A methodology to train and improve artificial neural networks' weights and connections

Abstract

This work presents a new methodology that integrates the heuristics Tabu search, simulated annealing, genetic algorithms and backpropagation in a pruning and constructive way. The approach obtained promising results in the simultaneous optimization of artificial neural network architecture and weights. The experiments were performed in four classification and one prediction problem.