# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 2 Related Work  
It is unusual for all properties in a considered dataset to be useful when designing an ML platform in real life. The inclusion of unwanted and redundant attributes lessens the model's classification capability and accuracy. As more factors are added to an ML framework, its complexity increases [19; 20]. By finding and assembling the ideal set of features, FS in ML aims to produce useful models of a problem under study and consideration [21]. Some important advantages of FS are [10; 12]:  
* reducing overfitting and eliminating redundant data,
* improving accuracy and reducing misleading results, and
* reducing the ML algorithm training time, dropping the algorithm complexity, and speeding up the training process. The prime components of an FS process are presented in Figure 1 and they are [18] as follows. 1. Searching Techniques: To obtain the best features with the highest accuracy, searching approaches are required to be applied in an FS process. Exhaustive search, heuristic search, and evolutionary computation are a few popular searching methods. An exhaustive search is explored in a few works [19; 20]. Numerous heuristic strategies and greedy techniques, such as sequential forward selection (SFS) [22], and sequential backward selection (SBS), have therefore been used for FS [23]. However, in later parts of the FS process, it could be impossible to select or delete eliminated or selected features because both SFS and SBS suffer from the \"nesting effect\" problem. After being selected, features in the SFS method cannot be discarded later, while the features discarded in the SBS cannot be selected again. These two approaches can be compromised by using SFS \(l\) times and then applying SBS \(r\) times [24]. The nesting effect can be reduced by such a method, but the correct values of \(l\) and \(r\) must be determined carefully. Sequential backward and forward floating methods were presented to avoid this problem [22]. A two-layer cutting plane approach was recently suggested in [23] to evaluate the best subsets of characteristics. In [24], an exhaustive FS search with backtracking and a heuristic search was proposed. Various EC approaches have been proposed in recent years to tackle the challenges of the FS problems successfully. Some of them are differential evolution (DE) [25], genetic algorithms (GAs) [26], grey wolf optimization (GWO) [27; 28], ant colony optimization (ACO) [29; 30; 31], binary Harris hawks optimization (BHHO) [32; 33] and improved BHHO (IBHHO) [34], binary ant lion optimization (BALO) [35; 36], salp swarm algo \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Parameters** & \\ \hline DBI & Davies–Bouldin Index \\ \hline DI & Dunn Index \\ \hline SI & Silhouette Index \\ \hline
**Others** & \\ \hline EC & Evolutionary Computation \\ \hline FS & Feature Selection \\ \hline ML & Machine Learning \\ \hline MO & Multiobjective \\ \hline MRMR & Minimum Redundancy Maximum Relevance \\ \hline OBL & Opposition-Based Learning \\ \hline SO & Single Objective \\ \hline WRS & Wilcoxon’s Rank Sum \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._rithm (SSA) [37], dragon algorithm (DA) [38], multiverse algorithm (MVA) [39], Jaya optimization algorithms such as the FS based on the Jaya optimization algorithm (FSJaya) [40] and the FS based on the adaptive Jaya algorithm (AJA) [41], grasshopper swarm intelligence optimization algorithm (GOA) and its binary versions [42], binary teaching learning-based optimization (BTLBO) [43], harmony search (HS) [44], and the vortex search algorithm (VSA) [45], etc. All these techniques have been applied for performing FS on various types of datasets, and they have been demonstrated to achieve high optimization rates and to increase the CA. EC techniques require no domain knowledge and do not presume whether the training dataset is linearly separable or not. Another valuable aspect of EC methods is that their population-based process can deliver several solutions in one cycle.