<p>Medical image classification is pivotal for computer-aided diagnosis, enabling automated and accurate disease detection or severity grading. However, high inter-class similarity, imbalanced data, and limited annotated images often limit performance on important classes, ultimately affecting the overall performance. In this paper, a novel genetic programming (GP) algorithm is proposed, where the selection method in crossover and the mutation operator are modified to allocate increased search pressure to more challenging classes. Specifically, the crossover operation with a new selection method is designed to increase the likelihood that better-performing individuals in the challenging class exchange their good genetic materials, while the new mutation operator mutates worse-performing individuals under the guidance from the better-performing ones, thereby steering the search toward more promising regions. Experimental results on six diverse medical image datasets demonstrate the superiority of the proposed algorithm over nine baseline methods. Further analysis highlights the potential of the new genetic operators to enhance the overall classification performance by targeting challenging classes. Moreover, the analysis of an evolved GP individual illustrates the potential interpretability of GP.</p>