Abstract
ABSTRACTFuzzy logic and evolutionary computation have proven to be convenient tools for handling real-world 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 structure-preserving genetic operators is also introduced.
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