## 2 Method Description
### The Second Phase of the Proposed Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \(F=1.5\) & \(F=3.0\) & \(F=5.0\) & \(F=10.0\) \\ \hline Wdbc & 4.94\% & 4.92\% & 4.90\% & 5.00\% \\ \hline Wine & 10.90\% & 9.37\% & 8.51\% & 7.71\% \\ \hline Z\_F\_S & 4.13\% & 3.73\% & 3.67\% & 3.16\% \\ \hline Z\_O\_N\_F\_S & 46.00\% & 45.61\% & 46.57\% & 46.77\% \\ \hline ZO\_NF\_S & 3.67\% & 4.19\% & 3.16\% & 3.63\% \\ \hline ZONF\_S & 2.59\% & 2.37\% & 2.06\% & 1.79\% \\ \hline ZOO & 11.17\% & 8.10\% & 8.00\% & 4.50\% \\ \hline
**Average** & **20.32\%** & **18.88\%** & **18.58\%** & **18.67\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \(F=1.5\) & \(F=3.0\) & \(F=5.0\) & \(F=10.0\) \\ \hline Abalone & 6.39 & 5.79 & 5.57 & 5.10 \\ \hline Airfoil & 0.004 & 0.004 & 0.004 & 0.004 \\ \hline Auto & 9.83 & 9.69 & 9.67 & 9.68 \\ \hline Baseball & 84.27 & 83.01 & 84.57 & 86.19 \\ \hline BK & 0.275 & 0.048 & 0.071 & 0.153 \\ \hline BL & 0.41 & 0.0005 & 0.0003 & 0.0002 \\ \hline Concrete & 0.006 & 0.006 & 0.006 & 0.006 \\ \hline Dee & 0.16 & 0.16 & 0.16 & 0.16 \\ \hline Housing & 16.75 & 17.82 & 18.07 & 18.70 \\ \hline Friedman & 6.59 & 3.85 & 1.67 & 1.45 \\ \hline FA & 0.054 & 0.03 & 0.053 & 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results for regression datasets using different values for parameter \(F\). In Figure 10, which compares different values of the parameter \(F\) for the classification datasets, several statistically significant differences are observed. The p-values for the comparisons \(F=1.5\) vs. \(F=3.0\) (\(p=0.00019\)), \(F=1.5\) vs. \(F=5.0\) (\(p=0.00012\)), and \(F=1.5\) vs. \(F=10.0\) (\(p=0.00069\)) indicate a strong difference in the model's performance. In contrast, the values for the comparisons \(F=3.0\) vs. \(F=5.0\) (\(p=0.027\)), \(F=3.0\) vs. \(F=10.0\) (\(p=0.062\)), and \(F=5.0\) vs. \(F=10.0\) (\(p=0.23\)) show that the differences between larger values of the parameter \(F\) are less significant. \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & \(F=1.5\) & \(F=3.0\) & \(F=5.0\) & \(F=10.0\) \\ \hline FY & 0.216 & 0.246 & 0.332 & 0.077 \\ \hline HO & 0.01 & 0.01 & 0.01 & 0.01 \\ \hline Laser & 0.022 & 0.011 & 0.005 & 0.003 \\ \hline MB & 0.116 & 0.135 & 0.307 & 5.49 \\ \hline Mortgage & 0.56 & 0.59 & 0.36 & 0.14 \\ \hline NT & 0.007 & 0.007 & 0.007 & 0.007 \\ \hline PL & 0.024 & 0.023 & 0.023 & 0.023 \\ \hline Plastic & 2.37 & 2.33 & 2.31 & 2.29 \\ \hline PY & 2.33 & 0.049 & 0.022 & 0.019 \\ \hline Quake & 0.036 & 0.036 & 0.036 & 0.036 \\ \hline SN & 0.028 & 0.04 & 0.025 & 0.024 \\ \hline Stock & 1.45 & 1.51 & 1.49 & 1.53 \\ \hline Treasury & 0.50 & 0.59 & 0.43 & 0.51 \\ \hline
**Average** & **5.52** & **5.25** & **5.22** & **5.48** \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._

Figure 10: Statistical comparison for the experimental results by the application of the proposed method with different values of parameter \(F\). The method was applied on the classification datasets. Figure 11 examines comparisons of the parameter \(F\) for the regression datasets and shows no statistically significant differences. The p-values for the comparisons \(F=1.5\) vs. \(F=3.0\) (\(p=0.18\)), \(F=1.5\) vs. \(F=5.0\) (\(p=0.15\)), \(F=1.5\) vs. \(F=10.0\) (\(p=0.21\)), \(F=3.0\) vs. \(F=5.0\) (\(p=0.7\)), \(F=3.0\) vs. \(F=10.0\) (\(p=0.54\)), and \(F=5.0\) vs. \(F=10.0\) (\(p=0.89\)) indicate that variations in the value of the parameter \(F\) do not significantly affect the model's performance on the regression datasets. This may suggest greater stability of the model to changes in this parameter compared to the classification datasets. ### Experiments on Number of Generations

In order to evaluate the convergence of the genetic algorithm, an additional experiment was conducted where the number of generations was altered from 25 to 200.