## 4 Conclusions  
This study proposed a practical framework for predicting dilution in underground mining operations by integrating machine learning (ML) models with genetic algorithms (GAs). Grounded in a statistically rigorous methodology, the proposed approach encompassed hyperparameter tuning, model training, testing, and validation, thereby enabling a comprehensive evaluation of predictive performance based on the dataset employed. Among the primary contributions of this work is the novel use of stacking models--constructed from individually optimized ML-based models--to address this task for the first time. The methodological pipeline, which includes stratified sampling to preserve the original distribution of the target variable, a repeated k-fold strategy to enhance cross-validation robustness, a 10-fold cross-validation procedure with 30 repetitions for reliable performance assessment, and the application of nonparametric statistical tests, constitutes a statistically sound framework that can support future research in the field. Despite the relatively limited dataset--comprising only 120 samples--and the computational constraints imposed by the use of a standard personal laptop, several ML-based models demonstrated remarkable predictive capabilities. Among the approaches evaluated, which included support vector machines (SVMs), tree-based models, and artificial neural networks (ANNs), the neural network-based models consistently outperformed the others in predicting dilution. The AGA-ANN-1 model with a population size of 20 individuals achieved \(MAE\), \(R^{2}\), and \(RMSE\) values of 0.2986, 0.8457, and 0.3928 on the training dataset, and 0.1882, 0.9508, and 0.2283 on the testing dataset, respectively. Comparable performance was observed for the extreme learning machine (ELM) models, a variant of neural networks, with both AGA-ELM-3 (20) and GA-ELM-4 (50) yielding \(MAE\), \(R^{2}\), and \(RMSE\) values of 0.1940, 0.9544, and 0.2197, respectively, on the testing set. Furthermore, to assess the potential benefits of model ensembling, four stacking models were constructed. The most effective configuration--Stacking 3--combined the AGA-ANN-1 (20), AGA-ELM-3 (20), GA-ANN-2 (50), and AGA-SVR (20) models, achieving testing set metrics of 0.2177 for \(MAE\), 0.9246 for \(R^{2}\), and 0.2826 for \(RMSE\). Additionally, an analysis of the validation metrics revealed that all neural network-based models, the SVR model, and all stacking configurations exhibited comparable performance. Although the Stacking 1 model--comprising AGA-ANN-1 (20) and AGA-ELM-3 (20)--achieved the best average validation results, with mean \(MAE\), \(R^{2}\), and \(RMSE\) values of 0.3476 \(\pm\) 0.0952, 0.7454 \(\pm\) 0.1570, and 0.4483 \(\pm\) 0.1230, respectively, these metrics alone were not sufficient to conclusively establish the superiority of the stacking models over the individual neural network-based models. However, the application of nonparametric statistical tests confirmed that all stacking models were at least as robust as and in some cases, statistically superior to, the individual models examined. Future research could explore the predictive capabilities of additional ML models, such as AdaBoost, gradient boosting machines (GBM), and radial basis networks (RBN) [66; 67]. Further improvements in hyperparameter optimization could be achieved by integrating alternative metaheuristics, including cuckoo search optimization (CSO) and ant colony optimization (ACO) [85; 86]. Given the small size of the dataset used in this study, collecting a larger dataset would be crucial for improving model reliability and reducing variability across different data splits. In this regard, data augmentation techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs) [87], could be explored. However, their potential benefits should be carefully evaluated, as there is no definitive guarantee that synthetic data generation will enhance model performance.