Multiple Layer Perceptron training using genetic algorithms

Multiple Layer Perceptron networks trained with backpropagation algorithm are very frequently used to solve a wide variety of real-world problems. Usually a gradient descent algorithm is used to adapt the weights based on a comparison between the desired and actual network response to a given input stimulus. All training pairs, each consisting of input vector and desired output vector, are forming a more or less complex multi-dimensional error surface during the training process. Numerous suggestions have been made to prevent the gradient descent algorithm from becoming captured in any local minimum when moving across a rugged error surface. This paper describes an approach to substitute it completely by a genetic algorithm. By means of some benchmark applications characteristic properties of both the genetic algorithm and the neural network are explained.