Abstract
Evolutionary learning and tuning mechanism to fuzzy systems is the main concern to researchers in the filed. The optimized final performance on the fuzzy system is dependent on the ability of the system to find the best optimized rule-set(s) as well as the optimized fuzzy variable definition. This paper proposes a mechanism of selection and optimization of fuzzy variables termed as “Fuzzimetric Arcs” and then discusses how this mechanism can become a standard of selection and optimization of fuzzy set shapes to tune the performance of GFS. Genetic algorithm is the technique that can be utilized to alter/modify the initial shape of fuzzy sets using two main operators (Crossover and Mutation). Optimization of rule-set(s) is mainly dependent on the measurement of fitness factor and the level of deviation from fitness factor.
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Kouatli, I. (2010). GFS Tuning Algorithm Using Fuzzimetric Arcs. In: Sobh, T., Elleithy, K. (eds) Innovations in Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9112-3_30
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DOI: https://doi.org/10.1007/978-90-481-9112-3_30
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