关键词: 递阶遗传算法, 粒子群算法, 误差反向传播（BP）算法, 人工神经网络, 优化, 混沌时间序列

Abstract: The Hierarchical Genetic Particle Swarm Optimization Algorithm（HGAPSO） is introduced into neural network design，and then the structure and weights of neural network are optimized simultaneously.The advantage of GA to solve the discrete problem and that of PSO to solve the continuous problem are combined.Then it makes use of the steepest descent error of BP to learn the network’s weights，so global optimization and fast searching are reached.The results of “the chaotic time series prediction” demonstrate that Hierarchical Genetic Particle Swarm Optimization algorithm improves the learning performance and generalization ability of neural network to a large degree.

Key words: hierarchical genetic algorithm, particle swarm optimization algorithm, Error Back Propagation（BP） algorithm, artificial neural network, optimization, chaotic time series