SIFT-SS: An Advanced Steady-State Multi-Objective Genetic Fuzzy System

  • Michel González
  • Jorge Casillas
  • Carlos Morell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)


Nowadays, automatic learning of fuzzy rule-based systems is being addressed as a multi-objective optimization problem. A new research area of multi-objective genetic fuzzy systems (MOGFS) has capture the attention of the fuzzy community. Despite the good results obtained, most of existent MOGFS are based on a gross usage of the classic multi-objective algorithms. This paper takes an existent MOGFS and improves its convergence by modifying the underlying genetic algorithm. The new algorithm is tested in a set of real-world regression problems with successful results.


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  1. 1.
    Ishibuchi, H.: Multiobjective genetic fuzzy systems: Review and future research directions. In: Proc. of 2007 IEEE International Conference on Fuzzy Systems, London, UK, July 23-26, pp. 913–918 (2007)Google Scholar
  2. 2.
    Ishibuchi, H.: Evolutionary multiobjective design of fuzzy rule-based systems. In: Proc. of 2007 IEEE Symposium on Foundation of Computational Intelligence, Honolulu, USA, April 1-5, pp. 9–16 (2007)Google Scholar
  3. 3.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  4. 4.
    Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary Many-Objective Optimization. In: Proc. of 3rd International Workshop on Genetic and Evolving Fuzzy Systems, Witten-Bommerholz, Germany, pp. 47–52 (2008)Google Scholar
  5. 5.
    Gacto, M.J., Alcalá, R., Herrera, F.: Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems. Soft Comput. 13(5), 419–436 (2008)CrossRefGoogle Scholar
  6. 6.
    Casillas, J.: Efficient multi-objective genetic tuning of fuzzy models for large-scale regression problems. In: Proc. of 2009 IEEE International Conference on Fuzzy Systems, Jeju, Republic of Korea, pp. 1712–1717 (2009)Google Scholar
  7. 7.
    Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. Ph.D. dissertation. Air Force Institute of Technology, Dayton (1999)Google Scholar
  8. 8.
    Ishibuchi, H., Narukawa, K., Tsukamoto, N., Nojima, Y.: An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization. European Journal of Operational Research 188(1), 57–75 (2008)zbMATHCrossRefGoogle Scholar
  9. 9.
    Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7, 30 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Michel González
    • 1
  • Jorge Casillas
    • 2
  • Carlos Morell
    • 1
  1. 1.Universidad Central “Marta Abreu” de Las Villas, CUBA 
  2. 2.Dept. Computer Science and Artificial IntelligenceUniversity of GranadaSpain

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