Genetic Fuzzy Systems
Genetic Fuzzy Systems are
fuzzy systems using a
genetic algorithm for determining the system parameters.
Fuzzy systems are fundamental methodologies to represent and process
linguistic information, with mechanisms to deal with uncertainty and imprecision. With such remarkable attributes, fuzzy systems have been widely and successfully applied to control, classification and modeling problems (Klir and Yuan, 1995) (Pedrycz and Gomide, 1998).
One of the most important tasks in the development of fuzzy systems is the design of its
knowledge base. An expressive effort has been devised lately to develop or adapt methodologies that are capable of automatically extracting the knowledge base from numerical data. Particularly in the framework of soft computing, significant methodologies have been proposed with the objective of building fuzzy systems by means of genetic algorithms (GAs).
Genetic algorithms have demonstrated to be a powerful tool to perform tasks such as generation of fuzzy rule base, optimization of fuzzy rule bases, generation of membership functions, and tuning of membership functions (Córdon et al., 2001a). All theses tasks can be considered as optimization or search processes. Fuzzy system generated or adapted by genetic algorithms are called Genetic Fuzzy Systems (Córdon et al., 2001b). The combination of Fuzzy Systems with Genetic Algorithms have great acceptance in the scientific community, once these algorithms are robust and can search efficiently large solution spaces (Yuan and Zhuang, 1996).
The first step in designing a Genetic Fuzzy System is to decide which parts of the knowledge base (KB) are subject to optimisation by the GA. The KB of a fuzzy system does not constitute an homogeneous structure but is rather the union of qualitativelydifferent components. As an example, the KB of a descriptive Mamdani-type fuzzy system is comprised of two components: a data base (DB), containing the definitions of the scaling factors and the membership functions of the fuzzy sets associated with the linguistic labels, and a rule base (RB), constituted by the collection of fuzzy rules.
Genetic learning of the rule base assumes a predefined set of fuzzy membership functions in the DB to which the rules refer to by means of linguistic labels.
* (Klir and Yuan, 1995) G. Klir and B. Yuan, "Fuzzy sets and Fuzzy Logic - Theory and Applications", Prentice-Hall, 1995.
* (Pedrycz and Gomide, 1998) W. Pedrycz and F. Gomide, "An Introduction to Fuzzy Sets: Analysis and Design", MIT Press, 1998.
* (Cordón et al., 2001a) O. Cordón, F. Herrera, F. Gomide, F. Hoffmann and L. Magdalena, ``Ten years of genetic-fuzzy systems: a current framework and new trends'', Proceedings of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, pp. 1241-1246, Vancouver - Canada, 2001.
* (Cordón et al., 2001b) O. Cord\'on, F. Herrera, F. Hoffmann and L. Magdalena, "Genetic Fuzzy Systems. Evolutionary tuning and learning of fuzzy knowledge bases", Advances in Fuzzy Systems: Applications and Theory, World Scientific, 2001.
* (Yuan and Zhuang, 1996) Y. Yuan and H. Zhuang, "A genetic algorithm for generating fuzzy classification rules", Fuzzy Sets and Systems, V. 84, N. 4, pp. 1-19, 1996.
*
Pablo Castro´s Home Page
