A novel mixture of experts model based on cooperative coevolution

Neurocomputing

Volumn 70, Issue 1-3, 2006, Pages 155-163

  Nguyen, Minh Ha ^a   Abbass, Hussein A ^a   Mckay, Robert I ^b  

^a Australian Government Australian Research Council   (Australia)

^b Seoul National University   (South Korea)

Author keywords

Artificial neural networks; Cooperative coevolution; Mixture of experts; Neuro ensemble

Indexed keywords

EVOLUTIONARY ALGORITHMS; EXPERT SYSTEMS; GATEWAYS (COMPUTER NETWORKS);

COOPERATIVE COEVOLUTIONARY ALGORITHM; MIXTURE OF EXPERTS; NEURO-ENSEMBLE;

NEURAL NETWORKS;

ALGORITHM; ANALYSIS OF VARIANCE; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; COEVOLUTION; COMPUTER SYSTEM; CONTROLLED STUDY; MATHEMATICAL COMPUTING; NONBIOLOGICAL MODEL; PRIORITY JOURNAL; PROBLEM SOLVING; TASK PERFORMANCE;

EID: 33750996171     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.04.009     Document Type: Article

References (34)

1. Alexandre L., Campilho A., and Kamel M. Bounds for the average generalization error of the mixture of experts neural network. In: Fred A. (Ed). SSPR/SPR (2004), Springer, Berlin 618-625
2. Avnimelech R., and Intrator N. Boosted mixture of experts: an ensemble learning scheme. Neural Comput. 11 2 (1999) 483-497
3. Bishop C.M. Neural Networks for Pattern Recognition (1995), Oxford University Press, Oxford
4. C. Blake, C. Merz, Uci repository of machine learning databases. 〈html://www.ics.uci.edu/mlearn/mlrepository.html〉, 1998.
5. Chellapilla K., and Fogel D. Evolution, neural networks, games, and intelligence. Proc. IEEE 87 9 (1999) 1471-1496
6. Garcia-Pedrajas N., Hervas-Martinez C., and Ortiz-Boyer D. Cooperative coevolution of artificial neural network ensembles for pattern recognition. IEEE Trans. Evol. Comput. 9 3 (2005) 271-302
7. Haykin S. Neural Networks: A Comprehensive Foundation (1999), Prentice-Hall, Englewood Cliffs, NJ
8. R. Jacobs, M. Jordan, A. Barto, Task decomposition through competition in a modular connectionist architecture: the what and where vision tasks, Technical Report, University of Massachusetts, 1991.
9. Jacobs R., Jordan M., Nowlan S., and Hinton G. Adaptive mixtures of local experts. Neural Comput. 3 (1991) 79-87
10. Jimenez D., and Walsh N. Dynamically weighted ensemble neural networks for classification. Proceedings of the 1998 International Joint Conference on Neural Networks (1998) 753-756
11. Jordan M., and Jacobs R. Hierarchies of adaptive experts. In: Moody J., Hanson S., and Lippmann R. (Eds). Advances in Neural Information Processing Systems vol. 4 (1992), Morgan Kaufmann, Los Altos, CA 985-992
12. Jordan M., and Jacobs R. Hierachical mixtures of experts and the EM algorithm. Neural Comput. 6 2 (1994) 181-214
13. M.I. Jordan, L. Xu, Convergence results for the EM approach to mixtures of experts architectures, Artificial Intelligence Laboratory@MIT, November 1993.
14. Kang K., and Oh J.-H. Statistical mechanics of the mixture of experts. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds). Advances in Neural Information Processing Systems vol. 9 (1997), The MIT Press, Cambridge, MA 183 URL: 〈citeseer.ist.psu.edu/kang97statistical.html〉
15. Khare V., Yao X., Sendhoff B., Jin Y., and Wersing H. Co-evolutionary modular neural networks for automatic problem decomposition. Proceedings of the 2005 IEEE Congress on Evolutionary Computation (2005), IEEE Press, New York 2691-2698
16. Liu Y., and Yao X. Evolving modular neural networks which generalise well. Proceedings of 1997 IEEE International Conference on Evolutionary Computation (1997), Indianapolis, USA 605-610
17. Liu Y., and Yao X. Ensemble learning via negative correlation. Neural Networks 12 (1999) 1399-1404
18. Liu Y., Yao X., and Higuchi T. Evolutionary ensembles with negative correlation learning. IEEE Trans. Evol. Comput. 4 4 (2000) 380-387
19. Moeland P. Mixtures of experts estimate a posteriori probabilities. Proceedings of the International Conference on Artificial Neural Networks (1997) 499-504
20. Nguyen M.H., Abbass H.A., and McKay R.I. Stopping criteria for ensemble of evolutionary artificial neural networks. Applied Soft Computing (2005), Elsevier, Amsterdam 100-107
21. Potter M., and De Jong K. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8 1 (2000) 1-29
22. M.A. Potter, The design and analysis of a computational model of cooperative coevolution, Ph.D. Thesis, George Mason University, 1997.
23. Rosen B. Ensemble learning using decorrelated neural networks. Connect. Sci. Spec. Issue Combining Artif. Neural: Ensemble Approaches 8 3/4 (1996) 373-384
24. Schwefehm H. Numerical Optimization for Computer Models (1981), Wiley, Chichester, UK
25. Sharkey A. Combining Artificial Neural Nets. Ensemble and Modular Multi-Net Systems (1998), Springer, New York
26. Tang B., Heywood M., and Shepherd M. Input partitioning to mixture of experts. International Joint Conference on Neural Networks (2002) 227-232
27. E. Wan, D. Bone, Interpolating earth-science data using rbf networks and mixtures of experts, in: NIPS, 1996, pp. 988-994.
28. S. Waterhouse, Divide and conquer pattern recognition using mixtures of experts, Ph.D. Thesis, University of Cambridge, February 1997.
29. Waterhouse S., and Cook G. Ensemble methods for phoneme classification. In: Mozer M., Jordan J., and Petsche T. (Eds). Advances in Neural Information Processing Systems vol. 9 (1997), The MIT Press, Cambridge, MA 800-806
30. Waterhouse S., MacKay D., and Robinson T. Bayesian methods for mixture of experts. In: Touretzky D.S., Mozer M.C., and Hasselmo M.E. (Eds). Advances in Neural Information Processing Systems vol. 8 (1996), The MIT Press, Cambridge, MA 351-357
31. Yao X. Evolving artificial neural networks. Proc. IEEE 87 (1999) 1423-1447
32. Yao X., and Liu Y. Ensemble structure of evolutionary artificial neural networks. IEEE International Conference on Evolutionary Computation (ICEC'96) (1996), Nagoya, Japan 659-664
33. Yao X., and Liu Y. A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 8 (1997) 694-713
34. Yao X., and Liu Y. Making use of population information in evolutionary artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 28 (1998) 417-425