Abstract
Based on the clonal selection theory, a new algorithm, Antibody Clone Simulated Annealing Algorithm, is put forward for optimizing the weights of neural networks. Combining the mechanism of the clonal selection and the simulated annealing, the new algorithm optimizes the weights using a population instead of single point so as to enlarge the searching range and overcome the shortcomings of the simulated annealing algorithm. The effectiveness of the method is proved by the experiments optimizing the weights of the forward neural networks.
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