Evolution strategies on connection weights into modified gradient function for multi-layer neural networks

In this paper, two modifications on the conventional back-propagation algorithm for feedforward multi-layer neural networks are presented. One modification is based on the calculation of the gradient function, while the other one is the use of evolution strategies on connection weights into the gradient search algorithm. From simulation results, the new modified algorithm always converges to the global optimal solution with better performance when compared with other fast learning algorithms and global search methods.