## 4 Experimental Setup  
The experimental setup consists of multiple parts, each with respective configurations and settings. Data loading, dataset preprocessing, and feature extractors have previously been discussed in earlier sections. Following the extraction of features, they undergo a feature selection stage where each feature is treated as a Boolean variable. Depending on whether the value is true or false, the corresponding feature is either retained or discarded. These Boolean variables are collectively optimized by the optimization algorithms as well. The retained features are then used for the training of classifiers and subsequent testing. The resulting accuracy and other metrics such as the F1-score, precision, and recall are then fed into an objective function, which includes the number of selected parameters into computing a final resulting metric that is used by optimizers. The objective function is designed to equally prioritize both high accuracy and a lower number of selected features through the use of the geometric mean, detailed as follows:  
\[Fitness\ Score =1-\sqrt{Acc}\times\sqrt{FL} \tag{16}\] \[FL =1-\frac{SF}{TF}\]  
In this formulation, the fitness score represents the assessment of a specific solution set offered by the proposed optimization algorithm. The multi-objective function in Equation (16) denotes the geometric mean \(GM\) of two components: accuracy \(Acc\) and feature loss \(FL\). \(FL\) quantifies the proportion of features that are not retained for the classification task. It is calculated by subtracting 1 from the ratio of the number of selected features \(SF\) to the total number of features \(TF\). To align with the convention in optimization problems, where the objective is typically aimed towards zero, the fitness score is subtracted from 1. This approach yields a better potential solution in our optimization problem, achieving a desirable trade-off between accuracy and feature selection simultaneously. For each experiment, the optimization algorithms consist of the HSMO, HPSO, BPSO, and BSMO. This section includes a discussion and details of each of the remaining experiment components. The process illustrated in Figure 8 includes the remaining experimental setup block of the XAI framework. This framework takes parameters from each stage as indicated in order to provide the two-pronged interpretability functions. The classification stage XAI inputs correspond to the utilization of SHAP to calculate the feature importance, while the feature selection and extraction stage XAI inputs correspond to the backwards-mapped selection of features to make the decision more interpretable. The optimization flow pipeline indicates one iteration of the complete process. In effect, as metaheuristic algorithms are designed, the optimization process of both continuous and binary variables is carried out in multiple iterations so that the process improves iteratively (see Figure 9). Visualizing the process itself over each stage can be thought of as modifying the values of all optimization variables over the iterations. Figure 8: Optimization process flow. Solid Arrows represent the flow of the process between the stages of the CAD system, while Dashed Boxes represent the Hybrid Optimization Scheme and the XAI Framework, which operate in parallel with the process to optimize various parameters at its different stages. The Objective Function evaluates the model’s classification accuracy. Figure 9: The overall HSMO optimization process. The widening search space at the beginning of the iterations illustrates the diverse solutions explored by the population. Both regions pertaining to binary feature selection and continuous parameter optimization demonstrate convergence as the algorithm iterates. This convergence signifies the collaborative nature of the HSMO algorithm, where local leaders and the global leader guide the population towards more optimal solutions.