A Hybrid Method for Searching Near-Optimal Artificial Neural Networks

This paper describes a method for searching nearoptimal neural networks using Genetic Algorithms. The method uses an evolutionary search with the simultaneous selection of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the method is able to produce compact, efficient networks with satisfactory generalization power and shorter training times in comparison to other algorithms.