## 5 Experimental Results
### Evaluation Results
We emphasize that the mutation operation and correlation assessment improved the performance (accuracy and F1) of the proposed algorithm by providing opportunities to explore the search space more extensively and replacing existing solutions with better ones, but they led to a slower overall time compared to BCS. **Evaluation of distribution of fitness scores across iterations (box plots):** In our research, we also examined the performance of the MBHO algorithm using the box plots produced. The box plots were used to visualize the distribution of fitness scores across different iterations. They helped illustrate changes in the fitness scores over time, displaying the spread of values and central tendency. They provided important insights into the convergence behavior of the algorithm used. The tests were carried out based on the mean fitness score of each algorithm across a range of iterations (from 1 to 25). The box plots are depicted in Figures 6 and 7, which are based on the utilized datasets. The \(X\)-axis represents the number of iterations, while the \(Y\)-axis indicates the \(Acc\) value for each algorithm. To provide a clear analysis, we first introduce the key factors of the box plot. The box depicts the interquartile range, the line inside the box is the median value of the average fitness score, and the lines extending from the box indicate the variability outside the upper and lower quartiles. According to Figure 6, MBHO with Spearman's coefficient generally achieved the highest fitness scores across six datasets out of the eight datasets shown in this figure, although it exhibits considerable variability with certain datasets, suggesting fluctuating performance across different problem instances. Furthermore, according to Figure 7, MBHO with Spearman's coefficient tended to have the highest fitness scores across four datasets out of the six datasets shown in this second figure, with some variability observed. However, this variability indicates fluctuations in the performance across different datasets, highlighting the importance of considering the specific characteristics of each problem instance. Yet this fluctuation indicates that, even when MBHO--correlation starts with low-quality solutions, through iterative evolution, it tends to reach better areas in the search space over time. This highlights the adaptive and evolutionary nature of the algorithm, which can progressively improve its performance through iterations even when faced with initially suboptimal solutions. Regarding the fluctuating performance, the proposed approach, MBHO, achieved the highest fitness score and accuracy across most of the used datasets. Figure 4: The convergence curves of MBHO, BCS [37], MBHO with Spearman’s coefficient, and BCS with Spearman’s coefficient across eight datasets. The \(X\)-axis signifies the number of iterations, while the \(Y\)-axis denotes the average fitness value. The graph illustrates a swift convergence towards a solution set during the initial stages, also known as iterations. MBHO surpassed the other algorithms due to its emphasis on maximizing the fitness score. Moreover, with each iteration, the performance of MBHO with Spearman’s coefficient was enhanced, as it tends to discover solutions with higher values by incorporating the best solutions from the preceding iterations into the subsequent ones. The convergence curves of MBHO substantiate that MBHO outpaces BCS [37] in procuring superior solutions within the search space. **Outliers in Fitness Function Scores:** We further identified the outliers corresponding to the fitness scores carried out through our approach, as they significantly influenced the performance of the proposed model. Outliers are the points (i.e., fitness scores) that fall outside a certain range. Our approach mainly relies on an optimization algorithm; hence, outliers cause convergence complications, leading the algorithm to get stuck in sub-optimal areas of solutions in the search space.