A Cooperative Evolutionary System for Designing Neural Networks

  • Ben Niu
  • Yunlong Zhu
  • Kunyuan Hu
  • Sufen Li
  • Xiaoxian He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


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.


Neural Network Genetic Algorithm Particle Swarm Optimization Hide Layer Tool Life 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ben Niu
    • 1
    • 2
  • Yunlong Zhu
    • 1
  • Kunyuan Hu
    • 1
  • Sufen Li
    • 1
  • Xiaoxian He
    • 1
    • 2
  1. 1.Shenyang Institute of AutomationChinese Academy of SciencesShenyangChina
  2. 2.Graduate School of the Chinese Academy of SciencesBeijingChina

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