Optimization and training of feedforward neural networks by genetic algorithms

The training of feedforward neural networks by backpropagation requires much time-consuming experimentation by the network designer. The authors use the genetic algorithm formalism to optimize network structure and training parameters automatically, so as to allow successful back-propagation learning. Additionally, they describe a method to optimize network weights directly using the genetic algorithm, removing any need for a gradient-descent algorithm such as back-propagation