## 2 Materials and Methods
### Tuning Model Hyperparameters  
Following the exploratory data analysis stage, a random seed was generated and stored to ensure reproducibility. The dataset was then categorized into _ELOS_ classes, as outlined in Table 1, to maintain the robustness of the subsample distributions. A stratified sampling approach was applied, dividing the dataset into training and testing subsets in a 5:1 ratio, ensuring that both subsets preserved the statistical properties of the original distribution. Given that distribution skewness and statistical outliers can hinder model training, and considering that independent variables typically exhibit varying scales--where larger numerical values might be incorrectly interpreted as more important than smaller ones--it was necessary to apply an appropriate distribution transformation. Since different machine learning models respond variably to different transformations, standard scaling, robust scaling, and power scaling were evaluated. The selected transformation was fitted to the X_train dataset and subsequently applied to both the X_train and X_test datasets, ensuring that the transformation parameters were learned solely from the training data. Additionally, the impact of transforming the y_train target variable was assessed for each model to determine whether it contributed to improved learning performance. Each AI-based model was trained using a genetic algorithm (GA) to optimize the set of hyperparameters outlined in Figure 2. To ensure reliable model evaluation and prevent overfitting, a 5-fold cross-validation procedure was applied during the training phase. The optimization objective was to minimize the mean absolute error (\(MAE\)), thus guiding the GA toward hyperparameter configurations that enhance predictive accuracy. To enhance the robustness of the results, the entire cross-validation process was repeated five times, with the data being randomly shuffled before each iteration. The final \(MAE\) score for each hyperparameter configuration was computed as the average across all folds and repetitions. Given that each set of hyperparameters is represented as a chromosome within the genetic algorithm framework--similar to the example illustrated in Figure 3--this entire training procedure was executed for each individual across all generations. Consequently, multiple hyperparameter configurations were evaluated for each model, and in each generation, the best-performing individual (i.e., the hyperparameter set yielding the lowest \(MAE\)) was stored. This iterative optimization process ultimately yielded the optimal hyperparameter configuration for each model in the studied training dataset. Pseudocode 1 provides a representation of the hyperparameter tuning procedure. **Pseudocode 1. ** Hyperparameter tuning procedure. **Input:** A _dataset_ with independent (\(X\)) and dependent (\(y\)) variables; a genetic algorithm \(GA\); AI-based models; hyperparameters; _Metrics_. **Exploratory data analysis:**  
Checking for the presence of noise data and outliers, identifying the important variables, and performing feature engineering, if necessary. ```
**Pseudocode 1. ** Cont. ``` Training and tuning the models:  Random _Seed_ generation and storage. Stratification of \(X/y\to X_{train}/y_{train}\) (5/6 samples); \(X_{test}/y_{test}\) (1/6 samples). Fitting and Transformation of \(X_{train}\to X_{train-transformed}\) (and \(y_{train}\to y_{train-transformed}\),  if necessary). Transformation of \(X_{test}\to X_{test-transformed}\) (and \(y_{test}\to y_{test-transformed}\), if necessary). Regardless of whether there is a transformation or not, they will be called \(y_{train}\) and \(y_{test}\) here. Let \(Model_{i}\in I\) be the AI-based models, \(H_{i}\) be the hyperparameters of \(Model_{i}\), \(p\in P\) be the population size, and \(r\in R\) be the repetitions, where \(R=5\).