Hierarchical genetic algorithm with new evaluation function and bi-coded representation for the selection of features considering their confidence rate

In this paper, we propose a new feature selection method based on a hierarchical genetic algorithm (GA) with a new evaluation function and a bi-coded representation. The hierarchical GA with homogeneous and heterogeneous population is used to minimize the computational load and to accelerate convergence speed. The fitness function is designed to find the solution that both maximizes the recognition rate and minimizes the feature set size. Each solution candidate is represented by two chromosomes which lengths are identical to the number of available features. The first binary chromosome represents the presence of features in the solution candidate; the second represents the confidence rates of features, which are used to assign different weights to features during the classification procedure and to achieve more accurate classifier. The proposed method is tested using five databases and is shown to outperform many commonly used feature selection algorithms.