Abstract
BP (backpropagation algorithm) and GA (genetic algorithm) are among the most effective algorithms of neural networks (NN). As deterministic gradient-descent algorithm and stochastic optimizing algorithm respectively, there exists great compatibility between their advantages and disadvantages. The proposed hybrid BP-GA learning method for multilayer feedforward neural networks blends the merits of both BP and GA. Based on BP-GA, a two-layer feedforward neural network is designed. HSPICE simulation results have proved its ability to solve the XOR problem.
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