A correlation guided genetic algorithm and its application to feature selection

Traditional feature selection methods based on genetic algorithms randomly evolve using unguided crossover operators and mutation operators. This leads to many inferior solutions being generated and verified using costly fitness functions. In this paper, we propose a new feature selection method based on a correlation-guided genetic algorithm. It first roughly checks the quality of the potential solutions to reduce the possibility of producing inferior solutions. Then more potentially superior solutions can be verified by the classifier to improve the efficiency of the evolutionary process. It is theoretically proven that the proposed method converges to the optimal solution with a very weak precondition. Numerical results on 4 artificial datasets and 6 real datasets show that compared with other existing methods, the proposed method is a competitive feature selection method with higher classification accuracy and a more efficient evolutionary process.