A Neuroevolutionary Symbolic Approach to the Interpretability–Performance Dilemma

Neural networks achieve unmatched performance in image classification, but their reasoning remains opaque. Genetic Programming (GP) offers a more transparent alternative by evolving explicit programs, yet it struggles to scale to high-dimensional multiclass image tasks. We address this gap by distilling the latent space of a trained neural network into independent GP symbolic programs, then recombining their outputs through a lightweight classifier head. By training GP students on image-to-latent subobjectives, the proposed decomposition simplifies symbolic search and scales GP-based image classification. On four of the five studied multiclass medical imaging datasets, fully symbolic models are competitive with deep learning baselines. This neuroevolutionary symbolic model exposes an interpretability–performance tradeoff. More programs are typically required to approach teacher performance, but each additional program increases the executable logic to inspect. Because distilled student programs are executable and symbolic, they can be directly inspected, scored, and explained. We use LLM-based judges to score the interpretability of GP functions and function compositions for three stakeholder audiences, namely computer scientists, doctors, and patients. 50 participants of a human study, including clinicians, computer-vision practitioners, and non-experts, chose the LLM-preferred symbolic unit consistently across participant groups, judge families, and composed operation chains. Audience-specific scores induce distinct interpretability–performance frontiers, which we illustrate on BloodMNIST. Symbolic program diversity can therefore support stakeholder-aware model selection and attenuate the tradeoff by prioritizing programs tailored to a given audience. More broadly, this suggests a path toward expressing opaque neural representations as symbolic components that can be selected, inspected, and explained according to the needs of different stakeholders.