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.
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References
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by ErrorPropagation. The MIT Press, Cambridge (1986)
Rumelhar, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-Propagating Errors. Nature 323, 533–536 (1986)
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)
Battit, R.: First and Second Order Methods of Learning: Between the Steepest Descent and Newton’s Method. Neural Network 4, 4141–4166 (1991)
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)
Boray, G.K., Srinath, M.D.: Conjugate Gradient Techniques for Adaptive Filtering. IEEE Transactions on Circuits and Systems 39(1), 1–10 (1992)
Hagan, M.T., Menhaj, M.B.: Training Feed Forward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks 5, 989–993 (1994)
Gallant, S.: Neural-Network Learning and Expert Systems. MIT Press, Cambridge (1993)
Reed, R.: Pruning algorithms—A Survey. IEEE Transactions on Neural Networks 4, 740–747 (1993)
Angeline, P., Saunders, G., Pollack, J.: An Evolutionary Algorithm That Constructs Recurrent Neural Networks. IEEE Trans. Neural Networks 5, 54–65 (1994)
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)
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)
Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 8, 694–713 (1997)
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)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. Michigan Press, Ann Arbor (1975)
Jalali, S.A., Kolarik, W.J.: Tool life and machinability models for drilling steels. International Journal of Machine Tools& Manufacturing 31, 273–282 (1991)
Armarego, E.J.A., Brown, R.H.: The Machining of Metals. Prentice-Hall Inc., Englewood Cliffs (1969)
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)
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)
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)
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Niu, B., Zhu, Y., Hu, K., Li, S., He, X. (2006). A Cooperative Evolutionary System for Designing Neural Networks. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_2
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DOI: https://doi.org/10.1007/11816157_2
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