###### Abstract
We discuss these studies more later in this work, in Section 2. The motivation behind adopting the BHO for FS can be illustrated through several key points:  
1. **Astrophysics inspiration:** BHO draws inspiration from astrophysics, specifically the fascinating concept of black holes. This unique property sets it apart from traditional statistical methods. By mimicking the gravitational interactions of black holes, BHO explores feature spaces in a novel and non-deterministic manner. 2. **Randomness-based exploration:** BHO leverages randomness and domesticity techniques to explore wider areas within the search space. Unlike deterministic approaches, it embraces stochasticity, allowing it to escape the local optima problem, found in many FS methods, and avoid premature convergence. This adaptability is particularly valuable in complex optimization landscapes. 3. **Simplicity and computational efficiency:** The mathematical model underlying BHO is not overly complex. Consequently, it requires less computational expense compared to more intricate approaches. This efficiency makes it practical for real-world applications that need feature selection. In summary, MBHO combines the power of black hole-inspired exploration with the practicality of simplicity, making it a promising candidate for feature selection tasks. In our proposed FS approach, we improve the fitness function to better assess the optimal feature subset and explore the search space effectively. Specifically, we consider the correlation among features and their relationship with the assigned label, utilizing Spearman's (and [PERSON]'s) correlation functions [36]. Additionally, we use the V2 transfer function to handle continuous values, as evolutionary algorithms often work with such solutions. Moreover, the enhanced binary version of BHO in this study incorporates inversion mutation to enhance population diversity and prevent premature convergence. These enhancements in our FS (MBHO) approach allow for a broader exploration of the search space and the extraction of a more refined feature subset. We thoroughly assess MBHO across fourteen benchmark datasets spanning diverse domains, such as healthcare and social life. Our evaluation focuses on accuracy, F1 score, precision, recall, and area under the curve (AUC) metrics, ensuring a comprehensive analysis across various problem domains. We compare MBHO against popular FS methods like Binary Cuckoo Search (BCS) [37], Mutual Information Maximization (MIM) [38], Joint Mutual Information (JMI) [39], and minimum Redundancy Maximum Relevance (mRMR) [40]. Notably, MBHO consistently delivers competitive results and exhibits robustness across different datasets, which is crucial for real-world applications where data properties vary significantly. A key insight from MBHO's performance is its consideration of feature dependence, aligning well with real-world scenarios. While MBHO performs well with validation datasets, it also demonstrates strong generalization capabilities with unseen data. These findings underscore its potential for practical deployment in real-life feature selection tasks. Moreover, MBHO achieves these outcomes efficiently, utilizing reasonable computational resources, as demonstrated in our evaluation. In summary, the main contributions of this work can be summarized as follows:  
1. We propose a novel wrapper feature selection (FS) approach that combines the black hole algorithm with inversion mutation to select the most descriptive subset of features from datasets that cover different application domains. 2. We modify a well-established, multi-objective function that focuses on the interleaved classifier and the number of selected features in the decision-making process. Additionally, we enhance the decision-making process by considering the correlation among the selected subset of features and the correlation of each feature within that subset with the corresponding label. 3. We assess our approach using fourteen benchmark datasets.