Skip to main content
SpringerLink
Log in

Evolutionary Optimization of Neural Systems: The Use of Strategy Adaptation

  • Conference paper
Trends and Applications in Constructive Approximation

Part of the book series: ISNM International Series of Numerical Mathematics ((ISNM,volume 151))

Abstract

We consider the synthesis of neural networks by evolutionary algorithms, which are randomized direct optimization methods inspired by neo-Darwinian evolution theory. Evolutionary algorithms in general as well as special variants for real-valued optimization and for search in the space of graphs are introduced. We put an emphasis on strategy adaptation, a feature of evolutionary methods that allows for the control of the search strategy during the optimization process.

Three recent applications of evolutionary optimization of neural systems are presented: topology optimization of multi-layer neural networks for face detection, weight optimization of recurrent networks for solving reinforcement learning tasks, and hyperparameter tuning of support vector machines.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. M.A. Arbib, editor. The Handbook of Brain Theory and Neural Networks. MIT Press, 2 edition, 2002.

    Google Scholar 

  2. M.A. Arbib. Towards a neurally-inspired computer architecture. Natural Computing, 2(1):1–46, 2003.

    MathSciNet  Google Scholar 

  3. H.-G. Beyer and H.-P. Schwefel. Evolution strategies: A comprehensive introduction. Natural Computing, 1(1):3–52, 2002.

    Google Scholar 

  4. H.-G. Beyer, H.-P. Schwefel, and I. Wegener. How to analyse evolutionary algorithms. Theoretical Computer Science, 287:101–130, 2002.

    MathSciNet  Google Scholar 

  5. C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.

    Google Scholar 

  6. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46(1):131–159, 2002.

    Google Scholar 

  7. K. Chellapilla and D.B. Fogel. Evolution, neural networks, games, and intelligence. Proceedings of the IEEE, 87(9):1471–1496, 1999.

    Google Scholar 

  8. K.-M. Chung, W.-C. Kao, C.-L. Sun, and C.-J. Lin. Radius margin bounds for support vector machines with RBF kernel. Neural Computation, 15(11):2643–2681, 2003.

    PubMed  Google Scholar 

  9. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000.

    Google Scholar 

  10. L. Davis. Adapting operator probabilities in genetic algorithms. In J.D. Schaffer, editor, Proceedings of the Third International Conference on Genetic Algorithms. ICGA’89, pages 61–69, Fairfax, VA, USA, 1989. Morgan Kaufmann.

    Google Scholar 

  11. A.E. Eiben, R. Hinterding, and Z. Michalewicz. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3(2):124–141, 1999.

    Google Scholar 

  12. A.E. Eiben and G. Rudolph. Theory of evolutionary algorithms: A bird’s eye view. Theoretical Computer Science, 229(1):3–9, 1999.

    MathSciNet  Google Scholar 

  13. T. Evgeniou, M. Pontil, and T. Poggio. Regularization networks and support vector machines. Advances in Computational Mathematics, 13:1–50, 2000.

    Google Scholar 

  14. F. Friedrichs and C. Igel. Evolutionary tuning of multiple SVM parameters. In M. Verleysen, editor, 12th European Symposium on Artificial Neural Networks (ESANN 2004), pages 519–524. Evere, Belgium: d-side publications, 2004.

    Google Scholar 

  15. C. Gold and P. Sollich. Model selection for support vector machine classification. Neurocomputing, 55(1–2):221–249, 2003.

    Google Scholar 

  16. F.J. Gomez and R. Miikulainen. Solving non-markovian tasks with neuroevolution. In T. Dean, editor, Proceeding of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), pages 1356–1361, Stockholm, Sweden, 1999. Morgan Kaufmann.

    Google Scholar 

  17. F. Gruau, D. Whitley, and L. Pyeatt. A comparison between cellular encoding and direct encoding for genetic neural networks. In J.R. Koza, D.E. Goldberg, D.B. Fogel, and R..L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 81–89, Stanford University, CA, USA, 1996. MIT Press.

    Google Scholar 

  18. N. Hansen and A. Ostermeier. Convergence properties of evolution strategies with the derandomized covariance matrix adaptation: The (μ/μ, λ)-CMA-ES. In 5th European Congress on Intelligent Techniques and Soft Computing (EUFIT’97), pages 650–654. Aachen, Germany: Verlag Mainz, Wissenschaftsverlag, 1997.

    Google Scholar 

  19. N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159–195, 2001.

    PubMed  Google Scholar 

  20. M. Hiisken and C. Igel. Balancing learning and evolution. In W.B. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R.. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke, and N. Jonoska, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pages 391–398. Morgan Kaufmann, 2002.

    Google Scholar 

  21. C. Igel. Neuroevolution for reinforcement learning using evolution strategies. In R. Sarker, R. Reynolds, H. Abbass, K.C. Tan, B. McKay, D. Essam, and T. Gedeon, editors, Congress on Evolutionary Computation (CEC 2003), volume 4, pages 2588–2595. IEEE Press, 2003.

    Google Scholar 

  22. C. Igel, W. Erlhagen, and D. Jancke. Optimization of dynamic neural fields. Neuro-computing, 36(1–4):225–233, 2001.

    Google Scholar 

  23. C. Igel and M. Hiisken. Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing, 50(C):105–123, 2003.

    Google Scholar 

  24. C. Igel and M. Kreutz. Operator adaptation in evolutionary computation and its application to structure optimization of neural networks. Neurocomputing, 55(1–2):347–361, 2003.

    Google Scholar 

  25. C. Igel and M. Toussaint. Neutrality and self-adaptation. Natural Computing, 2(2):117–132, 2003.

    MathSciNet  Google Scholar 

  26. M. Mandischer. A comparison of evolution strategies and backpropagation for neural network training. Neurocomputing, 42(1–4):87–117, 2002.

    Google Scholar 

  27. P. Meinicke, T. Twellmann, and H. Ritter. Discriminative densities from maximum contrast estimation. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, Cambridge, MA, 2002. MIT Press.

    Google Scholar 

  28. D.E. Moriarty, A.C. Schultz, and J.J. Grefenstette. Evolutionary Algorithms for Reinforcement Learning. Journal of Artificial Intelligence Research, 11:199–229, 1999.

    Google Scholar 

  29. S. Nolfi. Evolution and learning in neural networks. In M.A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 415–418. MIT Press, 2 edition, 2002.

    Google Scholar 

  30. M. Patel, V. Honavar, and K. Balakrishnan, editors. Advances in the Evolutionary Synthesis of Intelligent Agents. MIT Press, 2001.

    Google Scholar 

  31. G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for adaboost. Machine Learning, 42(3):287–32, 2001.

    Google Scholar 

  32. I. Rechenberg. Evolutionsstrategie ‘94. Werkstatt Bionik und Evolutionstechnik. Frommann-Holzboog, Stuttgart, 1994.

    Google Scholar 

  33. R.D. Reed and R.J. Marks II. Neural Smithing. MIT Press, 1999.

    Google Scholar 

  34. M. Riedmiller. Advanced supervised learning in multi-layer perceptrons — From back-propagation to adaptive learning algorithms. Computer Standards and Interfaces, 16(5):265–278, 1994.

    Google Scholar 

  35. H.A. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligenc, 20(1):23–38, 1998.

    Google Scholar 

  36. G. Rudolph. On correlated mutations in evolution strategies. In R. Männer and B. Manderick, editors, Parallel Problem Solving from Nature 2 (PPSN II), pages 105–114. Elsevier, 1992.

    Google Scholar 

  37. B. Schölkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.

    Google Scholar 

  38. H.-P. Schwefel. Evolution and Optimum Seeking. Sixth-Generation Computer Technology Series. John Wiley & Sons, 1995.

    Google Scholar 

  39. B.A. Sendhoff. Evolution of Structures — Optimization of Artificial Neural Structures for Information Processing. Shaker Verlag, Aachen, 1998.

    Google Scholar 

  40. H.T. Siegelmann and E.D. Sontag. On the computational power of neural nets. Journal of Computer and System Sciences, 50(1):132–150, 1995.

    Google Scholar 

  41. J. Šíma. Training a single sigmoidal neuron is hard. Neural Computation, 14:2709–2728, 2002.

    PubMed  Google Scholar 

  42. J. Šíma and P. Orponen. General-purpose computation with neural networks: A survey of complexity theoretix results. Neural Computation, 15(12):2727–2778, 2003.

    PubMed  Google Scholar 

  43. J.E. Smith and T.C. Fogarty. Operator and parameter adaptation in genetic algorithms. Soft Computing, 1(2):81–87, 1997.

    Google Scholar 

  44. E.D. Sontag. Recurrent neural networks: Some systems-theoretic aspects. In M. Karny, K. Warwick, and V. Kurkova, editors, Dealing with Complexity: A Neural Network Approach, pages 1–12. Springer-Verlag, 1997.

    Google Scholar 

  45. P. Stagge. Strukturoptimierung rückgekoppelter neuronaler Netze. Konzepte neu-ronaler Informationsverarbeitung. ibidem-Verlag, Stuttgart, 2001.

    Google Scholar 

  46. K.O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2):99–127, 2002.

    PubMed  Google Scholar 

  47. R.S. Sutton and A.G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.

    Google Scholar 

  48. V.N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, 1995.

    Google Scholar 

  49. S. Wiegand, C. Igel, and U. Handmann. Evolutionary optimization of neural networks for face detection. In M. Verleysen, editor, 12th European Symposium on Artificial Neural Networks (ESANN 2004), pages 39–144. Evere, Belgium: d-side publications, 2004.

    Google Scholar 

  50. A. Wieland. Evolving controls for unstable systems. In Proceedings of the International Joint Conference on Neural Networks, volume II, pages 667–673. IEEE Press, 1991.

    Google Scholar 

  51. M.-H. Yang, D.J. Kriegman, and N. Ahuja. Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1):34–58, 2002.

    Google Scholar 

  52. X. Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423–1447, 1999.

    Google Scholar 

  53. G. P. Zhang. Neural Networks for Classification: A Survey. IEEE Transactions on System. Man, and Cybernetics — Part C, 30(4), 2000.

    Google Scholar 

  54. A.A. Zhigljavsky. Theory of global random search. Kluwer Academic Publishers, 1991.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute für Neuroinformatik, Ruhr-Universität Bochum, D-44780, Bochum, Germany

    Christian Igel, Stefan Wiegand & Frauke Friedrichs

Authors
  1. Christian Igel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Wiegand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frauke Friedrichs
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. FB3, Applied Mathematics (Constructive Approximation), University of Applied Science Bochum, Herner Str. 45, D-44787, Bochum

    Detlef H. Mache

  2. Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, P.O. Box 127, H-1364, Budapest

    József Szabados

  3. Department of Applied Mathematics, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands

    Marcel G. de Bruin

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Birkhäuser Verlag Basel/Switzerland

About this paper

Cite this paper

Igel, C., Wiegand, S., Friedrichs, F. (2005). Evolutionary Optimization of Neural Systems: The Use of Strategy Adaptation. In: Mache, D.H., Szabados, J., de Bruin, M.G. (eds) Trends and Applications in Constructive Approximation. ISNM International Series of Numerical Mathematics, vol 151. Birkhäuser Basel. https://doi.org/10.1007/3-7643-7356-3_9

Download citation

Publish with us

Policies and ethics

Search

Navigation