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Genetic algorithms (GAs) are a highly effective and efficient means of solving optimization problems. Gene encoding, fitness landscape and genetic operations are vital to successfully developing a GA. F. Cheong and R. Lai (see ibid., vol. 30, p. 31-46 (2000)) described a novel method, which employed an enhanced genetic algorithm with multiple populations, to optimize a fuzzy controller, and the experimental results revealed that their method was effective in producing a well-formed fuzzy rule-base. However, their encoding method and fitness function appear unnatural and inefficient. This study proposes an alternative method of concise genetic encoding and fitness design.