Abstract
A back-propagation (BP) neural network has good self-learning, self-adapting and generalization ability, but it may easily get stuck in a local minimum, and has a poor rate of convergence. Therefore, a method to optimize a BP algorithm based on a genetic algorithm (GA) is proposed to speed the training of BP, and to overcome BP’s disadvantage of being easily stuck in a local minimum. The UCI data set is used here for experimental analysis and the experimental result shows that, compared with the BP algorithm and a method that only uses GA to learn the connection weights, our method that combines GA and BP to train the neural network works better; is less easily stuck in a local minimum; the trained network has a better generalization ability; and it has a good stabilization performance.
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Ding, S., Su, C. & Yu, J. An optimizing BP neural network algorithm based on genetic algorithm. Artif Intell Rev 36, 153–162 (2011). https://doi.org/10.1007/s10462-011-9208-z
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DOI: https://doi.org/10.1007/s10462-011-9208-z