An Ameliorated GA Used to Adjust Weights in Feedforward Networks
Shun Mao · Journalof Southwest China Normal University · 2002
An ameliorated GA that imports gradient descent methods is used to learn the training set and adjust the weights in Feedforward networks. Several important modules of the algorithm are described. Using the mutation and global optimize, the algorithm can find potential extremum; using gradient descent methods, it can quickly converge at these points. As simulation shows, the new algorithm has a much higher converging speed than artless GA and an evidently improved learning quality than traditional algorithm as well.