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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  1. 1.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by ErrorPropagation. The MIT Press, Cambridge (1986)Google Scholar
  2. 2.
    Rumelhar, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-Propagating Errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  3. 3.
    Vogl, T.P., Mangis, J.K., Rigler, A.K., Zink, W.T., Alkon, D.L.: Accelerating the Convergence of the Back-Propagation Method. Biological Cybernetics 59, 257–263 (1988)CrossRefGoogle Scholar
  4. 4.
    Battit, R.: First and Second Order Methods of Learning: Between the Steepest Descent and Newton’s Method. Neural Network 4, 4141–4166 (1991)Google Scholar
  5. 5.
    Stone, J.V., Lister, R.: On the Relative Time Complexities of Standard and Conjugate Gradient Back Propagation. In: Proceedings of IEEE World Congress on Computational Intelligence, pp. 84–87 (1994)Google Scholar
  6. 6.
    Boray, G.K., Srinath, M.D.: Conjugate Gradient Techniques for Adaptive Filtering. IEEE Transactions on Circuits and Systems 39(1), 1–10 (1992)Google Scholar
  7. 7.
    Hagan, M.T., Menhaj, M.B.: Training Feed Forward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks 5, 989–993 (1994)CrossRefGoogle Scholar
  8. 8.
    Gallant, S.: Neural-Network Learning and Expert Systems. MIT Press, Cambridge (1993)zbMATHGoogle Scholar
  9. 9.
    Reed, R.: Pruning algorithms—A Survey. IEEE Transactions on Neural Networks 4, 740–747 (1993)CrossRefGoogle Scholar
  10. 10.
    Angeline, P., Saunders, G., Pollack, J.: An Evolutionary Algorithm That Constructs Recurrent Neural Networks. IEEE Trans. Neural Networks 5, 54–65 (1994)CrossRefGoogle Scholar
  11. 11.
    Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S.: Tuning of the structure and parameters of neural network using an improved genetic algorithm. IEEE Trans. Neural Networks 14(1), 79–88 (2003)CrossRefGoogle Scholar
  12. 12.
    Miller, G.P., Todd, P.M., Hegde, S.U.: Designing neural networks using genetic algorithms. In: Proceedings of the 3rd Int. Conf. Genetic Algorithms Applications, San Mateo, CA, pp. 379–384 (1989)Google Scholar
  13. 13.
    Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 8, 694–713 (1997)CrossRefGoogle Scholar
  14. 14.
    Eberchart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceeding of the 6th international symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  15. 15.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  16. 16.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. Michigan Press, Ann Arbor (1975)Google Scholar
  17. 17.
    Jalali, S.A., Kolarik, W.J.: Tool life and machinability models for drilling steels. International Journal of Machine Tools& Manufacturing 31, 273–282 (1991)CrossRefGoogle Scholar
  18. 18.
    Armarego, E.J.A., Brown, R.H.: The Machining of Metals. Prentice-Hall Inc., Englewood Cliffs (1969)Google Scholar
  19. 19.
    Liu, Q., Altintas, Y.: On-line Monitoring of Flank Wear in Turning with Multi-layered Feed-forward Neural Network. International Journal of Machine Tools& Manufacturing 39, 1945–1959 (1999)CrossRefGoogle Scholar
  20. 20.
    Lee, B.Y., Liu, H.S., Tarng, Y.S.: Abductive Network for Predicting Tool Life in Drilling. IEEE Transactions on Industry Application 35, 190–195 (1999)CrossRefGoogle Scholar
  21. 21.
    Natarajan, U., Saravanan, R., Periasamy, V.M.: Application of particle swarm optimization in artificial neural network for the prediction of tool life. International Journal of Advanced Manufacturing Technology 28, 1084–1088 (2006)CrossRefGoogle Scholar

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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