Expert Mutation Operators for the Evolution of Radial Basis Function Neural Networks

  • J. González
  • I. Rojas
  • H. Pomares
  • M. Salmerón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2084)


This paper compares some mutation operators containing expert knowledge about the problem of optimizing the parameters of a Radial Basis Function Neural Network. It is shown that the expert knowledge is not always able to improve the results obtained by a blind evolutionary algorithm, and that the final results depend strongly on how the expert knowledge is utilized.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. Chen, C. F. N. Cowan, and P. M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Networks, 2:302–309, 1991.CrossRefGoogle Scholar
  2. 2.
    J. A. Dickerson and B. Kosko. Fuzzy function approximation with ellipsoidal rules. IEEE Trans. Syst. Man and Cyber.-Part B, 26(4):542–560, August 1996.CrossRefGoogle Scholar
  3. 3.
    G. H. Golub and C. F. Van Loan. Matrix Computations. Johns Hopkins University Press, Baltimore, 3rd edition, 1996.zbMATHGoogle Scholar
  4. 4.
    J. González, I. Rojas, H. Pomares, M. Salmerón, and A. Prieto. Evolution of fuzzy patches for function approximation. In A. Ollero, S. Sánchez, B. Arrue, and I. Baturone, editors, Actas del X Congreso Español sobre Tecnologías y Lógica Difusa, ESTYLF 2000, pages 489–495, Sevilla, Spain, Sept. 2000.Google Scholar
  5. 5.
    J. J. Holland. Adaption in Natural and Artificial Systems. University of Michigan Press, 1975.Google Scholar
  6. 6.
    P. P. Kanjilal and D. N. Banerjee. On the application of orthogonal transformation for the design and analysis of feed-forward networks. IEEE Trans. Neural Networks, 6(5):1061–1070, 1995.CrossRefGoogle Scholar
  7. 7.
    E. G. Kogbetliantz. Solution of linear equations by diagonalization of coefficients matrix. Quart. Appl. Math., 13:123–132, 1955.MathSciNetzbMATHGoogle Scholar
  8. 8.
    Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, 3rd edition, 1996.Google Scholar
  9. 9.
    H. Pomares, I. Rojas, J. Ortega, J. González, and A. Prieto. A systematic approach to a self-generating fuzzy rule-table for function approximation. IEEE Trans. Syst., Man, Cyber. Part B, 30(3):431–447, June 2000.CrossRefGoogle Scholar
  10. 10.
    I. Rojas, J. González, A. Cañas, A. F. Díaz, F. J. Rojas, and M. Rodriguez. Shortterm prediction of chaotic time series by using rbf network with regression weights. Int. Journal of Neural Systems, 10(5):353–364, 2000.Google Scholar
  11. 11.
    I. Rojas, H. Pomares, J. González, J. L. Bernier, E. Ros, F. J. Pelayo, and A. Prieto. Analysis of the functional block involved in the design of radial basis function networks. Neural Processing Letters, 12(1):1–17, Aug. 2000.zbMATHCrossRefGoogle Scholar
  12. 12.
    M. Salmerón, J. González, J. Ortega, I. Rojas, and C. G. Puntonet. Métodos ortogonales paralelos para predicción de series. In J. Ortega, editor, Perspectivas en Paralelismo de Computadores. Actas de las XI Jornadas de Paralelismo, pages 277–282, Granada, España, Sept. 2000. Universidad de Granada.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • J. González
    • 1
  • I. Rojas
    • 1
  • H. Pomares
    • 1
  • M. Salmerón
    • 1
  1. 1.Department of Computer Architecture and Computer TechnologyUniversity of GranadaGranadaSpain

Personalised recommendations