Universidad de Granada
Departamento de Ciencias de la Computación e Inteligencia Artificial DECSAI ETSIIT
Last Update: 27-August-2007  
  O. Cordón, F. Herrera, F. Hoffmann, L. Magdalena,
  Evolutionary Tuning and Learning of Fuzzy Knowledge Bases
  Vol. 19 of Advances in Fuzzy Systems - Applications and Theory.
  World Scientific, 2001, ISBN 981-02-4016-3

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Table of Contents
  • Fuzzy Rule-Based Systems
  • Evolutionary Computation
  • Introduction to Genetic Fuzzy Systems
  • Genetic Tuning Processes
  • Learning with Genetic Algorithms
  • Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach
  • Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach
  • Genetic Fuzzy Rule-Based Systems Based on the Iterative Rule Learning Approach
  • Other Genetic Fuzzy Rule-Based System
    • FRBSs with GP
    • Genetic Selection of Fuzzy Rule Sets
    • Learning the Knowledge Base via the Genetic Derivation of the Data Base
  • Other Genetic-Based Machine Learning Approaches
    • Genetic Fuzzy Neural Networks
    • Genetic Fuzzy Clustering
    • Genetic Fuzzy Decision Trees
  • Other Kinds of Evolutionary Fuzzy Systems
    • Genetic Fuzzy Neural Networks
    • Genetic Fuzzy Clustering
    • Genetic Fuzzy Decision Tree
  • Applications
    • Classification
    • System Modelling
    • Control Systems
    • Robotics


               In recent years, a great number of publications have explored the use of genetic algorithms
      as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this
      symbiosis of evolutionary computation and fuzzy logic.
      The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special
      attention to genetic algorithms that adapt and learn the knowledge base of a fuzzy-rule-basedsystem.
      It introduces the general concepts, foundations and design principles of genetic fuzzy systems and
      covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches
      to genetic learning processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods.
      Finally, it explores hybrid genetic fuzzy systems such as genetic fuzzy clustering or genetic neuro-fuzzy
      systems and describes a number of applications from different areas.

      Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based
      systems using genetic algorithms, both from a theoretical and a practical perspective. It is a valuable
      compendium for scientists and engineers concerned with research and applications in the domain of
      fuzzy systems and genetic algorithms.

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