Abstract
This paper proposes a co-evolutionary learning system, i.e., CELS, to design neural network (NN) ensembles. CELS addresses the issue of automatic determination of the number of individual NNs in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of CELS is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn the whole training data better. The cooperation and specialisation among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialise. Experiments on two real-world problems demonstrate that CELS can produce NN ensembles with good generalisation ability.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
X. Yao and Y. Liu, “Making use of population information in evolutionary artificial neural networks,” IEEE Transactions on Systems, Man and Cybernetics, 28B(3),pp.417–425, June 1998.
Y. Liu and X. Yao, “Negatively correlated neural networks can produce best ensembles,” Australian Journal of Intelligent Information Processing Systems, 4(3/4), pp.176–185, 1997.
Y. Liu and X. Yao, “A cooperative ensemble learning system,” Proc. of the 1998 IEEE International Joint Conference on Neural Networks (IJCNN'98), Anchorage, USA, 4–9 May 1998, pp.2202–2207.
X. Yao, Y. Liu and P. Darwen, “How to make best use of evolutionary learning,” Complex Systems — From Local Interactions to Global Phenomena, IOS Press, Amsterdam, pp.229–242, 1996.
X. Yao and Y. Liu, “A new evolutionary system for evolving artificial neural networks,” IEEE Transactions on Neural Networks, Vol.8, no.3, pp.694–713, May 1997.
D. B. Fogel, Evolutionary Computation: Towards a New Philosophy of Machine Intelligence, IEEE Press, New York, 1995.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,” Neural Computation, Vol.3, pp.79–87, 1991.
S. Haykin, Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, Inc., pp.151–152, 1994.
P. Darwen and X. Yao, “Every niching method has its niche: fitness sharing and implicit sharing compared,” Parallel Problem Solving from Nature (PPSN) IV (H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, eds.), Vol. 1141 of Lecture Notes in Computer Science, (Berlin), pp. 398–407, Springer-Verlag, 1996.
J. MacQueen, “Some methods for classification and analysis of multivariate observation,” Proceedings of the 5th Berkely Symposium on Mathematical Statistics and Probability, Berkely: University of California Press, Vol.1, pp.281–297, 1967.
D. Michie and D. J. Spiegelhalter and C. C. Taylor, Machine Learning, Neural and Statistical Classification, Ellis Horwood Limited, London, 1994.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Y., Yao, X. (1998). Towards designing neural network ensembles by evolution. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056904
Download citation
DOI: https://doi.org/10.1007/BFb0056904
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65078-2
Online ISBN: 978-3-540-49672-4
eBook Packages: Springer Book Archive