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Aiming at that training needs a long time and classification accuracy worse than grid algorithm which reduced the benefits of support vector machine (SVM) based on genetic algorithm.This paper redefined the optimal range of the parameters of genetic algorithm,and proposed an adaptive genetic algorithm.According to the grid search method to get the best optimization genetic algorithm parameter range, and then the genetic algorithm in this range for accurate optimization of parameters, get the final classification results. It can not only shorten the time effectively, and it has higher classification accuracy.Experimental results on the UCI 10 classical dataset verify that adaptive genetic algorithm is better than the grid search method、traditional genetic algorithm、particle swarm optimization (PSO) algorithm in the aspect of training time,and has higher classification accuracy.