I’ve done some searching in the Universidad de Cantabria‘s library looking for helping books for the Symbolic AI course of the Master Degree. I’ve found out and taken with me one which combines Fuzzy Symbolic and Genetic (sublcass of Evolutionary Computation) Artificial Intelligence to build Knowledge Base Systems.
Advances in Fuzzy Systems — Applications and Theory Vol. 19
Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases (Advances in Fuzzy Systems – Applications & Theory) (Paperback)
by Oscar Cordon (Author), Francisco Herrera (Author), (SCI2S University of Granada, Spain); Frank Hoffmann (Author) (Royal Institute of Technology, Stockholm), Luis Magdalena (Author) (Universidad Politécnica de Madrid, Spain)
“Nowadays, one of the most important areas of application of fuzzy set theory as developed by Zadeh (1965) are fuzzy rule-based systems (FRBSs)…”
World Scientific Publishing Co. Pte. Ltd.
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 the symbiosis of evolutionary computation and fuzzy logic.
The book summarizes and analyses the novel field of genetic fuzzy systems, paying special attention to the 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 (Chapters 1, 2 and 3) and covers the topic of genetic tuning of fuzzy systems (Chapter 4) It also introduces three fundamental approaches to genetic learning processes (Chapter 5) in fuzzy systems: the Michigan (Chapter 6), Pittsburgh (Chapter 7) and Iterative-leaning methods (Chapter 8) Finally, it explores hybrid genetic fuzzy systems such as genetic fuzzy clustering or genetic neuro-fuzzy systems (Chapter 10) and describes a number of applications from different areas (Chapter 11 pp. 375-424: Classification in medical domain, System Modelling, Control Systems and Robotics)
Genetic Fuzzy Sysmtems represents a comprehensive treatise on the design of fuzzy-rule-based systems using genetic algorithms, both from a theoretical and a 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.
The authors of the University of Granada (SCI2S – Soft Computing and Intelligent Information Systems) have developed a software called KEEL (Knowledge Extraction Based on Evolutionary Learning) which might be useful in combination withe book and for the course of data mining.