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A novel approach is proposed to combine the strengths of GA and GP to optimize rule sets and membership functions of fuzzy systems in a co-evolutionary strategy in order to avoid the problem of dual representation in fuzzy systems. The novelty of proposed algorithm is twofold. One is that GP is used for the structural part (Rule sets) and GA for the string part (Membership functions). The goal is to reduce/eliminate the problem of competing conventions by co-evolving pieces of the problem separately and then in combination. Second is exploiting the synergism between rules sets and membership functions by imitating the effect of "matching" and friendship in cooperating teams of humans, thereby significantly reducing the number of function evaluations necessary for evolution. The method is applied to a chaotic time series prediction problem and compared with the standard fuzzy table look-up scheme. demonstrate several significant improvements with the proposed approach; specifically, four times higher fitness and more steady fitness improvements as compared with epochal improvements observed in GP.