A new hybrid neural-genetic methodology for improving learning

A new hybrid neural-generic methodology is presented that exploits the optimization advantages of genetic algorithms for the purpose of accelerating neural network training. The choice of fitness function is addressed and experimental findings are shown where neural network training is improved through the proposed approach. The results suggest that genetic algorithms can be a powerful tool for improving learning in neural networks