Feature selection is an effective approach for solving the curse of dimensionality. Evolutionary computation, such as genetic algorithms, are extensively applied into feature selection. However, with the available algorithms, features aren’t screened before evolutionary computation starts and all of them are equal in status during the process of evolutionary computation. In this paper, a new algorithm that screens features before evolutionary computation starts, and makes full use of the screened ones during the process of evolutionary computation is proposed. In detail, important and useful features are found by scoring all features, and endowed with privileges in obtaining advantages comparing to other features during the forthcoming process of evolutionary computation, which is the first stage of our proposed algorithm. As for the second stage, we design a genetic algorithm with multiple sub populations, in which each sub population corresponds to a combination of important and useful features, and a competition mechanism between sub populations is introduced. As a result, important and useful features are further sufficiently used and extensively explored compared to the available algorithms, hence classification accuracies are increased. Experiments are performed with 8 datasets comparing to 11 state-of-the-art algorithms to validate our proposed algorithm. And the results show that our proposed algorithm outperforms the 11 state-of-the-art algorithms.
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