A cooperative evolutionary system for designing neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4113 LNCS - I, Issue , 2006, Pages 12-21

  Niu, Ben ^a,b   Zhu, Yunlong ^a   Hu, Kunyuan ^a   Li, Sufen ^a   He, Xiaoxian ^a,b  

^a SHENYANG INSTITUTE OF AUTOMATION   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CODES; GENETIC ALGORITHMS; OPTIMIZATION;

COOPERATIVE EVOLUTIONARY SYSTEM; GENERALIZATION; PARTICLE SWARM OPTIMIZATION (PSO);

NEURAL NETWORKS;

EID: 33749546271     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11816157_2     Document Type: Conference Paper

References (21)

1. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by ErrorPropagation. The MIT press, Cambridge, Massachusetts (1986)
2. Rumelhar, D.E., Hinton, G.E., Williams, R. J.: Learning Representations by Back-Propagating Errors. Nature. 323 (1986)533-536
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(1988)257-263
4. Battit, R: First and Second Order Methods of Learning: Between the Steepest Descent and Newton's Method. Neural Network. 4(1991)141-166
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, Orlando Florida USA (1994)84-87
6. Boray, G.K., Srinath, M.D.: Conjugate Gradient Techniques for Adaptive Filtering. IEEE Transactions on Circuits and Systems. 39(1) (1992)1-10.
7. Hagan, M.T., Menhaj, M.B.: Training Feed Forward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks. 5 (1994) 989-993
8. Gallant, S.: Neural-Network Learning and Expert Systems. Cambridge, MA: MIT Press (1993)
9. Reed, R.: Pruning algorithms - A Survey. IEEE Transactions on Neural Networks 4(1993)740-747
10. Angeline, P., Saunders, G., Pollack, J.: An Evolutionary Algorithm That Constructs Recurrent Neural Networks. IEEE Trans. Neural Networks. 5 (1994) 54-65
11. Leung, F.H.F., Lam, H.K., Ling, S.H., and Tam, P. K. S.: Tuning of the structure and parameters of neural network using an improved genetic algorithm. IEEE Trans. Neural Networks. 14 (1) (2003)79-88
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. (1989) 379-384.
13. Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 8 (1997) 694-713
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 (1995) 39-43
15. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: proceeding, of IEEE International Conference on Neural Networks, Piscataway, NJ (1995) 1942-1948
16. Holland, J. H.: Adaptation in Natural and Artificial Systems, Ann Arbor, MI: Univ. Michigan Press (1975)
17. Jalali, S.A., Kolarik, W.J.: Tool life and machinability models for drilling steels. International Journal of Machine Tools& Manufacturing 31 (1991) 273-282.
18. Armarego, E.J.A., Brown, R.H.: The Machining of Metals. Prentice-Hall Inc., Englewood Cliffs, NJ (1969)
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 (1999) 1945-1959
20. Lee, B.Y., Liu, H.S., Tarng, Y.S.: Abductive Network for Predicting Tool Life in Drilling. IEEE Transactions on Industry Application. 35 (1999) 190-195
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 (2006) 1084-1088