Author(s)

S.D. Likothanassis, E. Georgopoulos & D. Fotakis

Abstract

Evolutionary methods have been widely used to determine the structure of artificial neural networks. In this work, we propose a more efficient implementation which face the above problem, by using a neural network model as general as possible. So, we used a fully connected network, consisting of three parts, the input layer, the output layer and a number of hidden layers. This implementation has been proved, via simulations, that it optimazes the size of the network and gives better results compared with other existing methods, while the use of crossover results to more efficient networks. Furthermore, the random initialization has been proved more efficient, since it reduces significantly the numbe

Keywords