## 3 Proposed RBAVO-DE for GS
### The Complete RBAVO-DE Algorithm
#### 4.4.2 Results Employing the SVM Model

Table 5 displays the outcomes of the proposed RBAVO-DE and various optimization techniques employing the SVM model concerning classification accuracy results assessed under identical running conditions. The experimental outcomes demonstrate that the proposed RBAVO-DE algorithm outperformed the other approaches by obtaining the most promising values for four datasets. All competitive methods, including the proposed RBAVO-DE employing SVM, obtained equivalent values across 18 benchmarks. The rest of the metrics utilizing SVM are presented in Appendix A.2. Regarding the mean fitness values of the proposed RBAVO-DE and its counterparts employing the SVM model under equivalent running conditions, the proposed RBAVO-DE proved to be more efficient than the other methods. The proposed RBAVO-DE employing the SVM model yielded the lowest fitness values and the most competitive STDE across all benchmarks. Also, all utilized benchmarks are high-dimensional, demonstrating that the proposed RBAVO-DE can run effectively on all benchmarks regardless of size. The RBAVO-DE algorithm shows promise by effectively balancing exploitation and exploration of the search space, avoiding becoming trapped in local optima during iterations. Unlike many other algorithms that tend to become trapped, it demonstrated the ability to escape such traps. Regarding the mean results for the genes selected by the proposed RBAVO-DE and its counterparts employing SVM, the proposed RBAVO-DE achieved more promising results than other techniques across all benchmarks utilized in this study. Also, the superiority of the proposed RBAVO-DE employing SVM in this context demonstrates its capability to effectively explore valuable regions of the search space while avoiding regions with non-feasible solutions. With regard to the average precision values of the proposed RBAVO-DE and its counterparts employing SVM, the proposed RBAVO-DE outperformed the alternative approaches for three of the twenty-two datasets. On the other hand, for eighteen datasets, BABC, BGWO, BWOA, BBSA, and BASO yielded comparable results. For seventeen datasets, BSSA, BPSO, BGOA, and BHHO yielded results similar to those of RBAVO-DE, thereby ranking fourth. Ultimately, BBA ranked lowest among all approaches, producing similar outcomes to those of the proposed RBAVO-DE for twelve datasets. In terms of the mean recall of the proposed RBAVO-DE method and its counterparts using SVM, the proposed RBAVO-DE outperformed the alternative approaches for three of the twenty-two datasets. In contrast, BPSO and BHHO performed similarly for eighteen datasets, while BSSA, BABC, BGWO, BWOA, BGOA, and BBSA produced the same results as the proposed RBAVO-DE for nineteen datasets. BHGSO, which obtained the same outcomes as RBAVO-DE for seventeen datasets, ranked fourth. In the end, BASO and BBA ranked lowest among all approaches, producing outcomes similar to those of RBAVO-DE for sixteen datasets. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \hline \hline
**Dataset** & **Metric** & **RBAVO-DE** & **BSSA** & **BABC** & **BPSO** & **BBA** & **BGWO** & **BFOO, **BBOA** & **BHO** & **BBSA** & **BAGSO** & **BHHO** & **BBSA** & **BAGSO** \\ \hline PCPG & Average & **1.000** & **1. Regarding the mean F1-score of the proposed RBAVO-DE algorithm and its counterparts employing SVM, the proposed RBAVO-DE outperformed the alternative approaches for five of the twenty-two datasets. On the other hand, BWOA produced outcomes similar to those of the proposed RBAVO-DE for seventeen datasets, while BSSA, BABC, BPSO, BGWO, BGOA, and BBSA produced comparable results for sixteen datasets. BHHO, which obtained the same outcomes as the proposed RBAVO-DE for fifteen datasets, ranked fourth. Ultimately, BBA ranked lowest among all approaches, producing similar outcomes to those of RBAVO-DE for 12 datasets.