A cooperative evolutionary system for designing neural networks

Authors:
Ben Niu;Yunlong Zhu;Kunyuan Hu;Sufen Li;Xiaoxian He
Affiliations:
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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Abstract

A novel cooperative evolutionary system, i.e., CGPNN, for automatic design artificial neural networks (ANN’s) is presented where ANN’s structure and parameters are tuned simultaneously. The algorithms used in CGPNN combine genetic algorithm (GA) and particle swarm optimization (PSO) on the basis of a direct encoding scheme. In CGPNN, standard (real-coded) PSO is employed to training ANN’s free parameters (weights and bias) and binary-coded GA is used to find optimal ANN’s structure. In the simulation part, CGPNN is applied to the predication of tool life. The experimental results show that CGPNN has good accuracy and generalization ability in comparison with other algorithms.