# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 1 Introduction  
Feature selection (FS) is a method that aims to choose the minimum required features that can represent a dataset by selecting those features that add the most to the estimation variable that falls within the user's field of interest [1]. The volume of data available has risen significantly in recent years due to advancements in data gathering techniques in different fields, resulting in increased processing time and space complexity needed for the implementation of architectures in the realms of machine learning (ML). The collected data in many domains typically is of high dimensionality, making it impossible to select an optimum range of features and exclude unnecessary ones. The employed ML models are forced to learn insignificantly as a result of inappropriate features in the dataset, which leads to a poor recognition rate and a large drop in outcomes. By removing unnecessary and outdated features, FS reduces the dimensionality and improves the quality of the resulting attribute vector [2; 3; 4]. FS has been used for various purposes, including cancer classification (e.g., to improve the diagnosis of breast cancer and diabetes [5]), speech recognition [6], gene prediction [7], gait analysis [8], and text mining [9], etc. FS has a pair of essential opposing goals, namely, reducing the number of needed characteristics and maximizing the performance of classification to overcome the curse of dimensionality. The three principal kinds of any FS strategy are filter, wrapper, and embedded methods, which integrate both filters and wrappers [10; 11]. A filter technique isindependent of any ML algorithm. It is appropriate for datasets containing fewer features, and it often requires low-performance computing capabilities. In filtering approaches, the association among classifiers and attributes is not considered, and thus filters often fail to detect the samples correctly during the learning process. Many studies have used wrappers to address these problems. A wrapper technique frequently alters the training process and uses classifiers as assessment mechanisms. Thus, wrapper techniques for FS often affect the training algorithm and produce more precise results than filters. Wrappers put effort into training the employed ML algorithm by using only a subset of the features that are also needed for determining the training model performance. Depending on the selection accuracy determined in each preceding phase, a wrapper algorithm considers either adding or removing a feature from the selected number of features. As a result, wrapper methods are often more computationally complex and more expensive than most filtering techniques. Conventional wrapper approaches [12] take a set of attributes and require the user to include arguments as parameters, after which the most informative attributes are chosen from a set of features proportional to the arguments provided by the user. The limitations of such techniques are that the selected feature vector is recursively evaluated, in which case certain characteristics are not included at the first level for assessment. In addition, arguments are specified by the user, and thus certain feature mixtures cannot be taken into account even with more precision. These issues may cause searching overhead along with overfitting. Evolutionary wrapper approaches, which are more common when the search area is very broad, have been created to address the drawbacks of classic wrapper methods. These approaches have many benefits over conventional wrapper methods, including the fact that they need fewer domain details. Evolutionary optimization techniques are population-based metaheuristic strategies that can solve a problem with multiple candidate solutions described by a group of individuals. Each entity in the FS tasks represents a part of the feature vector. An objective (target) function is employed to evaluate and assess the consistency of every candidate solution.