Evolutionary Optimized Diverse Ensemble Learning (EODE) for Accurate Cancer Screening from Gene Expression Data

The proposed Evolutionary Optimized Diverse Ensemble Learning (EODE) framework synergistically combines grey wolf optimization-based feature selection, diversity injection via randomized model training, and evolutionary optimization of ensemble classifiers to achieve significantly improved cancer screening accuracy and robustness compared to individual and conventionally aggregated models.