Abstract
This paper presents a genetic fuzzy system for identification of Paretooptimal Mamdani fuzzy models (FMs) for function estimation problems. The method simultaneously optimizes the parameters of fuzzy sets and selects rules and rule conditions. Selection of rules and rule conditions does not rely only on genetic operators, but it is aided by heuristic rule and rule conditions removal. Instead of initializing the population by commonly usedWang-Mendel algorithm, we propose a modification to decision tree initialization. Experimental results reveal that our FMs are more accurate and consist of less rules and rule conditions than the FMs obtained by two recently published genetic fuzzy systems [2, 3].
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