## 2 Related Work
### Genetic-Algorithm-Based Feature Selection
Due to their high dimensionality, reducing dimensions is necessary to increase the accuracy of cancer classification. [PERSON] and [PERSON] [31] proposed a solution to this problem by first selecting the most significant features of cancer microarray datasets using filter feature selection methods such as information gain, information gain ratio, and Chi-squared. Then, GA was used to further optimize the selected features to improve the accuracy of cancer classification. The early diagnosis of cognitive impairment in Alzheimer's disease is crucial to effective treatment and maintaining independence. [PERSON] and [PERSON] [32] focus on the classification of Alzheimer's disease, mild cognitive impairment, and normal control based on MRI images from the ADNI dataset. Various feature selection techniques are applied to different classifiers to improve classification performance. They used RFE and GA for feature selection and obtained better results with the latter. One of the topics that has been recently studied is detecting spam on Twitter proposes a method that simultaneously performs dimensionality reduction and hyperparameter optimization to create a spam prediction model [33]. They have used a modified GA to achieve better performance than Chi2 and PCA feature selection methods, using less than 10% of the total feature space. Detecting spam effectively requires identifying important features that can accurately represent the behavior of spammers. In this regard, [PERSON] and [PERSON] [34] proposed a method that uses GA to combine the selection of feature subsets with multiple evaluation metrics. This approach not only considers the features with the highest weight, but also pays attention to the features with the lowest weight. By comprehensively evaluating all the different evaluation metrics, this method generates an appropriate feature subset that has improved the efficiency of spam detection. [PERSON] et al. [35] proposed a model with which to detect malicious domains by passively analyzing DNS data and used a GA to select numerous features of DNS data. They then developed a real-time, accurate, and fast classifier by combining a two-step quantum ant colony optimization (QABC) algorithm with the selected features. In text processing, feature selection is an approach used to reduce dimensions because high dimensionality is a significant problem. [PERSON] et al. [36] used a GA to reduce dimensions in their study and compared it with other filtering methods. Hybrid and ensemble GAFS approaches are also proposed by researchers. For example, [PERSON] and [PERSON] [37] proposed a hybrid intelligent approach for predicting phishing websites using feature selection and weighting methods based on EAs and Deep Neural Networks (DNN). To improve the accuracy of phishing website prediction, they heuristically determined the most effective features and optimal weights of website features with GA. The website features selected and weighted by the GA were then used to train DNN to accurately predict the phishing website. The proposed approach achieved higher accuracy, sensitivity, and specificity in the prediction of phishing websites than other studies. [PERSON] et al. [38] propose a model that is a hybridization of three algorithms: Binary Genetic Algorithm (BGA), Electromagnetism-like Mechanism (EM), and k-means for educational data analysis. The BGA selection mechanism has been modified on the basis of EM and k-means algorithms. Enhanced BGA has been employed as a wrapper feature selection algorithm to reduce the dimensionality of the data and remove irrelevant or redundant features, improving the performance of ML classifiers. [PERSON] et al. [39] present the EFS-BGA algorithm, which is an approach to the selection of ensemble characteristics based on a GA. Following the generation of a feature subset by each base feature selector, the EFS-BGA technique employs a GA in order to acquire weights that are optimal for each feature subset.