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.