Multi-Objective Genetic Programming with Ensemble Construction for Medical Image Classification

Medical image classification is challenging due to limited labeled instances, high inter-class similarity, and imbalanced class distributions. Although genetic programming (GP) has demonstrated strong potential in general image classification, its application to medical image classification remains underexplored. To address this research gap, this paper proposes a Multi-Objective Genetic Programming with Ensemble Construction (MOGPEC) algorithm for medical image classification. First, a new GP representation is designed to align with typical medical image processing workflows, enabling the evolved solutions to be more consistent with clinical reasoning. A multi-objective optimization framework is then introduced to encourage the generation of diverse solutions across different classes, creating a rich pool of candidate models for subsequent ensemble construction. Finally, an ensemble construction strategy is developed to select and combine complementary GP solutions, thereby enhancing the overall classification performance. Extensive evaluations on multiple medical image datasets demonstrate that MOGPEC outperforms traditional methods, representative GP methods, and deep-learning-based methods in most cases. Ablation experiments in the Supplementary Material further analyze the contributions of the proposed GP representation, multi-objective optimization, and ensemble construction strategy. Visualization of the evolved solutions provides valuable interpretability.