Evolving Neural Network Structure by Indirect Encoding Based on BQPSO
This paper proposes a novel algorithm of neural network structure evolve. First, the algorithm designs an indirect encoding schema representing the structure of neural network, use joint seed representing the existence of connection in neural network. Then, creating and evolving the coordinates of the joint seed using Binary Quantum-behaved Particle Swarm Optimization (BQPSO), evolving the value of the joint seed using nine-palace evolving rule, by separately evolve the coordinates and value of the joint seed, the growing and pruning of the network structure is achieved. The experimental results show that the algorithm has stable complexity when dealing with different scales of neural network. By the proposed indirect encoding schema and separated coordinates and value evolving, the algorithm solves the problem of geometrically growing structure-evolving complexity successfully.
Keywords: neural network structure evolve indirect
Fang Bao Jun Sun Wenbo Xu
Jiangyin polytechnic college. No.168, xicheng road,Jiangyin Jiangsu, China 214405 School of Internet of Things Engineering, SouthYangtzuniversity. No. 1800, Lihudadao, Wuxi Jiangsu, School of Internet of Things Engineering, SouthYangtz university. No. 1800, Lihudadao, Wuxi Jiangsu,
国际会议
无锡
英文
297-302
2011-10-14（万方平台首次上网日期，不代表论文的发表时间）