The fuzzy classifier system: Motivations and first results
This paper presents a proposal for a machine learning system, called the fuzzy classifier system. The fuzzy classifier system allows for inputs, outputs, and internal variables to take continuous values over given ranges. The fuzzy classifier system learns by creating fuzzy rules which relate the values of the input variables to internal or output variables. It has credit assignment and conflict resolution mechanisms which reassemble those of common classifier systems, with a fuzzy nature. The fuzzy classifier system employs a genetic algorithm to evolve adequate fuzzy rules. Preliminary results show that the fuzzy classifier system can effectively create fuzzy rules that imitate the behavior of a simple static system.
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