Skip to main content

Evolutionary Fuzzy Systems

  • Reference work entry
  • First Online:
Encyclopedia of Machine Learning and Data Mining
  • 139 Accesses

Definition

An evolutionary fuzzy system is a hybrid automatic learning approximation that integrates fuzzy systems with evolutionary algorithms, with the objective of combining the optimization and learning abilities of evolutionary algorithms together with the capabilities of fuzzy systems to deal with approximate knowledge. Evolutionary fuzzy systems allow the optimization of the knowledge provided by the expert in terms of linguistic variables and fuzzy rules, the generation of some of the components of fuzzy systems based on the partial information provided by the expert, and in some cases even the generation of fuzzy systems without expert information. Since many evolutionary fuzzy systems are based on the use of genetic algorithms, they are also known as genetic fuzzy systems. However, many models presented in the scientific literature also use genetic programming, evolutionary programming, or evolution strategies, making the term evolutionary fuzzy systemsmore adequate. Highly...

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 699.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 949.99
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

Recommended Reading

  • Alpaydtn G, Dundar G, Balktr S (2002) Evolution-based design of neural fuzzy networks using self-adapting genetic parameters. IEEE Trans Fuzzy Syst 10(2):211–221

    Article  Google Scholar 

  • Babuska R (1998) Fuzzy modeling for control. Kluwer Academic Press, Norwell

    Book  Google Scholar 

  • Bonarini A (1996) Evolutionary learning of fuzzy rules: competition and cooperation. In: Pedrycz W (ed) Fuzzy modeling: paradigms and practice. Kluwer Academic Press, Norwell

    Google Scholar 

  • Casillas J, Cordon O, Herrera F, Magdalena L (eds) (2003) Interpretability issues in fuzzy modeling. Studies in fuzziness and soft computing, vol 128. Springer, Berlin/New York

    Google Scholar 

  • Cordon O, Gomide F, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 141:5–31

    Article  MathSciNet  MATH  Google Scholar 

  • Cordon O, Herrera F, Hoffmann F (2001) Genetic fuzzy systems. World Scientific Publishing, Singapore

    Book  MATH  Google Scholar 

  • Hoffmann F (2001) Evolutionary algorithms for fuzzy control system design. Proc IEEE 89(9):1318–1333

    Article  Google Scholar 

  • Juang CF, Lin JY, Lin CT (2000) Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Trans Syst Man Cybern 30(2):290–302

    Article  Google Scholar 

  • Karr CL, Gentry EJ (1993) Fuzzy control of PH using genetic algorithms. IEEE Trans Fuzzy Syst 1(1):46–53

    Article  Google Scholar 

  • Kavka C, Roggero P, Schoenauer M (2005) Evolution of Voronoi based fuzzy recurrent controllers. In: Proceedings of GECCO. ACM Press, NeW York, pp 1385–1392

    Google Scholar 

  • Lee M, Takagi H (1993) Integrating design stages of fuzzy systems using genetic algorithms. In: Proceedings of the second IEEE international conference on fuzzy systems, San Francisco, pp 612–617

    Google Scholar 

  • Pedrycz W (2003) Evolutionary fuzzy modeling. IEEE Trans Fuzzy Syst 11(5):652–665

    Article  Google Scholar 

  • Zadeh L (1988) Fuzzy logic. IEEE Comput 21(4):83–93

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this entry

Cite this entry

Kavka, C. (2017). Evolutionary Fuzzy Systems. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_281

Download citation

Publish with us

Policies and ethics