Abstract
The new genetic algorithm for training layered feedforward neural networks proposed here uses a mutation operator for performing the search behaviors of local optimization. Combining the random restart method with the local search technique, the algorithm can converge asymptocally, to the optimal solution. Test with a practical example showed that the improved genetic algorthm is more efficient than the conventional genetic algorithm.
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Project supported by NSFC (No 39870940) and (G199054405-973) the National Key Scientific Research & Development Program.
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Ping, L., Yi-yu, C. An improved genetic algorithm for training layered feedforward neural networks. J. Zhejiang Univ. Sci. A 1, 322–326 (2000). https://doi.org/10.1631/BF02910644
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DOI: https://doi.org/10.1631/BF02910644