On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control

摘要

Fuzzy logic and evolutionary computation have proven to be convenient tools for handling realworld uncertainty and designing control systems, respectively. An approach is presented that combines attributes of these paradigms for the purpose of developing intelligent control systems. The potential of the genetic programming paradigm (GP) for learning rules for use in fuzzy logic controllers (FLCs) is evaluated by focussing on the problem of discovering a controller for mobile robot path tracking. Performance results of incomplete rule-bases compare favorably to those of a complete FLC designed by the usual trial-and-error approach. A constrained syntactic representation supported by structurepreserving genetic operators is also introduced. Keywords: fuzzy control, genetic programming, syntactic contraints, mobile robots, rule-base discovery. 1 Introduction Recent research and applications employing non-analytical methods of soft computing such as fuzzy logic and evolutionary computati...