GP-GOMEA is a state-of-the-art evolutionary algorithm for symbolic regression, known for discovering small and interpretable models. However, its computational cost remains substantial, limiting its applicability to larger datasets and more complex target expressions. In contrast, the rise of modern subsymbolic approaches, particularly deep learning, has been driven largely by the massive parallelism offered by GPUs. This thesis takes the first major step toward a fully GPU-accelerated GP-GOMEA by introducing a GPU-based fitness evaluation scheme. A GPU-friendly representation of GP-GOMEA's template-based individuals is designed alongside a corresponding evaluation strategy that exploits the inherent parallelism of population-based search. This substantially increases evaluation throughput, enabling orders of magnitude more evaluations within the same time budget.

Across four standard symbolic regression benchmarks, this increased evaluation capacity yields performance improvements, particularly for larger datasets and larger population sizes. Additionally, a dynamic-block strategy is introduced that further improves GPU utilization for small batch sizes, and GPU-accelerated evaluation is extended to Modular GP-GOMEA. Moreover, the ability to efficiently evaluate much larger datasets and more complex templates enables analyses that were previously infeasible, allowing a systematic investigation of what makes expressions increasingly difficult for GP-GOMEA and providing new insights into how expression structure affects search difficulty. Finally, for the first time, this expanded capability allows a problem-agnostic evolutionary algorithm to reliably regress one of the largest Feynman equations within four hours. ... Read more

Multimodal machine learning models can exploit complementary information from multiple data modalities. MultiFIX (Multimodal Feature engIneering eXplainable artificial intelligence) is a framework designed to construct partially interpretable multimodal models, providing explanations for both modality-specific features and each modality its contribution to the final prediction. However, it was shown to not scale effectively for tasks with extreme joint-modality dependence.

This thesis proposes an alternative training strategy that integrates knowledge of the features to be engineered, expressed as feature targets that guide the learning process. The strategy improves upon baseline performance, even when the feature targets are non-ideal. Since ground-truth feature targets are typically unavailable in real-world settings, the feature targets are optimised using the Gene-pool Optimal Mixing Evolutionary Algorithm. The optimised feature targets, though only loosely aligned with the ground-truth features, enables the alternative training method to surpass baseline MultiFIX performance on a three-gated XOR task.

The same approach was evaluated on simpler tasks, such as the single XOR and AND problems, where it achieved slightly lower but still comparable performance to the already strong baselines. Results indicate that this computationally intensive approach is most beneficial for problems characterised by high joint-modality dependence and complex feature interactions. Interestingly, closer alignment between the optimised and ground-truth feature targets did not consistently lead to higher MultiFIX performance. Consequently, future improvements are likely to stem from refining how feature targets are integrated into the training process, rather than from further optimisation of the targets themselves. ... Read more