## 5 Results and Discussion
### Random Forest on UCI Datasets
The performance of the PSO is a little closer at 93.1%, BBO at 93.092% and the GA performed the least at 93.081%. From the hyperparameters of the Cover Type dataset (Table 4E), it was observed that the random forest algorithm's accuracy can still be improved by increasing both \(m\) and \(n\) as those hyperparameters near the edges tend to have higher accuracies. The time column in Table 4 refers to the time it takes to execute the whole optimization run excluding the preprocessing step. Here, it can be observed that, while there is no consistent pattern, the SDSGA performed the best in the three datasets (BREAST CANCER, LETTER and ADULT). Especially in the case of the LETTER dataset, where it took 422 s, or roughly half the time it took the PSO algorithm to execute at (787 s). We note that the PSO algorithm performed the best in terms of its execution time at 14.02 s when used to optimize the CAR dataset. This may be because the CAR dataset is the lightest and least complicated of the datasets considered. However, the BO algorithm at 79.47 s performed the worst with a large margin in the CAR dataset as compared to the BBO algorithm, which comes next at 38.5 s. We also note that the execution time of GA was close to that of SDSGA in three of the datasets (CAR, BREAST CANCER and LETTER) except in the case of ADULT and COVER TYPE datasets where they are very far apart. The SDSGA performed uncharacteristically poorly in the COVER TYPE dataset. This could be attributed to the population's low diversity which makes it difficult for the selection algorithm to find suitable non-duplicate chromosomes to be selected as parents. This issue can be solved by using a different random seed or increasing the probability of mutation. The Iter column in Table 4 refers to the number of iterations it took to get the best solution. Because the optimal value is unknown in advance in this optimization problem, it is impossible to predict the number of iterations or fitness evaluations. What determines this is the budget such as the amount of time or number of fitness evaluations available for the optimization to be completed in hopes of obtaining a nearly optimal value within this budget. It has been observed that, because metaheuristic optimization algorithms are population-based, if the hyperparameter space is not large enough, these algorithms will converge very quickly to the optimal values. Because of its sequential nature, the BO tends to approach optimal values gradually. The progression of the BO algorithm in Figure 7 shows a straight line for the car dataset. This is due to the fact that the BO algorithm achieved its best solution during the first 15 fitness evaluations. Following that, it never achieved a higher value in any of the subsequent evaluations. It is observed in the BREAST CANCER dataset that, despite all algorithms being convergent to the same accuracy value, the number of iterations required to achieve this accuracy varies as it does in the other datasets. We note that GA's remains average across all datasets, in contrast to the proposed SDSGA, which performed competitively well against the other optimization algorithms.