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This paper presents a neural network with variable parameters. These variable parameters adapt to the changes of the input environment, and tackle different input data sets in a large domain. Each input data set is effectively handled by its corresponding set of network parameters. Thus, the proposed neural network exhibits a better learning and generalization ability than a traditional one. An improved genetic algorithm (Lam et al., 2004) is proposed to train the network parameters. An application example on hand-written pattern recognition will be presented to verify and illustrate the improvement.