Advertisement

Evolving Fuzzy Rules: Evaluation of a New Approach

  • Adam Ghandar
  • Zbigniew Michalewicz
  • Frank Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6457)

Abstract

Evolutionary algorithms have been successfully applied to optimize the rulebase of fuzzy systems. This has lead to powerful automated systems for financial applications. We experimentally evaluate the approach of learning fuzzy rules by evolutionary algorithms proposed by Kroeske et al. [10]. The results presented in this paper show that the optimization of fuzzy rules may be universally simplified regardless of the complex fitness surface for the overall optimization process. We incorporate a local search procedure that makes use of these theoretical results into an evolutionary algorithms for rule-base optimization. Our experimental results show that this improves a state of the art approach for financial applications.

Keywords

Evolutionary Algorithm Fuzzy System Fuzzy Rule Rule Base Output Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chang, P., Liu, C.: A tsk type fuzzy rule based system for stock price prediction. Expert Syst. Appl. 34(1), 135–144 (2008)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Cordón, O., Gomide, F.A.C., Herrera, F., Hoffmann, F., Magdalena, L.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141(1), 5–31 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Eiben, G., van Hemert, J.: Saw-ing eas: adapting the fitness function for solving constrained problems, pp. 389–402 (1999)Google Scholar
  4. 4.
    Fama, E.F., French, K.R.: Multifactor explanations of asset pricing anomalies. Journal of Finance 51(1), 55–84 (1996)CrossRefGoogle Scholar
  5. 5.
    Fazel Zarandi, M.H., Rezaee, B., Turksen, I.B., Neshat, E.: A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Syst. Appl. 36(1), 139–154 (2009)CrossRefGoogle Scholar
  6. 6.
    Ghandar, A., Michalewicz, Z., Schmidt, M., To, T.-D., Zurbruegg, R.: Computational intelligence for evolving trading rules. To appear in IEEE Transactions On Evolutionary Computation (2009)Google Scholar
  7. 7.
    Hinterding, A.E.E.R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3, 124–141 (1999)CrossRefGoogle Scholar
  8. 8.
    Ishibuchi, H., Nakashima, T., Murata, T.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 29(5), 601–618 (1999)CrossRefGoogle Scholar
  9. 9.
    John, R.: Type 2 fuzzy sets: an appraisal of theory and applications. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6(6), 563–576 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Kroeske, J., Ghandar, A., Michalewicz, Z., Neumann, F.: Learning fuzzy rules with evolutionary algorithms – an analytic approach. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1051–1060. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Nomura, H., Hayashi, I., Wakami, N.: A learning method of fuzzy inference rules by descent method, pp. 203–210 (March 1992)Google Scholar
  12. 12.
    Nozaki, K., Ishibuchi, H., Tanaka, H.: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets Syst. 86(3), 251–270 (1997)CrossRefGoogle Scholar
  13. 13.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man and Cybernetics 15(1), 116–132 (1985)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adam Ghandar
    • 1
  • Zbigniew Michalewicz
    • 1
    • 2
    • 3
  • Frank Neumann
    • 4
  1. 1.School of Computer ScienceUniversity of AdelaideAdelaideAustralia
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  3. 3.Polish-Japanese Institute of Information TechnologyWarsawPoland
  4. 4.Max-Planck-Institut für InformatikSaarbrückenGermany

Personalised recommendations