Automatically searching near-optimal artificial neural networks
Leandro Maciel Almeida, Teresa B. Ludermir · The European Symposium on Artificial Neural Networks · 2007
The idea of automatically searching neural networks that learn faster and generalize better is becoming increasingly widespread. In this paper, we present a new method for searching near-optimal artificial neural networks that include initial weights, transfer functions, architec- tures and learning rules that are specially tailored to a given problem. Experimental results have shown that the method is able to produce com- pact, efficient networks with satisfactory generalization power and shorter training times.