A modified differential evolution algorithm and its application in the training of BP neural network

The paper is given a new modified differential evolution (MDE) algorithm in which a novel mutation operator is introduced. The MDE algorithm can obtain a good balance between global search and local search and was applied in BP neural network training. The numerical results demonstrate that the new MDE algorithm has the abilities of good global search and faster convergence speed and higher convergence accuracy. It can overcome the disadvantages of the traditional BP algorithm and reduce the training time and improve the training accuracy.