## 6 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Evaluation Metric** & **GbQGA** & **GbQGA std** & **FbQGA** & **FbQGA std** & **SSD** \\ \hline \multirow{8}{*}{Musk} & F1 & 0.6756 & 4.48\% & **0.6965** & 5.48\% & **yes** \\ \cline{2-6}  & Precision & 0.6331 & 4.10\% & **0.654** & 4.15\% & **yes** \\ \cline{2-6}  & Recall & 0.7242 & 6.13\% & **0.745** & 6.13\% & no \\ \cline{2-6}  & Accuracy & 0.6912 & 7.19\% & **0.712** & 6.37\% & no \\ \cline{2-6}  & Avg. No. of Selected Features & 24.3 & 25.70\% & **22.13** & 24.97\% & **yes** \\ \cline{2-6}  & Running Time (s) & **213.4605** & 14.59\% & 215.4651 & 15.83\% & no \\ \hline \multirow{8}{*}{Parkinson’s Disease} & F1 & 0.6759 & 2.70\% & **0.6822** & 2.72\% & **yes** \\ \cline{2-6}  & Precision & 0.6673 & 2.83\% & **0.6872** & 2.34\% & **yes** \\ \cline{2-6}  & Recall & 0.6855 & 2.89\% & **0.7031** & 2.61\% & **yes** \\ \cline{2-6}  & Accuracy & 0.7441 & 2.44\% & **0.7731** & 2.74\% & **yes** \\ \cline{2-6}  & Avg. No. of Selected Features & 244.67 & 26.43\% & **236.91** & 25.24\% & **yes** \\ \cline{2-6}  & Running Time (s) & 80.1235 & 12.12\% & **80.1122** & 16.05\% & no \\ \hline \multirow{8}{*}{Backdoor} & F1 & 0.8392 & 3.25\% & **0.8422** & 1.94\% & no \\ \cline{2-6}  & Precision & **0.9405** & 1.43\% & 0.9205 & 5.10\% & no \\ \cline{2-6}  & Recall & 0.7576 & 3.22\% & **0.7762** & 2.08\% & no \\ \cline{2-6}  & Accuracy & **0.9647** & 1.57\% & 0.9547 & 2.20\% & no \\ \cline{2-6}  & Avg. No. of Selected Features & 18.80 & 5.17\% & **12.50** & 3.78\% & **yes** \\ \cline{2-6}  & Running Time (s) & **198.99** & 23.23\% & 212.35 & 23.96\% & no \\ \hline \multirow{8}{*}{Campaign} & F1 & 0.6613 & 2.49\% & **0.6934** & 1.96\% & no \\ \cline{2-6}  & Precision & 0.6553 & 2.50\% & **0.6826** & 1.63\% & no \\ \cline{2-6}  & Recall & 0.6726 & 3.23\% & **0.7046** & 3.18\% & no \\ \cline{2-6}  & Accuracy & 0.8573 & 1.61\% & **0.8733** & 1.20\% & no \\ \cline{2-6}  & Avg. No. of Selected Features & 15.40 & 2.01\% & **10.70** & 4.32\% & **yes** \\ \cline{2-6}  & Running Time (s) & **88.50** & 15.32\% & 92.83 & 16.06\% & no \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Evaluation Metric** & **IbQGA** & **IbQGA std** & **FbQGA** & **FbQGA std** & **SSD** \\ \hline \multirow{8}{*}{Taiwan Bankruptcy} & F1 & 0.5643 & 2.17\% & **0.5886** & 1.17\% & **yes** \\ \cline{2-6}  & Precision & 0.55 & 1.33\% & **0.5663** & 1.32\% & **yes** \\ \cline{2-6}  & Recall & 0.5793 & 4.90\% & **0.6127** & 3.06\% & **yes** \\ \cline{2-6}  & Accuracy & 0.872 & 3.15\% & **0.8961** & 4.50\% & no \\ \cline{2-6}  & Avg. No. of Selected Features & 32.05 & 25.70\% & **31.52** & 23.12\% & no \\ \cline{2-6}  & Running Time (s) & 35.7801 & 13.92\% & **33.1522** & 15.90\% & no \\ \hline \multirow{8}{*}{Musk} & F1 & 0.6762 & 4.48\% & **0.6965** & 5.48\% & **yes** \\ \cline{2-6}  & Precision & 0.642 & 5.10\% & **0.654** & 4.15\% & no \\ \cline{2-6}  & Recall & 0.7245 & 6.13\% & **0.745** & 6.13\% & no \\ \cline{2-6}  & Accuracy & 0.6912 & 7.37\% & **0.712** & 6.37\% & no \\ \cline{2-6}  & Avg. No. of Selected Features & 30.12 & 21.13\% & **22.13** & 24.97\% & **yes** \\ \cline{2-6}  & Running Time (s) & 226.6581 & 17.72\% & **215.4651** & 15.83\% & no \\ \cline{2-6}  & \multicolumn{1}{c}{} & & & & \\ \cline{2-6}  & Running Time (s) & **88.50** & 15.32\% & 92.83 & 16.06\% & no \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of IbQGA and FbQGA (std: standard deviation; SSD: Significant Statistical Differences; the best performance is highlighted in “Bold”). All the implementations are done in Python 3.9.19. The experiments are run on an Intel Core i7-1185G7 @ 3.00 GHz (8 CPUs) and 16 GB RAM. The fitness function was defined in Equation (6) with \(\lambda\) and \(err\) as 0.99 and \(1-\mathrm{F1}\), respectively. Due to the stochastic nature of metaheuristic algorithms, the experiment was done for 30 runs on each method and the average of the simulated results was recorded.