Feature selection is an important part of machine learning and data mining which may enhance the speed and the performance of learning and mining algorithms. Given certain criteria to evaluate features, the problem of feature selection can be regarded as an optimization problem. Therefore, evolutionary algorithms can be used to solve such a kind of optimization problems. In this paper, we present a novel feature subset selection approach based on the framework of genetic algorithms. Two new mutation operators are constructed using the standard deviation of candidate features and the cardinality of candidate feature subsets. Then, a filter feature subset selection approach using a dynamic mixed strategy is proposed, which combines the new mutation operators with the single-point mutation operator. The new approach can not only dynamically adjust the probability distribution over these three mutation operators, but also maintain the combined effects of feature subsets as a whole fitness evaluation. The proposed approach is able to quickly escape from local optimal feature subsets and to obtain smaller scale subsets than evolutionary algorithms using a single mutation operator. Experiments have been implemented on six standard UCI datasets and the proposed algorithm is compared with other classical algorithms. The comparison outcomes confirm the effectiveness of our approach.