World Scientific
  • Search
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Tue, Oct 25th, 2022 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.
Genetic Fuzzy Systems cover

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.


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

Readership: Probabilists, analysts, statisticians and mathematicians.
Free Access
FRONT MATTER
  • Pages:i–xxv

https://doi.org/10.1142/9789812810731_fmatter

No Access
Fuzzy Rule-Based Systems
  • Pages:1–46

https://doi.org/10.1142/9789812810731_0001

No Access
Evolutionary Computation
  • Pages:47–78

https://doi.org/10.1142/9789812810731_0002

No Access
Introduction to Genetic Fuzzy Systems
  • Pages:79–97

https://doi.org/10.1142/9789812810731_0003

No Access
Genetic Tuning Processes
  • Pages:99–125

https://doi.org/10.1142/9789812810731_0004

No Access
Learning with Genetic Algorithms
  • Pages:127–151

https://doi.org/10.1142/9789812810731_0005

No Access
Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach
  • Pages:153–178

https://doi.org/10.1142/9789812810731_0006

No Access
Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach
  • Pages:179–218

https://doi.org/10.1142/9789812810731_0007

No Access
Genetic Fuzzy Rule-Based Systems Based on the Iterative Rule Learning Approach
  • Pages:219–264

https://doi.org/10.1142/9789812810731_0008

No Access
Other Genetic Fuzzy Rule-Based System Paradigms
  • Pages:265–332

https://doi.org/10.1142/9789812810731_0009

No Access
Other Kinds of Evolutionary Fuzzy Systems
  • Pages:333–374

https://doi.org/10.1142/9789812810731_0010

No Access
Applications
  • Pages:375–424

https://doi.org/10.1142/9789812810731_0011

Free Access
BACK MATTER
  • Pages:425–462

https://doi.org/10.1142/9789812810731_bmatter