## 2 Method Description
### The Second Phase of the Proposed Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \(N_{g}=25\) & \(N_{g}=50\) & \(N_{g}=100\) & \(N_{g}=200\) \\ \hline Alcohol & 38.19\% & 34.98\% & 32.45\% & 31.28\% \\ \hline Appendicitis & 15.53\% & 15.40\% & 16.00\% & 15.27\% \\ \hline Australian & 23.92\% & 23.77\% & 22.56\% & 21.00\% \\ \hline Balance & 19.08\% & 16.71\% & 13.57\% & 12.95\% \\ \hline Cleveland & 51.08\% & 50.85\% & 51.26\% & 50.82\% \\ \hline Circular & 4.76\% & 4.94\% & 5.13\% & 4.19\% \\ \hline Dermatology & 36.12\% & 36.17\% & 35.81\% & 36.13\% \\ \hline Hayes-Roth & 37.85\% & 35.56\% & 33.56\% & 33.54\% \\ \hline Heart & 15.58\% & 15.62\% & 15.52\% & 15.33\% \\ \hline HeartAttack & 20.28\% & 19.14\% & 18.89\% & 18.52\% \\ \hline HouseVotes & 3.46\% & 3.81\% & 3.78\% & 3.74\% \\ \hline Ionosphere & 9.16\% & 8.23\% & 7.95\% & 7.39\% \\ \hline Liverdisorder & 27.77\% & 28.23\% & 28.26\% & 27.92\% \\ \hline Lymphography & 19.62\% & 20.26\% & 20.62\% & 20.64\% \\ \hline Mammographic & 16.99\% & 16.90\% & 16.92\% & 17.21\% \\ \hline Parkinsons & 14.58\% & 14.51\% & 13.76\% & 15.35\% \\ \hline Phoneme & 17.29\% & 17.45\% & 17.21\% & 16.62\% \\ \hline Pima & 23.83\% & 24.09\% & 23.87\% & 23.59\% \\ \hline Popfailures & 4.93\% & 4.61\% & 4.70\% & 4.80\% \\ \hline Regions2 & 25.65\% & 25.74\% & 25.60\% & 25.54\% \\ \hline Saheart & 28.50\% & 29.23\% & 29.07\% & 29.64\% \\ \hline Segment & 41.81\% & 42.77\% & 43.65\% & 40.83\% \\ \hline Sonar & 21.77\% & 20.45\% & 19.60\% & 18.25\% \\ \hline Spiral & 31.82\% & 28.09\% & 26.07\% & 22.52\% \\ \hline StatHeart & 20.05\% & 20.09\% & 19.93\% & 19.52\% \\ \hline Student & 4.43\% & 4.62\% & 5.21\% & 5.11\% \\ \hline Transfusion & 24.82\% & 24.72\% & 24.39\% & 24.59\% \\ \hline Wdbc & 4.97\% & 5.10\% & 5.25\% & 5.00\% \\ \hline Wine & 7.80\% & 7.37\% & 7.69\% & 7.71\% \\ \hline Z\_F\_S & 3.52\% & 3.55\% & 3.59\% & 3.16\% \\ \hline Z\_O\_N\_F\_S & 49.56\% & 48.04\% & 47.77\% & 46.77\% \\ \hline ZO\_NF\_S & 4.19\% & 3.97\% & 3.77\% & 3.63\% \\ \hline ZONF\_S & 2.22\% & 1.92\% & 2.00\% & 1.79\% \\ \hline ZOO & 8.57\% & 8.40\% & 7.60\% & 4.50\% \\ \hline
**Average** & **19.99\%** & **19.57\%** & **19.21\%** & **18.67\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results using the proposed method and a series of numbers of generations for the classification datasets. Figure 12 presents the significance levels p for the experiments conducted on the classification datasets with various models. The results indicate that the difference between \(N_{g}=25\) and \(N_{g}=50\) is not statistically significant, as \(p\) is 0.12. In contrast, the transition from \(N_{g}=50\) to \(N_{g}=100\) is marginally significant, with \(p=0.048\), suggesting that the increase in generations begins to influence model performance. Finally, the difference between \(N_{g}=100\) and \(N_{g}=200\) shows high statistical significance, with \(p=0.00083\), confirming that the increase in the number of generations significantly contributes to performance improvement. In Table 9, the experimental findings suggest that the convergence behavior of the genetic algorithm on regression datasets shows varied patterns as the number of generations \(N_{g}\) increases from 25 to 200. Specifically, a reduction in error values is observed on several datasets, while on others, errors either increase or remain stable. For instance, on the \"Abalone\" dataset, the error decreases steadily from 6.08 at \(N_{g}=25\) to 5.10 at \(N_{g}=200\), indicating improved performance. On the \"Friedman\" dataset, there is a significant reduction from 3.22 to 1.45, demonstrating a clear trend of convergence toward optimal solutions.