Skip to main content
SpringerLink
Log in

On the Use of Participatory Genetic Fuzzy System Approach to Develop Fuzzy Models

  • Chapter
  • First Online:
Frontiers of Higher Order Fuzzy Sets

  • 622 Accesses

Abstract

Genetic fuzzy systems constitute an essential approach to build fuzzy models. There is an increasing interest to develop fuzzy models in the realm of complex, large-scale, multiobjective, and high-dimensional systems. Nowadays, fast and scalable evolutionary algorithms to handle complex fuzzy modeling is a major need. Procedures to learn rule bases and tune their parameters are being shaped with the purpose to produce parsimonious and accurate models. Approaches to develop distinct types of fuzzy models such as type one and higher types, or higher order fuzzy trees, fuzzy relations, fuzzy cognitive maps, and neural fuzzy networks rare. This chapter introduces participatory evolutionary learning as a framework for data driven fuzzy modeling. The participatory evolutionary learning approach is a population-based paradigm in which the population itself defines the fitness of the individuals as evolution progress. The approach uses compatibility between population individuals during selection and recombination. A mechanism for information exchange in recombination based on selective transfer is introduced. Combination of participatory learning and selective transfer offers a new class of genetic fuzzy systems. Despite the focus on participatory learning and the selective transfer to build first order fuzzy rule-based models, the use of the genetic fuzzy systems to develop higher order fuzzy rule-based models is also discussed. An electric system maintenance data modeling problem is explored to illustrate the usefulness of the participatory genetic fuzzy systems approach in practice. The performance of participatory evolutionary learning is evaluated using the mean squared modeling error and number of fuzzy rules to measure model accuracy and complexity, respectively. The results suggest that the participatory evolutionary learning develops high quality models and is highly competitive with current state of the art approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A. Abraham, C. Grosan, W. Pedrycz, Engineering Evolutionary Intelligent Systems, vol. 82 (Springer, Berlin, 2008)

    Book  MATH  Google Scholar 

  2. R. Alcalá, J. Alcalá-Fdez, F. Herrera, J. Otero, Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation. Int. J. Approx. Reason 44(1), 45–64 (2007)

    Article  MATH  Google Scholar 

  3. R. Alcalá, M. Gacto, F. Herrera, A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modeling in high-dimensional regression problems. IEEE Trans. Fuzzy Syst. 19(4), 666–681 (2011)

    Article  Google Scholar 

  4. A. Azzini, A.G.B. Tettamanzi, A new genetic approach for neural network design, in Engineering Evolutionary Intelligent Systems, ed. by A. Abraham, C. Grosan, W. Pedrycz. (Springer, Berlin, 2008) pp. 289–323

    Google Scholar 

  5. C. Birchenhall, L. Jie-Shin, Learning Adaptive Artificial Agents: Analysis of an Evolutionary Economic Model. 6th International Conference Society for Computational Economics, Spain, July 2000

    Google Scholar 

  6. C. Birchenhall, N. Kastrinos, S. Metcalfe, Genetic algorithms in evolutionary modelling. J. Evol. Econ. 7, 375–393 (1997)

    Article  Google Scholar 

  7. O. Cordón, F. Herrera, F. Hoffmann, L. Magdalena, Genetic Fuzzy Systems. Evolutionary Tuning and Learning of Fuzzy Knowledge Base (World Scientific, Singapore, 2001)

    Book  Google Scholar 

  8. O. Cordón, F. Herrera, P. Villar, Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Trans. Fuzzy Syst. 9(4), 667–674 (2001)

    Article  Google Scholar 

  9. O. Cordón, F. Gomide, F. Herrera, F. Hoffmann, L. Magdalena, Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst. 41(1), 5–31 (2004)

    Article  Google Scholar 

  10. M. Delgado, F. Von Zubenb, F. Gomide, Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach. Fuzzy Sets Syst. 141(1), 89–106 (2004)

    Article  MATH  Google Scholar 

  11. J. Eggermont, Evolving fuzzy decision trees with genetic programming and clustering, in Genetic Programming, ed. by J.A. Foster, E. Lutton, J.F. Miller, C. Ryan, A.G.B. Tezzamanzi. (Springer, Berlin, 2002), pp. 71–82

    Google Scholar 

  12. M. Gacto, R. Alcalá, F. Herrera, An improved multi-objective genetic algorithm for tuning linguistic fuzzy systems. Proceedings of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Spain, pp. 1121–1128, 2008

    Google Scholar 

  13. S. Gottwald, Set theory for fuzzy sets of higher level. Fuzzy Sets Syst. 2, 125–151 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  14. F. Herrera, Genetic fuzzy systems: taxonomy, current research trends and prospects, Evol. Intell. 1, 27–46 (2008)

    Article  Google Scholar 

  15. F. Herrera, L. Martínez, A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000)

    Article  Google Scholar 

  16. F. Herrera, M. Lozano, A. Sánchez, A taxonomy for the crossover operator for real-coded genetic algorithms: an experimental study. Int. J. Intell. Syst. 18, 309–338 (2003)

    Article  MATH  Google Scholar 

  17. H. Hwang, Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway. IEEE Trans. Syst. Man Cybern. 28(6), 791–802 (1998)

    Google Scholar 

  18. M.W. Kim, J.W. Ryu, Optimized fuzzy decision tree using genetic algorithm, in Neural Information Processing, ed. by I. King, J. Wang, L-W. Chan, D. Wang. (Springer, Berlin, 2006), pp. 797–806

    Google Scholar 

  19. Y.L. Liu, F. Gomide, Evolutionary Participatory Learning and Fuzzy Systems Modeling, Internal Report UNICAMP-FEEC-DCA, (submitted), 2014

    Google Scholar 

  20. J. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions (Prentice Hall, Upper-Saddle River, 2001)

    Google Scholar 

  21. H. Nguyen, E. Walker, \textitA First Course in Fuzzy Logic, 3rd edn. (Chapman Hall, Boca Raton 2006)

    Google Scholar 

  22. W. Pedrycz, F. Gomide, Fuzzy Systems Engineering: Toward Human-Centric Computing. (Wiley Interscience/IEEE Press, Hoboken, 2007)

    Book  Google Scholar 

  23. M. Porter, K. De Jong, Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8, 1–29 (2000)

    Article  Google Scholar 

  24. M. Prosperi, G. Ulivi, Evolutionary fuzzy modelling for drug resistant HIV-1 treatment optimization, in Engineering Evolutionary Intelligent Systems, ed. by A. Abraham, C. Grosan, W. Pedrycz. (Springer, Berlin, 2008), pp. 251–287

    Google Scholar 

  25. N.J. Radcliffe, P.D. Surry, Evolutionary computing, vol. 865, in Lecture Notes in Computer Science, Formal Memetic Algorithms, ed. by Fogarty, T.C. (Springer, Berlin, 1994)

    Google Scholar 

  26. F. Rothlauf, \textitDesign of Modern Heuristics: Principles and Application (Springer, Berlin, 2011)

    Book  Google Scholar 

  27. S. Voget, M. Kolonko, Multidimensional optimization with a fuzzy genetic algorithm. J. Heuristics 4(3), 221–244 (1998)

    Article  MATH  Google Scholar 

  28. L. Wang, \textitAdaptive Fuzzy Systems and Control: Design and Stability Analysis (Prentice Hall, Upper-Saddle River, 1994)

    Google Scholar 

  29. L. Wang, J. Mendel, Generation fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)

    Article  MathSciNet  Google Scholar 

  30. T. Wang, Z. Li, Y. Yan, H. Chen, A survey of fuzzy decision tree classifier methodology. Fuzzy Info. Eng. 40, 959–968 (2007)

    Article  Google Scholar 

  31. P. Windrum, C. Birchenhall, Is product life cycle theory a special case? Dominant designs and the emergence of market niches through coevolutionary-learning. Struct. Change Econ. Dynam. 9, 109–134 (1998)

    Article  Google Scholar 

  32. R. Yager, A model of participatory learning. IEEE Trans. Syst. Man Cybern. 20(5), 1229–1234 (1990)

    Article  MathSciNet  Google Scholar 

  33. R. Yager, Participatory Genetic Algorithms. BISC Group List, Message posted on 29 Aug 2000.

    Google Scholar 

Download references

Acknowledgment

The second author is grateful to CNPq, the Brazilian National Research Council, for grant 304596/2009-4.

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Campinas, Sao Paulo, Brazil

    Yi Ling Liu & Fernando Gomide

Authors
  1. Yi Ling Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Gomide
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Ling Liu .

Editor information

Editors and Affiliations

  1. Ryerson University, Toronto, Ontario, Canada

    Alireza Sadeghian

  2. Ryerson University, Toronto, Ontario, Canada

    Hooman Tahayori

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Liu, Y., Gomide, F. (2015). On the Use of Participatory Genetic Fuzzy System Approach to Develop Fuzzy Models. In: Sadeghian, A., Tahayori, H. (eds) Frontiers of Higher Order Fuzzy Sets. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3442-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-3442-9_5

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-3441-2

  • Online ISBN: 978-1-4614-3442-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation