A hybrid algorithm of weight evolution and generalized back-propagation for finding global minimum

Concerns feedforward neural net training. The conventional backpropagation algorithm will always get stuck into local minima and converge very slowly. Other fast algorithms can increase the convergence speed however they still converge to local minima. We introduce a new hybrid algorithm with the use of weight evolution into the generalized back-propagation method. The hybrid algorithm further improve the convergence rate and the global convergence capability, it ensures the convergence to a global minimum in a compact region of a weight vector space