# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 4 Analysis and Discussion  
According to the analysis of the mentioned articles, the majority of studies employed the wrapper strategy mainly due to its supremacy in terms of higher accuracy compared to filter techniques, which have consistently been shown through experimentation that perform inaccurate filtration. In an effort to utilize all the advantages from both approaches, numerous researchers have tried to integrate and hybridize filter and wrapper methods. Figure 4 displays the number of papers broken down by the evolutionary methods that were employed in the corresponding studies. These results demonstrate unequivocally that PSO is utilised for fusion in the most number of research articles (11). This is most likely caused by the PSO's lack of derivatives and its simpler concept and coding technology compared to other evolutionary methods. In particular, in contrast to the other competing evolutionary methods, PSO uses two acceleration coefficients (i.e., the cognitive and social parameters, respectively) and an inertia weight, and thus in PSO there are few parameters required to be adjusted. Furthermore, on average the convergence rate of PSO is faster than other stochastic algorithms [96]. Feature-selection techniques can be applicable to any area where there is a chance of facing the \"curse of dimensionality problem\". However, after studying the presented works (Figure 5), we found that most of the hybrid FS techniques (54%) have verified  
Figure 4: Number of papers per technique. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline
**Paper** & **Classifier** & **Description** \\ \hline P20 [9] & Bayesian & The Bayesian classification model forecasts membership estimates for every group, such as the likelihood that a certain record contributes to a certain class. The category with the greatest likelihood is thought to be the most probable. \\ \hline P32 [93], P33 [94] & DT, J48 classifier & [PERSON]’s DT is generated by using C4.5 (J48). C4.5 is an extension of the previous ID3 algorithm of [PERSON]. For classification, the DTs generated by C4.5 can be used and, hence, C4.5 is also called a statistical classifier. \\ \hline P6 [69], P28 [89] & ANN & To provide computer programs with the ability to process information and make judgments similarly to those of humans, ANN models are used in AI to simulate the networks of neurons that make up the human brain. In order to develop an ANN, an artificial model is designed and programmed to operate analogously to a network of interconnected neurons and brain cells. \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._their performance by considering some benchmark datasets. Only 22% of the total articles have applied their technique to the biomedical area (microarray gene selection, disease diagnosis etc.). Additionally, Figure 6 displays the quantity of papers by using different standard classifiers as wrappers. Because it is simple to grasp and requires less calculation time, 21 out of 35 research employed KNN as a wrapper in their fitness computation procedure. KNN's training procedure is also incredibly fast because it makes decisions without using any training data. Additionally, the bulk of FS researchers using hybrid evolutionary techniques have the goal of reducing both the number of features and the error rate. This is a challenging task,  
Figure 5: Distribution of papers according to application area. Figure 6: Number of papers vs. classifier. despite the inadequacies of many ECs. The recommended hybrid approaches, however, surpass the current FS methodology after hybridization, which looks for comparable alternatives to produce the best results when tackling optimization tasks, according to tests performed on various datasets. By combining and merging the exploration and exploitation processes, this is achieved. Overall, the previous studies have led to several enhancements and alterations, and, in short, each specific research approach requires the employment of unique approaches in order to provide the required outcomes.