oriented learning algorithm is presented for the optimal interpolative (OI) artificial neural net proposed by R. J. P. deFigueiredo (1990). The algorithm is based on a recursive least squares training procedure. One of its key attributes is that it incorporates in the structure of the net the smallest number of prototypes from the training set T necessary to correctly classify all the members of T. Thus, the net grows only to the degree of complexity that it needs in order to solve a given classification problem. It is shown how this approach avoids some of the difficulties posed by the backpropagation algorithm because of the latter´s inflexible network architecture. The performance of this new algorithm is demonstrated by experiments with real data, and comparisons with other methods are also presented