## 1 Introduction  
With the widespread use of the internet today, there has been a significant increase in the size of emerging datasets. The rapid growth of datasets leads to challenges such as difficulties in extracting relevant information from real-world applications, increased processing time, hardware limitations, and higher computational complexity. Therefore, feature selection methods, which hold an important place in data mining, are gaining importance [1]. Feature selection methods aim to identify relevant features prior to training models on real-world datasets containing many features. Relevant features are those directly related to the target concept and are neither irrelevant nor redundant; irrelevant features do not affect the target concept, whereas redundant features contain duplicate or unnecessary information [2]. Given a dataset with N features, there exist possible subsets. As the number of features increases, the search space grows exponentially, making it increasingly challenging to find the optimal subset. Therefore, removing unnecessary features reduces both training time and memory requirements during model training [2]. Moreover, reducing the dataset size facilitates simpler representation, visualization, and interpretation of the data [3]. In line with the advantages offered by feature selection methods, various techniques have been developed in the literature. These methods are generally classified into three main categories: filter methods, wrapper methods, and hybrid methods [4]. Filter methods operate independently of the learning algorithm; data are scored based on statistical criteria, and feature selection is performed according to these scores [5]. These methods are generally faster than wrapper methods. However, due to uncertainties in determining the threshold that distinguishes between relevant and irrelevant features, filter methods are often considered less effective and less successful compared to wrapper methods [3]. Wrapper methods comprise feature evaluation criterion, a search strategy, and a stopping criterion. Unlike filter methods, wrapper models use the performance of the learning algorithm as the evaluation metric and select the subset of features that achieves the best performance [6]. Wrapper methods can be categorized into three main groups based on their feature search strategies: sequential, bio-inspired, and iterative methods. Sequential search methods are easy to implement and fast. Bio-inspired methods incorporate randomness into the search process to avoid getting trapped in local minima, while iterative methods aim to avoid combinatorial search [4]. Hybrid methods have been developed to overcome the disadvantages of filter and wrapper methods. These approaches incorporate both a feature selection algorithm and a classification algorithm, and perform both processes simultaneously [7]. Feature selection methods aim to determine which features should be selected from a dataset. The resulting solutions take binary values of 0 or 1, indicating whether each feature is excluded or included. In order to obtain an exact solution, all possible subsets must be evaluated; however, this significantly increases the computational cost. As the size of datasets increases, the use of heuristic methods to achieve an optimal solution has become increasingly common [8]. Continuous versions of optimization algorithms need to be discretized in order to be applied to decision problems. Numerous discretization methods have been proposed in the literature for this purpose. By developing a reupdate rule as in discrete Jaya algorithm (DJAYA) [9] algorithm, using XOR method as in Jaya-based binary optimization algorithm (JAMAX) [10] or using transfer functions [11; 12], discrete versions of these algorithms have been developed. Although these algorithms do not always guarantee the best solution, they can produce acceptable solutions within a reasonable time.