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This paper proposes a novel approach combining wavelet-based networks and game-theoretical decision approach to reach the terms real-time power dispatch and the best compromise solution. The goals considered are both fuel cost and environment impact of NOx emission. The wavelet-based networks, evolved by an evolutionary computing algorithm, are composed of 3-layer structures, which contain the wavelet, weighting, and summing nodes. The parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned to make the computed outputs fit the historical data. Once the networks are trained properly, the desired outputs can be produced as soon as the inputs are given. Based on the set of noninferior solutions for a certain load level, a game-theoretical approach is relied on to provide operators the best compromise solution. The effectiveness of the proposed approach has been demonstrated by the IEEE 30-bus 6-generator test system. Comparisons of learning performances are made to the existing artificial neural networks (ANNs) method.