# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 3 A Brief Survey
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Search Method** & **Fitness Function** & **Means of Hybridization** \\ \hline P23 [84] & SCA, Binary PSO (BPSO) & \(\frac{f(x)-p(x)}{\max\left\{f(x),p(x)\right\}}\). & The methodology is used in this article to increase the search capabilities and find a close-to-optimum global solution by combining the BPSO with the SCA. In this context, SCA improves the movement of a particle in the BPSO. \\ \hline P24 [85] & GA, PSO & ANN & The procedure is applied three times at a time and continued until the given number of generations is reached after producing the initial population and determining the cost of each solution. The GA and PSO follow these three moves. Two steps are taken concurrently in the GA, while the PSO takes only a single move. \\ \hline P25 [86] & ALO, GWO & \(f_{\theta}=\alpha\cdot E+(1-\alpha)\frac{\
abla_{\theta}}{N}\), where \(f_{\theta}\): fitness function considering \(\theta\) number of features, N: total feature count, E: CE, \(\alpha\): constant. \\ \hline P26 [87] & ALO, DE & \(F=(\max\{ECT_{ik}\}\&\min\{R_{lk}\})\) \\  & & \(\forall\in[1,N_{sk}]\) mapped to \(k_{\text{th}}\,VM\), where \(k=1,2,3,\ldots,N_{\text{sm}}\) \\  & & \(i=1,2,3,\ldots,N_{\text{tsk}}\) \\  & & \(ECT_{\text{st}}\): required RT, \(N_{\text{sm}}\): \\  & & \#virtual machines, \(N_{\text{tsk}}\): \\  & & \#tasks. \\ \hline P27 [88] & PSO and CSA & \(Fitness=\alpha\Delta_{R}(D)+\beta\frac{|Y|}{|T|}\), where \(\alpha\Delta_{R}(D)\): classifier’s error rate, \(|Y|\): size of the subset, and \(|T|\): \#total features. \\ \hline P27 [88] & PSO and CSA & \(Fitness=\alpha\Delta_{R}(D)+\beta\frac{|Y|}{|T|}\), where \(\alpha\Delta_{R}(D)\): classifier’s error rate, \(|Y|\): size of the subset, and \(|T|\): \#total features. \\ \hline P27 [88] & SSA, TLBO & \(f(X_{i})=(E_{x_{i}}\times(1+0.5\times\frac{S}{N}))^{2}\), where \(f(X_{i})\): fitness value of \(x_{i}\). \(E_{x_{i}}\): performance of the classifier, S/N: \#features selected/\#total features. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Search Method** & **Fitness Function** & **Means of Hybridization** \\ \hline P29 [90] & HHO-CS & \(Fitness=\alpha+\beta\frac{|\beta|}{|\zeta|}-G\), where \(R\): CE, C: total attribute count, \(\beta\): size of subset, \(\alpha\): performance of the classification. & The merits of the CS approach for controlling HHO vectors in place is taken in the CHHO-CS algorithm. CS attempts to determine the optimal solution after each iteration T. As a result, if the fitness of the current solution is greater than that of the new solution derived from HHO, the new solutions will be determined; otherwise, the older one will stay intact. & SCA and HHO are paired to execute their discovery task by SCA and exploitation by HHO. \\ \hline P30 [91] & SCA-HHO & \(f_{i}=w_{1}\times\epsilon_{i}+w_{2}\times\frac{d}{D},w_{1}=0.99,w_{2}=1-w_{1}\), where \(\epsilon_{i}\): error by KNN, \(d_{i}\): count of attributes picked, \(D\): actual \#feature. & \begin{tabular}{c} \end{tabular}  \\ \hline P31 [92] & GWO-HHO & \(Fitness=\alpha(ER)+(1-\alpha)\frac{|\beta_{f}|}{|f_{i}|}\), where \(\alpha\) lies between 0 and 1, ER: error, \(S_{f}\): \#picked factors and \(T_{f}\): \# actual factors. & Exploration is carried out by HHO, while exploitation is done by GWO. \\ \hline P32 [93] & ACO-ABC & \(fit_{j}=\frac{1}{1+f_{i}^{\prime}}\), where \(f_{j}\): value of the objective for the corresponding attribute set. & The ants use the bees’ exploitation to decide the best ant and optimal attribute substring; bees incorporate the attribute substring that the ants create as a supply of food. & The ants use the bees’ exploitation to decide the best ant and optimal attribute substring; bees incorporate the attribute substring that the ants create as a supply of food. \\ \hline P34 [95] & ACO-CS & Mean square error (MSE) of SVM. & ACO is an excellent evolutionary strategy.