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This paper presents a self-organized genetic algorithm-based rule generation (SOGARG) method for fuzzy logic controllers. It is a three-stage hierarchical scheme that does not require any expert knowledge and input-output data. The first stage selects rules required to control the system in the vicinity of the set point. The second stage extends this to the entire input space, giving a rulebase that can bring the system to its set point from almost all initial states. The third stage refines the rulebase and reduces the number of rules. The first two stages use the same fitness function whose aim is only to acquire the controllability, but the last stage uses a different one, which attempts to optimize both the settling time and number of rules. The effectiveness of SOGARG is demonstrated using an inverted pendulum and the truck reversing.