Abstract
This paper uses a hybrid genetic learning algorithm to train Pi-sigma neural network and this algorithm was once applied to resolve a function optimizing problem. The hybrid genetic learning algorithm incorporates the stronger global search of genetic algorithm into the stronger local search of flexible polyhedron method, and can search out the global optimum faster than standard genetic algorithm. The experiments show that the hybrid genetic algorithm can achieve better performance. At last, the hybrid genetic algorithm is proved converge to the global optimum with the probability of 1.
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