Evolution of connection weights combined with local search for multi-layered neural network

The conventional backpropagation algorithm is basically a local search technique which uses the gradient information for convergence. However, it has the problems of local minima and slow convergence. Evolution of the connection weights based on backpropagation can effectively avoid these local minima and speed up the convergence rate. The idea is to perturb the network weights in a controlled manner so as to 'jump off' from the local minima. In this paper, the weight evolution algorithm and the effect of parameters are thoroughly described. A mathematical analysis on the weight evolution algorithm is also included. Simulation results show that the weight evolution algorithm can effectively give fast learning behaviour with global search capability.