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The support vector machine is a powerful supervised learning algorithm that has been successfully applied to a plenty of fields including text and image recognition, medical diagnosis and so on. The kernel and its parameters optimization, formally known as model selection, is a crucial factor which influences a good tradeoff between bias and variance. To automate model selection of support vector machine, this paper presents a strategy utilizing self-adaptive genetic algorithm and data distribution to determine the kernel function and all the free kernel parameters. The model selection criterion using a novel fitness function with hyperplane radius and a off-spring individual selection method during the process of constructing the model. The experiments on well known benchmark data sets are carried out to validate the effectiveness of proposed strategy. The experimental results show that using genetic algorithm to tune the model is a promising way to enhance the performance of support vector machine.