An improved method for automatically searching near-optimal artificial Neural Networks

This paper describes an improved version of a method that automatically searches near-optimal multi-layer feedforward artificial neural networks using genetic algorithms. This method employs an evolutionary search for simultaneous choices of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the developed method can produce compact, efficient networks with a satisfactory generalization power and with shorter training times when compared to other methods found in the literature.