Rule Pairing Methods for Crossover in GA for Automatic Generation of Fuzzy Control Rules
نویسندگان
چکیده
We had presented fuzzy rule generation methods by Genetic Algorithm. In this paper, we propose three methods to determine rule pairs for crossover in GA for fuzzy rules generation in order to improve search e ciency and reduction of the number of rules. The rst two methods are that rule pairs are determined based on a distance between rules of two individuals to be crossed. The third one is that rules of each individual are sorted based on a distance between the origin and a rule center in input space. We apply these methods to fuzzy rules generation for a trailer truck back up control, and we show that the rule sorting method can generate a compact and high performance fuzzy system.
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