Tuning of neural networks based on genetic algorithm and statistical learning theory

Based on the statistical learning theory and support vector machines, a novel fitness function is constructed according to the structural risk minimization principle. Then, a new hybrid genetic process is presented and implemented in real coding. This new hybrid genetic process is used to optimize neural networks, and a classification task is taken for an example to examine the performance of the new hybrid genetic algorithm. The simulation results are compared with those obtained from the neural networks trained by the previous genetic algorithm. The hybrid genetic algorithm based on the statistical learning theory proposed in this paper shows better generalization ability in testing sample set.