Skip to main content
SpringerLink
Log in

Application of Genetic Algorithms to the Construction of Topologies for Multilayer Perceptrons

  • Conference paper
Artificial Neural Nets and Genetic Algorithms

Abstract

In this paper we present a new approach for automatic topology optimization of backpropagation networks. It is based on a genetic algorithm. In contrast to other approaches it allows that two networks with different number of units can be crossed to a new valid “child” network. We applied this algorithm to a medical classification task, which is extremely difficult to solve. The results confirm, that optimization make sence, because the generated network outperform all fixed topologies.

This work is supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the project FE-generator (grant Schi 304/1-1)

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. Minsky and Papert, Perceptrons, MIT Press, 1969

    Google Scholar 

  2. Rumelhart D.E., Hinton G.E. and Williams R.J., Learning internal representations by error propagation, in Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol.I, MIT Press, pp. 318–362, 1986

    Google Scholar 

  3. Hornik K., Stinchcombe M. and White H., Multilayer Feedforward Networks are Universal Approximators, Neural Networks, Vol. 2, pp. 359–366, Pergamon Press, 1989

    Article  Google Scholar 

  4. Miller G., Todd P.M. and Hegde S.U., Designing Neural Networks Using Genetic Algorithms, Proc. of the third Intern. Conf. on Genetic Algorithms (ICGA), pp. 379-384, San Mateo (CA), 1989

    Google Scholar 

  5. Schiffmann W.H. and Mecklenburg K., Genetic Generation of Backpropagation Trained Neural Networks, Proc. of Parallel Processing in Neural Systems and Computers (ICNC), Eckmiller R. et al. (Eds.), pp. 205-208, Elsevier, 1990

    Google Scholar 

  6. Schiffmann W.H., Selbstorganisation neuronaler Netze nach den Prinzipien der Evolution, Fachbericht 7/1989, Universität Koblenz

    Google Scholar 

  7. Schiffmann W.H., Joost M. and Werner R., Performance Evaluation of Evolutionarily Created Neural Network Topologies, Proc. of Parallel Problem Solving from Nature, Schwefel H.P. and Maenner R. (Eds.), pp. 274-283, Lect. Notes in Computer Science, Springer, 1991

    Google Scholar 

  8. Whitley D., Applying Genetic Algorithms to Neural Net Problems, Neural Networks, Vol. 1, p. 230, 1988

    Article  Google Scholar 

  9. Whitley D. and Hanson T., Optimizing Neural Networks Using Faster, More Accurate Genetic Search, Proc. of the third Intern. Conf. on Genetic Algorithms (ICGA), pp. 391-396, San Mateo (CA), 1989

    Google Scholar 

  10. Whitley D. and Bogart C., The Evolution of Connectivity: Pruning Neural Networks Using Genetic Algorithms, Proc. of the intern. Joint Conf. on Neural Networks, Vol. I, pp. 134–137, 1990

    Google Scholar 

  11. Whitley D., Starkweather and Bogart C., Genetic Algorithms and Neural Networks: Optimizing Connections and Connectivity, Parallel Computing Vol. 14, pp. 347–361, Elsevier, 1990

    Article  Google Scholar 

  12. Whitley D., The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best, Proc. of the third Intern. Conf. on Genetic Algorithms (ICGA), pp. 116-121, San Mateo (CA), 1989

    Google Scholar 

  13. Heistermann J., Parallel Algorithms for Learning in Neural Networks with Evolution Strategy, Parallel Computing, Vol. 12, 1989

    Google Scholar 

  14. Heistermann J., Learning in Neural Nets by Genetic Algorithms, Proc. of Parallel Processing in Neural Systems and Computers (ICNC), Eckmiller R. et al. (Eds.), pp. 165-168, Elsevier, 1990

    Google Scholar 

  15. Montana D. and Davis C., Training Feedforward Neuronal Networks Using Genetic Algorithms, Technical Report, BBN Systems and Technologies Inc., Cambridge (MA), 1989

    Google Scholar 

  16. de Garis H., Genetic Programming — Modular Neural Evolution for Darwin Machines, Proc. of the Intern. Joint Conf. on Neural Networks, Vol I, pp. 194–197, 1990

    Google Scholar 

  17. Weiss, G., Combining neural and evolutionary learning: Aspects and approaches, Report FKI-132-90, Technische Universität München, 1990

    Google Scholar 

  18. Kitano H., Empirical Studies on the Speed of Convergence of Neural Network Training Using Genetic Algorithms, Proc. of the National Conf. of the American Association of Artificial Intelligence (AAAI), pp. 789-795, 1990

    Google Scholar 

  19. Dodd N., Optimization of Network Structure Using Genetic Techniques, Proc. of the Intern. Conf. on Neural Networks, pp. 693-696, Paris, 1990

    Google Scholar 

  20. Lehar S. and Weaver J., A Developmental Approach to Neural Network Design, Proc. of the IEEE Intern. Conf. on Neural Networks, Vol. I, pp. 97–104, 1987

    Google Scholar 

  21. Harp S.A., Samad T. and Guha A., Towards the Genetic Synthesis of Neural Networks, Proc. of the third Intern. Conf. on Genetic Algorithms (ICGA), pp. 360-369, San Mateo (CA), 1989

    Google Scholar 

  22. Harp S.A., Samad T. and Guha A., The Genetic Synthesis of Neural Networks, Technical Report CSDD-89-14852-2, Honeywell, Golden Valley (MN), 1989

    Google Scholar 

  23. Merrill J. and Port R., Fractally Configured Neural Networks, Neural Networks, Vol. 4, pp.53–60, 1991

    Article  Google Scholar 

  24. Mjolsness E. and Sharp D.H., A Prelimiary Analysis of Recursively Generated Networks, in Denker J. (Eds.): Neural Networks for Computing, Snowbird (Utah), 1986

    Google Scholar 

  25. Mjolsness E., Sharp D.H. and Alpert B.K., Recursively Generated Neural Networks, Proc. of the IEEE Intern. Conf. on Neural Networks, Vol. III, pp. 165–171, 1987

    Google Scholar 

  26. Mühlenbein H. and Kindermann J., The Dynamics of Evolution and Learning — Towards Genetic Neural Networks, in Pfeifer et al. (Eds.): Connectionism in Perspective, Elsevier, 1989

    Google Scholar 

  27. Schiffmann W.H., Joost M. and Werner R. Optimization of the Backpropagation Algorithm for Training Multilayer Perceptrons, Technical Report 15/1992, University of Koblenz

    Google Scholar 

  28. Schiffmann W.H., Joost M. and Werner R. Synthesis and Performance Analysis of Multilayer Neural Network Architectures, Technical Report 16/1992, University of Koblenz

    Google Scholar 

  29. Fahlman S.E., An Empirical Study of Learning Speed in Back-Propagation Networks, Technical Report, Carnegie-Mellon University, CMU-CS-88-162, 1988

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Physics, University of Koblenz, Rheinau 3-4, D-5400, Koblenz, Germany

    W. Schiffmann, M. Joost & R. Werner

Authors
  1. W. Schiffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Joost
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Werner
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

    Rudolf F. Albrecht

  2. Division of Statistics and Operational Research, UK

    Colin R. Reeves

  3. Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

    Nigel C. Steele

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Springer-Verlag/Wien

About this paper

Cite this paper

Schiffmann, W., Joost, M., Werner, R. (1993). Application of Genetic Algorithms to the Construction of Topologies for Multilayer Perceptrons. In: Albrecht, R.F., Reeves, C.R., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7533-0_98

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-7533-0_98

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82459-7

  • Online ISBN: 978-3-7091-7533-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation