## 1 Introduction
Under the complete dataset scenario, the SSE-CART model achieved the best accuracy, while the PCA-CART model achieved the best F1-score value, indicating a potential trade-off between dimensionality reduction and predictive robustness. [PERSON] et al. (2010) introduced a hybrid ANN model incorporating the ant lion optimizer (ALO-ANN) to predict overbreak and underbreak (dilution) in tunnel excavations, utilizing a dataset with 12 independent variables from a tunnel in China. In terms of model architecture, they identified that employing a single-hidden layer with eight nodes significantly enhanced the predictive performance of ALO-ANN. The effectiveness of this model was benchmarked against traditional ANN, generalized regression neural network (GRNN), and extreme learning machine (ELM) models, corroborating the superiority of the ALO-ANN model. [PERSON] et al. [64] advanced the ANN framework by integrating deep continual learning techniques, specifically elastic weight consolidation and memory replay (EWC-MR-ANN), alongside an attention mechanism to enhance overbreak prediction. Their study utilized a considerably larger dataset, consisting of 2268 samples with seven independent variables collected from tunnels in China. The dataset was partitioned into ten subsets across five tasks to emulate a multitask and continuous learning scenario. Compared to a conventional ANN, the EWC-MR-ANN demonstrated superior retention of learned parameters across different tasks, thereby mitigating the issue of catastrophic forgetting. While the standard ANN exhibited considerable fluctuations in hyperparameters between tasks, leading to performance degradation, the EWC-MR-ANN maintained stability and exhibited better generalization. The literature review demonstrates that various AI-based models, including hybrid machine learning models enhanced with metaheuristics, have been successfully applied over the past decades to predict dilution in underground mines. Hyperparameter tuning is a critical step in ML applications, as traditional methods like grid search offer a limited capacity for exploring the hyperparameter space. By formulating this process as an optimization problem and leveraging population-based metaheuristics, the likelihood of identifying an optimal hyperparameter configuration that minimizes training error is significantly increased. Nevertheless, future research in this domain could benefit from the exploration of alternative methodologies. While [PERSON] et al. [64] proposed a framework to simulate a continual learning environment--an important consideration given the inherently static nature of most supervised machine learning implementations--the task of predicting dilution in underground mining operations has yet to be formally addressed from a time series modeling perspective [65]. Additional machine learning paradigms, such as radial basis function networks (RBNs) and adaptive boosting (AdaBoost) algorithms, remain underexplored in this context and may offer enhanced predictive performance [66; 67]. Ensemble techniques, particularly stacking, also hold promise for improving accuracy through the aggregation of multiple base learners into a more robust metamodel [68]. Moreover, a critical limitation of many existing studies lies in their insufficient statistical grounding, often lacking rigorous explanation or validation of the results obtained. Given the stochastic behavior and sensitivity of most machine learning models to variations in the data, the implementation of nonparametric statistical tests should be encouraged to assess whether observed differences in performance are statistically significant [69]. This study introduces a practical framework for predicting dilution (_ELOS_) in underground mining using ML models in combination with genetic algorithms (GAs). The proposed pipeline effectively addresses variable distribution skewness and is designed for implementation even on domestic computing systems.