Evolutionary Tuning and Learning of Fuzzy Knowledge Bases

Oscar Cordón, Francisco Herrera, Frank Hoffmann & Luis Magdalena

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
  • Other Kinds of Evolutionary Fuzzy Systems
  • Applications


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-based system. 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.