Evolutionary neural networks for nonlinear dynamics modeling

  • I. De Falco
  • A. Iazzetta
  • P. Natale
  • E. Tarantino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

In this paper the evolutionary design of a neural network model for predicting nonlinear systems behavior is discussed. In particular, the Breeder Genetic Algorithms are considered to provide the optimal set of synaptic weights of the network. The feasibility of the neural model proposed is demonstrated by predicting the Mackey-Glass time series. A comparison with Genetic Algorithms and Back Propagation learning technique is performed.

Keywords

Time Series Prediction Artificial Neural Networks Genetic Algorithms Breeder Genetic Algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. E. Rumelhart, J. L. McLelland, Parallel Distributed Processing,I–II, MIT Press, 1986.Google Scholar
  2. 2.
    J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.Google Scholar
  3. 3.
    D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, Massachusetts, 1989.Google Scholar
  4. 4.
    J. D. Shaffer, D. Whitley and L. J. Eshelman, Combination of Genetic Algorithms and Neural Networks: A Survey of the State of the Art, in Combination of Genetic Algorithms and Neural Networks, J. D. Shaffer, L. D. Whitley eds., pp. 1–37, 1992.Google Scholar
  5. 5.
    X. Yao, A Review of Evolutionary Artificial Networks, Int. J. Intelligent Systems, 8 (4), pp. 539–567, 1993.Google Scholar
  6. 6.
    D. J. Montana and L. Davis, Training Feedforward Neural Networks using Genetic Algorithms, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Morgan Kaufmann, pp. 762–767, 1989.Google Scholar
  7. 7.
    D. Whitley, Genetic Algorithms and Neural Networks, in Genetic Algorithms in Engineering and Computer Science, J. Periaux, M. Galan and P. Cuesta eds., John Wiley, pp. 203–216, 1995.Google Scholar
  8. 8.
    I. De Falco, A. Della Cioppa, P. Natale and E. Tarantino, Artificial Neural Networks Optimization by means of Evolutionary Algorithms, in Soft Computing in Engineering Design and Manufacturing, Springer-Verlag, London, 1997.Google Scholar
  9. 9.
    A. Imada and K. Araki, Evolution of a Hopfield Associative Memory by the Breeder Genetic Algorithm, Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 784–791, 1997.Google Scholar
  10. 10.
    X. Yao and Y. Liu, A New Evolutionary Systems for Evolving Artificial Neural Networks, IEEE Trans. on Neural Networks, 8 (3), pp. 694–713, 1997.MathSciNetCrossRefGoogle Scholar
  11. 11.
    D. B. Fogel, Evolutionary Computation: Towards a New Philosophy of Machine Intelligence, New York, NY 10017–2394: IEEE Press, 1995.Google Scholar
  12. 12.
    H. Mühlenbein and D. Schlierkamp-Voosen, Analysis of Selection, Mutation and Recombination in Genetic Algorithms, Neural Network World, 3, pp. 907–933, 1993.Google Scholar
  13. 13.
    H. Mühlenbein and D. Schlierkamp-Voosen, Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization, Evolutionary Computation, 1(1), pp. 25–49, 1993.Google Scholar
  14. 14.
    H. Mühlenbein and D. Schlierkamp-Voosen, The Science of Breeding and its Application to the Breeder Genetic Algorithm, Evolutionary Computation, 1, pp. 335–360, 1994.Google Scholar
  15. 15.
    M. Mackey and L. Glass, Oscillation and Chaos in Physiological Control System, Science, pp. 197–287, 1977.Google Scholar
  16. 16.
    D. Farmer and J. Sidorowich, Predicting Chaotic Time Series, Physical Review Letter 59, pp. 845–848, 1987.MathSciNetCrossRefGoogle Scholar
  17. 17.
    N. H. Packard, J. D. Crutchfield, J. D. Farmer and R. S. Shaw, Geometry from a Time Series, Physical Review Letters, 45, pp. 712–716, 1980.CrossRefGoogle Scholar
  18. 18.
    F. Takens, Detecting Strange Attractors in Turbulence, in Lecture Notes in Mathematics, Springer-Verlag, Berlin, 1981.Google Scholar
  19. 19.
    M. Casdagli, S. Eubank, J. D. Farmer and J. Gibson, State Space Reconstruction in the Presence of Noise, Physica D, 51, pp. 52–98, 1991.MATHMathSciNetCrossRefGoogle Scholar
  20. 20.
    A. Lapedes and R. Farber, Nonlinear Signal Processing using Neural Networks: Prediction and System Modeling, Los Alamos National Laboratory Technical Report LA-UR-87-2662, 1987.Google Scholar
  21. 21.
    H. M. Voigt, H. Mühlenbein and D. Cvetković, Fuzzy Recombination for the Continuous Breeder Genetic Algorithm, Proceedings of the Sixth International Conference on Genetic Algorithms, Morgan Kaufmann, 1995.Google Scholar
  22. 22.
    D. Schlierkamp-Voosen and H. Mühlenbein, Strategy Adaptation by Competing Subpopulations, Proceedings of Parallel Problem Solving from Nature (PPSNIII), Morgan Kaufmann, pp. 199–208, 1994.Google Scholar
  23. 23.
    P.G. Korning, Training of neural networks by means of genetic algorithm working on very long chromosomes, Technical Report, Computer Science Department, Aarhus, Denmark, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • I. De Falco
    • 1
  • A. Iazzetta
    • 1
  • P. Natale
    • 1
  • E. Tarantino
    • 1
  1. 1.Research Institute on Parallel Information Systems (IRSIP)National Research Council of Italy (CNR)NaplesItaly

Personalised recommendations