The optimization of radial basis function network based on chaos immune genetic algorithm

This paper presents a hybrid algorithm which combines chaos, immune and genetic algorithm to design the radial basis function neural networks. We use the chaos variable which has the characters of pseudo-randomness and irregularity in chaos theory to generate the initial population, ensuring the initial solutions would map into the whole solution space. Moreover, by introducing the affinity calculated operation in immune algorithm to keep the diversity of population during the evolution. Finally, we use the trained RBF networks on an artificial problem with uniform input distribution, a real-world non-uniform with higher dimensional benchmark problem and Mackey-Glass time series problem. The results show a good generalization capability compared with other training methods.