Memetic Neuro-Fuzzy System with Differential Optimisation

  • Krzysztof Siminski
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 613)


Neuro-fuzzy systems are capable of tuning theirs parameters on presented data. Both global and local techniques can be used. The paper presents a hybrid memetic approach where local (gradient descent) and global (differential evolution) approach are combined to tune parameters of a neuro-fuzzy system. Application of the memetic approach results in lower error rates than either gradient descent optimisation or differential evolution alone. The results of experiments on benchmark datasets have been statistically verified.


Neuro-fuzzy system Memetic algorithm Differential evolution 


  1. 1.
    Alcalá-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17(2–3), 255–287 (2011)Google Scholar
  2. 2.
    Cordón, O., Herrera, F.: Identification of linguistic fuzzy models by means of genetic algorithms. In: Hellendoorn, H., Driankov, D. (eds.) Fuzzy Model Identification, pp. 215–250. Springer, Heidelberg (1997). CrossRefGoogle Scholar
  3. 3.
    Cordón, O., Herrera, F.: A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples. Int. J. Approximate Reasoning 17(4), 369–407 (1997). Genetic Fuzzy Systems for Control and RoboticsCrossRefzbMATHGoogle Scholar
  4. 4.
    Czogała, E., Łeski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. STUDFUZZ. Physica-Verlag, A Springer-Verlag Company, Heidelberg, New York (2000)CrossRefzbMATHGoogle Scholar
  5. 5.
    Di Gesù, V., Lo Bosco, G., Millonzi, F., Valenti, C.: A memetic algorithm for binary image reconstruction. In: Brimkov, V.E., Barneva, R.P., Hauptman, H.A. (eds.) IWCIA 2008. LNCS, vol. 4958, pp. 384–395. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Łeski, J., Czogała, E.: A neuro-fuzzy inference system optimized by deterministic annealing. In: Hampel, R., Wagenknecht, M., Chaker, N. (eds.) Fuzzy Control. Advances in Soft Computing, vol. 6, pp. 287–293. Physica-Verlag HD (2000).
  7. 7.
    Nalepa, J., Kawulok, M.: A memetic algorithm to select training data for support vector machines. In: Proceedings of the 2014 conference on Genetic and evolutionary computation, pp. 573–580 (2014)Google Scholar
  8. 8.
    Qing, A., Lee, C.K. (eds.): Differential Evolution in Electromagnetics. ALO, vol. 4. Springer, Heidelberg (2010)Google Scholar
  9. 9.
    Reichenbach, H.: Erkenntnis. Wahrscheinlichkeitslogik 5, 37–43 (1935)Google Scholar
  10. 10.
    Saaty, T.L., Ozdemir, M.S.: Why the magic number seven plus or minus two. Math. Comput. Model. 38(3–4), 233–244 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Siminski, K.: Memetic neuro-fuzzy system with Big-Bang-Big-Crunch optimisation. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds.) Man-Machine Interactions 4. AISC, pp. 583–592. Springer International Publishing, New York (2016)CrossRefGoogle Scholar
  12. 12.
    Storn, R., Price, K.V.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Insitute (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

Personalised recommendations