In this paper, we suggest Evolution Algorithms (EA) for development of neural network topologies to find the optimal solution of some problems. Topologies are modified in feed-forward neural networks and in special cases of recurrent neural networks.
We applied two approaches to the tuning of neural networks. One is classical, using evolution principles only. In the other approach, the adaptation phase (training phase) of the neural network is raade in two steps. In the hrst step we use the genetic algorithm to find better than random starting weights (nearly optimal values), in the second step we use the backpropagation algorithm to finish the adaptation phase. This means that the starting weights for the backpropagation algorithm are not random values, but approximately optimum values. In this context, the fitness of a chromosome (neural network) is a function of its estimated test error (its estimated generalization ability).
Some results obtained by these methods are demonstrated in a prediction of Geo-Magnetic Storms (GMS) and Handwriting Recognition (HWR).