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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chang, P., Liu, C.: A tsk type fuzzy rule based system for stock price prediction. Expert Syst. Appl. 34(1), 135–144 (2008)
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)
Eiben, G., van Hemert, J.: Saw-ing eas: adapting the fitness function for solving constrained problems, pp. 389–402 (1999)
Fama, E.F., French, K.R.: Multifactor explanations of asset pricing anomalies. Journal of Finance 51(1), 55–84 (1996)
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)
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)
Hinterding, A.E.E.R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3, 124–141 (1999)
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)
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)
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)
Nomura, H., Hayashi, I., Wakami, N.: A learning method of fuzzy inference rules by descent method, pp. 203–210 (March 1992)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ghandar, A., Michalewicz, Z., Neumann, F. (2010). Evolving Fuzzy Rules: Evaluation of a New Approach. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-17298-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17297-7
Online ISBN: 978-3-642-17298-4
eBook Packages: Computer ScienceComputer Science (R0)