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Feature selection is of great importance in recognition system design because it directly affects the overall performance of the recognition system. Feature selection can be considered as a problem of global combinatorial optimization. It is a very time-consuming task to search the most suitable features amongst a huge number of possible feature combinations, therefore, an effective and efficient search technique is desired. In this paper, we use genetic algorithms (GA) to design a feature selection approach for handwritten Chinese character recognition. Four contributions are claimed: First, the general transformed divergence among classes, which is derived from Mahalanobis distances, is proposed to be the fitness function in the feature selection based on GA; Second, a special crossover operator other than traditional one is given; Third, a special criterion of terminating selections is inferred from the criterion of …