## 2 Background Studies
### Nature-Based Algorithms in Feature Selection Used in Different Studies  
Many recent studies have started using nature-based algorithms in different areas for feature selection and to achieve higher accuracy. Some of these studies are included in this section. In [5], for the feature selection problem, a new hybrid model based on the idea of opposition based learning (OBL) was provided that combines the whale optimization algorithm (WOA) and the FPA. The result obtained from experience showed that the proposed algorithm was more successful in terms of accuracy. The dataset used for this experiment is from the University of California Irvine (UCI) data repository and spam email dataset. In order to increase classification performance, the authors of [6] suggested combining the binary flower pollination algorithm (BFPA) and improved binary particle swarm optimization (iBPSO) with naive Bayes (NB) and K-nearest neighbor (K-NN). Results from the experiment show that the hybrid iBPSO BFPA outperformed the current strategy by achieving the highest accuracy of 94.43%. In [7], researchers presented an enhanced adaptive FPA that can alter its parameter settings dynamically during the convergence process and keep track of the optimum solution. The experimental findings showed that the other strategies with nine benchmark functions from the literature performed better, with a faster convergence rate. In order to reduce computational complexity, the authors of [18] proposed a wrapper-filter combination of ant colony optimization (ACO) to which they introduced subset evaluation using a filter approach rather than a wrapper method. Utilizing K-NN and multilayer perceptron classifiers, their suggested strategy was tested on several real-world datasets collected from the UCI ML repository and the NIPS2003 feature selection challenge. Comparison of the findings demonstrates unequivocally that their method outperforms the majority of the cutting-edge feature selection algorithms. To improve the accuracy of heart disease categorization, in [8], researchers used the fast correlation-based feature selection (FCBF) method to filter out redundant information. They then carried out classification using various classification algorithms, including K-NN, support vector machine (SVM), NB, RF, and a multilayer perception artificial neural network optimized by PSO combined with ACO approaches. Using the improved model suggested by FCBF, PSO, and ACO, the maximum classification accuracy was 99.65%. The purpose of [19] was to provide a thorough analysis of the nature-inspired metaheuristics used in the feature selection field. This review-based study discovered the answers to four research questions, including the number of metaheuristic techniques, their use, various feature selection methods, their contributions to the field of feature selection, and the frequency with which articles based on these techniques are published. To handle feature selection tasks, a novel gravitational search-approach-based algorithm with evolutionary crossover and mutation operators was proposed in [20]. K-NN and DT classifiers were both employed as evaluators for the proposed wrapper feature selection technique. A total of 18 well-known UCI datasets were used to evaluate the effectiveness of the suggested methods. The thorough results and comparisons show that the proposed model greatly outperforms previous wrapper approaches and has virtues in terms of exploration and exploitation, tradeoffs between searching trends, and faster convergence rates compared to other peers on a number of feature selection tasks. The authors of [21] used RF to choose the key characteristic for classification. Additionally, they assessed and contrasted each classification model's accuracy and performance, including linear discriminant analysis (LDA), RF, SVM, and K-NN. The results of the experiments show that RF performed better across all experimental groups. According to another review [22], RF, SVM, LR, and k-NN are the ML algorithms most frequently employed for microbiome analysis.