GINN (Genetic Inside Neural Network): towards a non-parametric training

A genetic algorithm (steady state evolutionary algorithm without duplication) to train neural networks for classification problems is investigated. The algorithm is based on direct optimization of frequencies of both misclassifications and number of correct classifications with special attention to the parameters known as sensibility and specificity. This way, it is possible to find more information about the behaviour of artificial neural networks on classification.