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Meta-Heuristic Optimization of a Fuzzy Character Recognizer

  • Alex Tormási
  • László T. Kóczy
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 326)

Abstract

Meta-heuristic algorithms are well researched and widely used in optimization problems. There are several meta-heuristic optimization algorithms with various concepts and each has its own advantages and disadvantages. Still it is difficult to decide which method would fit the best to a given problem. In this study the optimization of a fuzzy rule-base from a classifier, more specifically fuzzy character recognizer is used as the reference problem and the aim of the research was to investigate the behavior of selected meta-heuristic optimization techniques in order to develop a multi meta-heuristic algorithm.

Keywords

Fuzzy systems Fuzzy rule-base optimization Bacterial evolutionary algorithm Big bang–big crunch algorithm Imperialist competitive algorithm Particle swarm optimization Multi meta-heuristics 

Notes

Acknowledgments

This paper is partially supported by the TÁMOP-4.2.2.A-11/1/KONV-2012-0012 and Hungarian Scientific Research Fund (OTKA) grants K105529, K108405.

References

  1. 1.
    Holland, J.H.: Adaption in Natural and Artificial Systems. The MIT Press, Cambridge (1992)Google Scholar
  2. 2.
    Nawa, N.E., Furuhashi, T.: Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Trans. Fuzzy Syst. 7(5), 608–616 (1999)CrossRefGoogle Scholar
  3. 3.
    Erol, K.O., Eksin, I.: A new optimization method: big bang-big crunch. Adv. Eng. Softw. 37(2), 106–111 (2006)CrossRefGoogle Scholar
  4. 4.
    Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the 2007 IEEE Congress on Evolutionary Computation vol. 7, pp. 4661–4666. Singapore (2007)Google Scholar
  5. 5.
    Kowalski, P.A., Lukasik, S.: Tuning neural networks with krill herd algorithm. In: Proceedings of the 6th Győr Symposium and 3rd Hungarian-Polish and 1st Hungarian-Romanian Joint Conference on Computational Intelligence, ConfCI 2014, pp. 119–128. Győr (2014)Google Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7, 1–13 (1975)CrossRefzbMATHGoogle Scholar
  8. 8.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. In: IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-15, pp. 116–132 (1985)Google Scholar
  9. 9.
    Tormási, A., Botzheim, J.: Single-stroke character recognition with fuzzy method. In: Balas V.E., et al. (eds.) New Concepts and Applications in Soft Computing SCI, vol. 417, pp. 27–46 (2012)Google Scholar
  10. 10.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the IEEE International Conference on Neural Networks IV, IEEE Press, pp. 1942–1948. Piscataway (1995)Google Scholar
  11. 11.
    Dányádi, Zs., Balázs, K., Kóczy, L.T.: A comparative study of various evolutionary algorithms and their combinations for optimizing fuzzy rule-based inference systems. Scientific Bulletin of Politehnica University of Timisoara, Romania, 55(69), 247–254 (2010)Google Scholar
  12. 12.
    Balázs, K., Horváth, Z., Kóczy, L.T.: Hybrid bacterial iterated greedy heuristics for the permutation flow shop problem. In: In World Congress on Computational Intelligence, WCCI 2012, pp. 1–8. Brisbane, Australia (2012)Google Scholar
  13. 13.
    Balázs, K., Kóczy, L.T.: A remark on adaptive scheduling of optimization algorithms. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2010, pp. 719–728. Dortmund, Germany (2010)Google Scholar
  14. 14.
    Ishibuchi, H., Nakashima, T.: Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 9(4), 506–515 (2001)CrossRefGoogle Scholar
  15. 15.
    van den Berg, J., Kaymak, U., van den Bergh, W.M.: Fuzzy classification using probability-based rule weighting. In: Proceedings of the 11th IEEE International Conference on Fuzzy Systems, Hawaii (2002)Google Scholar
  16. 16.
    Ishibuchi, H., Yamamoto, T.: Rule weight specification in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 13(4), 428–435 (2005)CrossRefGoogle Scholar
  17. 17.
    Tormási, A., Kóczy, L.T.: Fuzzy-based multi-stroke character recognizer. In: Preprints of the Federated Conference on Computer Science and Information Systems, pp. 675–678. Krakow (2013)Google Scholar
  18. 18.
    Tormási, A., Kóczy, L.T.: Comparing the efficiency of a fuzzy single-stroke character recognizer with various parameter values. In: Greco S., et al. (eds.) Proceedings of the IPMU 2012, Part I. CCIS, vol. 297, pp. 260–269 (2012)Google Scholar
  19. 19.
    Tormasi, A., Kóczy, L.T.: Fuzzy single-stroke character recognizer with various rectangle fuzzy grids. In: Issues and challenges of intelligent systems and computational intelligence, Springer, pp. 145–159 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Information TechnologySzéchenyi István UniversityGyőrHungary
  2. 2.Department of AutomationSzéchenyi István UniversityGyőrHungary
  3. 3.Department of Telecommunication and Media InformaticsBudapest University of Technology and EconomicsBudapestHungary

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