MULTIPLE OBJECTIVE FEATURE SELECTION BASED ON SELF-LEARNING BINARY DIFFERENTIAL EVOLUTION
In order to improve the group search ability and accelerate the convergence speed,a multiple objective feature selection method based on self-learning binary differential evolution is proposed.Three operators were introduced,and the binary mutation operator based on probability difference was used to generate the optimal solution,so as to quickly guide individuals to locate the potential optimal region.The clean search operator was introduced to improve the self-learning ability of the elites in the optimal region,while the non-dominated sorting operator with crowding distance could reduce the computational complexity of the selection operator in differential evolution.The experimental results on multiple data sets show that the proposed method is efficient and accurate on multiple objective feature selection.
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