###### Abstract
This method is known for its high accuracy in selecting the most satisfactory group of genes. Nevertheless, the filter approach has some limitations, as functioning on each gene individually ignores the interrelationships between genes, which can result in a local optima issue [14]. It is worth noting that the filter approach can be categorized into two sub-types--univariate and multivariate--with the latter considering the correlations between genes. Some examples of the filter approach include Relief [15], Fisher score [16], \(t\)-test [17], and information gain [18]. The wrapper method investigates all feasible gene subsets to test and create a subset of genes to determine their implications. A specific classifier is employed to determine the outcome of each subset, and the categorization technique is used multiple times for each assessment. Compared to the filter approach, the wrapper approach delivers superior performance by using a categorization technique that directs the learning procedure. However, this method requires substantial time and computational resources, especially when dealing with large-scale data [19]. Meta-heuristic methods (MHMs) are high-level heuristics used in mathematical optimization and computer science to find satisfactory solutions to optimization problems when information is imperfect or resources are limited [20]. MHMs sample a subset of solutions, making them useful for various optimization issues due to their few assumptions about the problem [21]. However, they do not guarantee globally optimal solutions. Many MHMs employ stochastic optimization, meaning the solution is based on random variables generated during the process [22]. Meta-heuristic methods (MHMs) are more practical than traditional iterative methods and optimization algorithms because they can explore a broader range of possible solutions. Consequently, MHMs have become a preferred strategy for solving optimization problems [23]. Numerous research papers have demonstrated that among the different wrapper methods, MHMs are well suited to address the feature selection (FS) issue. Stochastic methods, including MHMs, can produce optimal or near-optimal results quickly. Theyoffer benefits such as flexibility, self-management without detailed mathematical properties, and the ability to assess numerous outcomes simultaneously. Various MHMs have recently been developed to solve the FS problem, providing reliable near-optimal solutions at significantly reduced computational costs [24]. Embedded FS methods utilize a learning technique to select appropriate genes that interact with the classification process. This method combines the FS technique as part of the learning model. The learning algorithm is trained with an initial attribute subset to estimate a measure for evaluating the rank values of attributes [25]. The ultimate goal is to decrease the computation time for reclassifying different subsets by incorporating the FS stage into the training process. Some techniques in this approach perform feature weighting based on regularization models with objective functions that minimize fitting errors while enforcing feature coefficients to be small or precisely zero. Examples of embedded methods are the First-Order Inductive Learner (FOIL) rule-based feature subset selection algorithm and SVM based on Recursive Feature Elimination (SVM-RFE) [26]. The hybrid method is a well-crafted technique that amalgamates the filter and wrapper methods to leverage the strengths of each. This approach begins with a filter that reduces the feature space dimensionality, generating multiple subsets with intermediate complexity. Subsequently, a wrapper is employed as a learning technique to choose the most suitable candidate subset [27]. The hybrid approach integrates the accuracy of wrappers and the efficiency of filters, resulting in an optimal methodology [28; 29].