Abstract
BP Algorithm, as a well-known training method for artificial neural network, has been widely used in all the main fields of science and engineering. However, owing to the overwhelming dependency on gradient of loss function, BP-ANN still suffers several drawbacks, for example, the training process is prone to stuck at local optima, cause early convergence while the whole process is lack of generalization performance. Aimed at improving the current BP training Algorithm, a new algorithm called GENOUD-BP is proposed in this paper by introducing the GENOUD algorithm which combines the global searching power of genetic algorithms and convergence speed of traditional gradient based optimization algorithms. Two UCI datasets are employed to carry out benchmark experiments, the result of which shows that the GENOUD-BP significantly outperforms traditional BP algorithms.
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