While mutual information and correlation distance are effective metrics for feature selection, they do have limitations when used as the sole objectives in a bi-objective optimization problem. One potential limitation is that these metrics may not capture all aspects of feature relevance and redundancy, leading to suboptimal feature selection. To address this, alternative objectives can be incorporated to provide a more comprehensive evaluation of features. For example, incorporating measures of feature importance, such as feature weights from a machine learning model, can offer additional insights into the significance of each feature. Additionally, diversity metrics can be included to ensure a diverse set of features is selected, preventing redundancy. By integrating a combination of different objectives, the FMLFS method can enhance the feature selection process and improve the overall performance of the model.