Elsevier

Fuzzy Sets and Systems

Volume 141, Issue 1, 1 January 2004, Pages 5-31
Fuzzy Sets and Systems

Ten years of genetic fuzzy systems: current framework and new trends

https://doi.org/10.1016/S0165-0114(03)00111-8Get rights and content

Abstract

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridise fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms.

The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems. After a brief introduction to models and applications of genetic fuzzy systems, the field is overviewed, new trends are identified, a critical evaluation of genetic fuzzy systems for fuzzy knowledge extraction is elaborated, and open questions that remain to be addressed in the future are raised. The paper also includes some of the key references required to quickly access implementation details of genetic fuzzy systems.

References (135)

  • H.B. Gurocak

    A genetic-algorithm-based method for tuning fuzzy logic controllers

    Fuzzy Sets and Systems

    (1999)
  • U.D. Hanebeck et al.

    Genetic optimization of fuzzy networks

    Fuzzy Sets and Systems

    (1996)
  • H. Heider et al.

    A cascade genetic algorithm for improving fuzzy-system design

    Int. J. Approximate Reasoning

    (1997)
  • F. Herrera et al.

    Tuning fuzzy controllers by genetic algorithms

    Int. J. Approx. Reasoning

    (1995)
  • F. Hoffmann et al.

    Genetic programming for model selection of TSK-fuzzy systems

    Inform. Sci.

    (2001)
  • F. Hoffmann et al.

    Evolutionary design of a fuzzy knowledge base for a mobile robot

    Int. J. Approximate Reasoning

    (1997)
  • J.H. Holland et al.

    Cognitive systems based on adaptive algorithms

  • H. Ishibuchi et al.

    Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems

    Fuzzy Sets and Systems

    (1997)
  • H. Ishibuchi et al.

    Three-objective genetics-based machine learning for linguistic rule extraction

    Inform. Sci.

    (2001)
  • D. Kim et al.

    An optimal design of neuro-FLC by lamarckian co-adaption of learning and evolution

    Fuzzy Sets and Systems

    (2001)
  • L. Magdalena

    Adapting the gain of an FLC with genetic algorithms

    Int. J. Approximate Reasoning

    (1997)
  • L. Magdalena et al.

    A fuzzy logic controller with learning through the evolution of its knowledge base

    Int. J. Approx. Reasoning

    (1997)
  • J.S. Aguilar-Ruiz, J.C. Riquelme, C. Del Valle, Improving the evolutionary coding for machine learning tasks, in Proc....
  • E. Alba et al.

    Evolutionary design of fuzzy logic controllers using strongly-typed GP

    Mathware Soft Comput.

    (1999)
  • E. Alba et al.

    Parallelism and evolutionary algorithms

    IEEE Trans. Evolutionary Computation

    (2002)
  • R. Alcalá et al.

    Fuzzy control of HVAC systems optimised by genetic algorithms

    Appl. Intell.

    (2003)
  • R. Alcalá et al.

    Fuzzy graphsfeatures and taxonomy of learning methods for non-grid oriented fuzzy rule-based systems

    J. Intell. Fuzzy Systems

    (2001)
  • R. Alcalá, J. Casillas, O. Cordón, F. Herrera, Linguistic modeling with weighted double-consequent fuzzy rules based on...
  • G. Alpaydin et al.

    Evolution-based design of neural fuzzy networks using self-adapting genetic parameters

    IEEE Trans. Fuzzy Systems

    (2002)
  • P.P. Angelov

    Evolving Rule-Based Models. A Tool for Design of Flexible Adaptive Systems

    (2002)
  • R. Babuska

    Fuzzy Modeling for Control

    (1998)
  • J.M. Benı́tez et al.

    Are artificial neural networks black boxes?

    IEEE Trans. Neural Networks

    (1997)
  • A. Bonarini

    Evolutionary learning of fuzzy rules: competition and cooperation

  • P. Bonissone et al.

    Hybrid soft computing systemsindustrial and commercial applications

    Proc. IEEE

    (1999)
  • P.P. Bonissone, P.S. Khedkar, Y. Chen, Genetic algorithms for automated tuning of fuzzy controllers: a transportation...
  • P.P. Bonissone, R. Subbu, K.S. Aggour, Evolutionary optimization of fuzzy decision systems for automated insurance...
  • B.P. Buckles, F.E. Petry, D. Prabhu, R. George, R. Srikanth, Fuzzy clustering with genetic search, in Proc. 1st IEEE...
  • M.V. Butz

    Anticipatory Learning Classifier Systems

    (2002)
  • W. Caminhas et al.

    Fuzzy set based neural networksstructure, learning and application

    J. Adv. Comput. Intell.

    (1999)
  • E. Cantú-Paz

    Efficient and Accurate Parallel Genetic Algorithms

    (2000)
  • B. Carse et al.

    Evolving fuzzy rule based controllers using genetic algorithms

    Fuzzy Sets and Systems

    (1996)
  • J. Casillas et al.

    Genetic feature selection in a fuzzy rule-based classification system learning process for high dimensional problems

    Inform. Sci.

    (2001)
  • J.L. Castro et al.

    Interpretation of artificial neural networks by means of fuzzy rules

    IEEE Trans. Neural Networks

    (2002)
  • F. Cheong et al.

    Constraining the optimization of a fuzzy logic controller using an enhanced genetic algorithm

    IEEE Trans. Systems Man Cybernet. B

    (2000)
  • Z. Chi et al.

    Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition

    (1996)
  • C.A. Coello et al.

    Evolutionary Algorithms for Solving Multi-Objective Problems

    (2002)
  • O. Cordón et al.

    A two-stage evolutionary process for designing TSK fuzzy rule-based systems

    IEEE Trans. Systems Man Cybernet.

    (1999)
  • O. Cordón et al.

    A proposal for improving the accuracy of linguistic modeling

    IEEE Trans. Fuzzy Systems

    (2000)
  • O. Cordón et al.

    Genetic Fuzzy Systems—Evolutionary Tuning and Learning of Fuzzy Knowledge Bases

    (2001)
  • O. Cordón et al.

    Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base

    IEEE Trans. Fuzzy Systems

    (2001)
  • Cited by (0)

    View full text