Skip to main content

Simultaneous Evolution of Neural Network Topologies and Weights for Classification and Regression

  • Conference paper
Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

Included in the following conference series:

Abstract

Artificial Neural Networks (ANNs)

This work was supported by the ALGORITMI research center and the FCT project POSI/EIA/59899/2004.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cortez, P., Rocha, M., Neves, J.: A Lamarckian Approach for Neural Network Training. Neural Processing Letters 15(2), 105–116 (2002)

    Article  MATH  Google Scholar 

  2. Dietterich, T.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  3. Haykin, S.: Neural Networks - A Compreensive Foundation, 2nd edn. Prentice-Hall, New Jersey (1999)

    Google Scholar 

  4. Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Quebec, Canada (August 1995)

    Google Scholar 

  5. Kwok, T., Yeung, D.: Constructive algorithms for structure learning in feedforward neural networks for regression problems problems: A survey. IEEE Transactions on Neural Networks 8(3), 630–645 (1999)

    Article  Google Scholar 

  6. Liu, Y., Yao, X.: Evolving Modular Neural Networks Which Generalize Well. In: Proc. of the 1997 IEEE Intern. Confer. on Evolutionary Computation, Indianapolis, New York, pp. 670–675. IEEE, Los Alamitos (1997)

    Google Scholar 

  7. Liu, Y., Yao, X., Higuchi, T.: Evolutionary Ensembles with Negative Correlation Learning. IEEE Transactions on Evolutionary Computation 4(4), 380–387 (2000)

    Article  Google Scholar 

  8. Quinlan, J.R.: Comparing Connectionist and Symbolic Learning Methods, pp. 445–456. MIT Press, Cambridge (1994)

    Google Scholar 

  9. Riedmiller, M.: Supervised Learning in Multilayer Perceptrons - from Backpropagation to Adaptive Learning Techniques. Computer Standards and Interfaces 16 (1994)

    Google Scholar 

  10. Rocha, M., Cortez, P.C., Neves, J.: Evolutionary Neural Network Learning. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 24–28. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  11. Rocha, M., Cortez, P., Neves, J.: Ensembles of Artificial Neural Networks with Heterogeneous Topologies. In: Proceedings of the 4th Symposium on Engineering of Intelligent Systems (EIS 2004), ICSC Academic Press (March 2004)

    Google Scholar 

  12. Rocha, M., Cortez, P., Neves, J.: Evolutionary Design of Neural Networks for Classification and Regression. In: ICANNGA Proceedings, Coimbra, Portugal, Springer, Heidelberg (March 2005)

    Google Scholar 

  13. Soares, C.: Is the UCI Repository Useful for Data Mining? In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 209–223. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  14. Thimm, G., Fiesler, E.: Evaluating pruning methods. In: Proc. of the Int. Symp. on Artificial Neural Networks, Taiwan, pp. 20–25 (December 1995)

    Google Scholar 

  15. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, USA (2000)

    Google Scholar 

  16. Yao, X.: Evolving Artificial Neural Networks. Proc. of the IEEE 87(9), 1423–1447 (1999)

    Article  Google Scholar 

  17. Yao, X., Liu, Y.: A New Evolutionary System for Evolving Artificial Neural Networks. IEEE Transactions on Neural Networks 8(3), 694–713 (1997)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rocha, M., Cortez, P., Neves, J. (2005). Simultaneous Evolution of Neural Network Topologies and Weights for Classification and Regression. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_8

Download citation

  • DOI: https://doi.org/10.1007/11494669_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics