## 2 Related Work
### Genetic-Algorithm-Based Feature Selection  
GA was widely used in the feature selection stage of ML pipelines that were implemented to solve various problems such as phishing, spam, malicious domain detection, or disease diagnosis such as cancer detection. [PERSON] et al. [23] tackled the challenge of heterogeneity and high dimensionality in EEG signals for data interpretation. They proposed a GA for feature selection, modifying stopping criteria and fitness functions to accommodate supervised and unsupervised approaches. The proposed GAFS outperforms benchmarks in overall performance and feature reduction, with consistent features aligned with neuroscientific literature, and the proposal is effective for heterogeneous data. Addressing the high-dimensionality of document representation in text classification tasks, [PERSON] suggests a GA-based meta-heuristic optimization for feature selection [24]. He had developed a new objective function based on the F1 score model and the size of the subset of features to improve the F1 score of the classifier hypothesis. In this study, it is shown that the proposed approach can improve the generalization ability of a classifier by reducing redundant and irrelevant features. [PERSON] presents a model with which to recognize and classify children's activities with audio data from embedded sensors in clothing [25]. Due to limited mobile device resources, a GA is used to reduce the size of the dataset, achieving an accuracy of 0.92 with a random forest classifier. In the work of [PERSON] et al. [26], a model is introduced to detect phishing websites using ML approaches is introduced. They demonstrate improved detection accuracy through feature selection using GA, with the combination of ID3 and YAGGA achieving up to 95% accuracy. The authors proposed a model for the detection of anomalies in breast thermograms using a combination of a transfer-learning-based Deep Learning (DL) model and feature selection approaches [27]. The DL model generates a large number of features and requires a significant amount of memory and computational time to process these features. The number of features is reduced using a hybrid of optimization algorithms GA and graywolf. In tests on an open source dataset, 100% accuracy was achieved using only 3% of the extracted features. In their study, [PERSON] and [PERSON] [28] used GA for feature selection to improve the classifier's performance and proposed a model called \"Subset Generation using Genetic Algorithm for Improved Classification\" (ISG-GA-IC). Furthermore, the paper presents the results of studies on the use of EAs for feature extraction and selection in high-dimensional datasets. Similarly to these, [PERSON] introduces a GA based on community detection for feature selection, which operates in three steps [29]. First, they calculate feature similarities. Second, community detection algorithms are used to classify features into clusters. Lastly, a GA with a community-based repair operation is used to select features. The performance of the proposed approach is evaluated on nine benchmark classification problems. Fur thermore, the performance is compared with three new feature selection methods based on PSO, ACO, and ABC algorithms in three classifiers. The results indicate that the proposed method achieves higher accuracy compared to the PSO, ACO, and ABC algorithms, with average improvements of 0.52%, 1.20%, and 1.57%, respectively. [PERSON] et al. [30] present a wrapper approach for feature selection in multi-target regression. The method utilizes the MTR-SAFER algorithm, a safe semi-supervised regression algorithm designed for multi-target regression with a single target technique. To determine the optimal feature subset, the paper incorporates a GA as the chosen feature selection method. The GA is employed to search for the most suitable set of features that maximize the performance of the MTR-SAFER algorithm. Microarray datasets are currently being used in the early diagnosis of some chronic diseases, such as cancer.