A number of publications have explored the use of genetic algorithms as a tool for designing fuzzy systems. This volume explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the 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 Systems" 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 aims to provide a valuable compendium for scientists and engineers concerned with research and applications in the domain of fuzzy systems and genetic algorithms - specifically for probabilists, analysts, statisticians and mathematicians.