Genetic fuzzy systems
In computer science and operations research, Genetic fuzzy systems are fuzzy systems constructed by using genetic algorithms or genetic programming, which mimic the process of natural evolution, to identify its structure and parameter.
When it comes to automatically identifying and building a fuzzy system, given the high degree of nonlinearity of the output, traditional linear optimization tools have several limitations. Therefore, in the framework of soft computing, genetic algorithms and genetic programming methods have been used successfully to identify structure and parameters of fuzzy systems.
Fuzzy systems
Fuzzy systems are fundamental methodologies to represent and process linguistic information, with mechanisms to deal with uncertainty and imprecision. For instance, the task of modeling a driver parking a car involves greater difficulty in writing down a concise mathematical model as the description becomes more detailed. However, the level of difficulty is not so much using simple linguistic rules, which are themselves fuzzy. With such remarkable attributes, fuzzy systems have been widely and successfully applied to control, classification and modeling problems .Although simplistic in its design, the identification of a fuzzy system is a rather complex task that comprises the identification
of the input and output variables, the rule base, the membership functions and the mapping parameters.
Usually the rule base consists of several IF-THEN rules, linking input and output.
A simple rule of a fuzzy controller could be:
IF THEN
The numerical impact/meaning of this rule depends on how the membership functions of HOT and HIGH are shaped and defined.
The construction and identification of a fuzzy system can be divided into the structure and the parameter identification of a fuzzy system.
The structure of a fuzzy system is expressed by the input and output variables and the rule base, while the parameters of a fuzzy system are the rule parameters and the mapping parameters related to the mapping of a crisp set to a fuzzy set, and vice versa..
Much work has been done to develop or adapt methodologies that are capable of automatically identifying a fuzzy system 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 or genetic programming.
Genetic algorithms for fuzzy system identification
Given the high degree of nonlinearity of the output of a fuzzy system, traditional linear optimization tools do have their limitations.Genetic algorithms have demonstrated to be a robust and very powerful tool to perform tasks such as the generation of fuzzy rule base, optimization of fuzzy rule bases, generation of membership functions, and tuning of membership functions. All these tasks can be considered as optimization or search processes within large solution spaces .