An Effective Differential Evolution With Binary Strategy for Feature Selection Problem

Abstract

In this paper, an effective differential evolution is proposed with binary strategy to solve feature selection problem. Firstly, a new binary mutation operator and a binary crossover operator are designed. The two-stage adaptive strategy is constructed in the mutation operator to generate new individuals to improve the diversity of the population. Then, the adaptive cross-parameter selection based on individual is developed in the crossover operator to fully exploit each potential optimal individual. Finally, six benchmark data sets are adopted to evaluate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm significantly improves the classification accuracy, reduction rate and time cost.