Multiple Layer Perceptron training using genetic algorithms

Udo Seiffert
2001 The European Symposium on Artificial Neural Networks  
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
more » ... tions 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.
dblp:conf/esann/Seiffert01 fatcat:ixcarurfzzghdd23iovnxu3lim