# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 4 Results and Discussion
### Feature Selection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **No. of Features** & **No. of Samples** \\ \hline Breast cancer & 9 & 699 \\ Breast EW & 30 & 569 \\ Congress EW & 16 & 453 \\ Exactly & 13 & 1000 \\ Exactly2 & 13 & 1000 \\ HeartEW & 13 & 270 \\ Ionosphere EW & 34 & 351 \\ Krvskp EW & 36 & 3196 \\ Lymphography & 18 & 148 \\ M-of-n & 13 & 1000 \\ Penglung EW & 325 & 73 \\ Sonar EW & 60 & 208 \\ Spect EW & 22 & 267 \\ Tic-tac-toe & 9 & 958 \\ Vote & 16 & 300 \\ Waveform EW & 40 & 5000 \\ Wine EW & 13 & 178 \\ Zoo & 16 & 101 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The UCI datasets used in this evaluation. Table 7 displays the average number of selected features from datasets by different algorithms. The algorithm with fewer features is ranked as the best one. Firstly, WOASAT-2 was able to find appropriate solutions in two datasets, including Breast Cancer and Breast EW. In sharp contrast, EJAYA is rated as the best algorithm in Congress EW. Similarly, both WOASAT-2 and EJAYA demonstrate the best performance in the Exactly dataset. When compared to other algorithms, EJAYA is subsequently listed as the best algorithm due to its fewer features for Exactly2. However, in Heart EW, WOASAT-2 is slightly better than the other methods for finding the fewest features. The fewest features for the Ionosphere EW dataset are associated with ALO, and the proposed method is placed second. Another dataset is Krvskp EW, in which EJAYA achieves the best performance by finding the fewest features. For three datasets, Lymphography, M-of-N, and Tic-Tac-Toe, two algorithms, namely, WOASAT-2 and EJAYA, provide the same results. Interestingly, EJAYA is ranked as the best algorithm when compared to the other algorithms for six datasets, including Peng Lung EW, Sonar EW, Spect EW, Vote, Waveform EW, and Wine EW. Overall, most of the previous methods tend to become caught up in the local optimum when coping with this problem. Nevertheless, EJAYA, due to integrating a balanced search strategy, has better efficiency in escaping from the local optima and selects a set of features yielding satisfactory quality results. To evaluate the performance of the proposed strategy, a robust statistical test is required to ensure the reliability and effectiveness of the results. For this purpose, the Friedman test, a non-parametric method commonly used to compare multiple algorithms, is applied. This test assesses the performance of optimizers by assigning average rankings based on their results, in which a lower ranking indicates superior performance. Consequently, the objective is to determine which algorithm achieves the lowest rank, signifying the highest efficiency and reliability among the algorithms [23; 74; 75; 76; 77; 23]. According to the results presented in Table 8, the proposed method, EJAYA, achieves the lowest average rank, thereby outperforming the other algorithms under consideration. This demonstrates that EJAYA is the most effective optimization approach among the tested methods, validating its suitability for the intended application. The application of the Friedman test ensures that this conclusion is statistically significant and reliable, highlighting the robustness of the proposed strategy in various scenarios and applications.