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

High-Dose-Rate brachytherapy is a critical component in the treatment of locally advanced cervical cancer. While automated treatment planning systems, such as BRIGHT, have demonstrated the ability to generate high-quality plans, their clinical adoption is hindered by the complexity of their configuration. Deploying such a system in a new hospital requires the manual definition of a clinical protocol that accurately reflects the local institution’s specific standard of care. This "cold start" problem is time-consuming for both doctors and researchers.

This thesis proposes a novel framework for the Automated Discovery of Clinical Protocols. By formulating the protocol configuration as a bi-level optimization problem, we employ the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm to autonomously extract implicit expert knowledge from a repository of historical clinical plans. The system evolves a set of protocol parameters that, when fed into BRIGHT, reproduce radiation dose distributions as preferred by human experts.

We validate this approach using anonymized patient data from Virginia Commonwealth University. Through a series of experiments with incrementally increasing complexity, ranging from optimizing simple dose thresholds to evolving the definitions of dosimetric metrics, we demonstrate that the proposed framework can successfully identify protocols that generate treatment plans that are quantitatively similar to the clinical ground truth. This research serves as a proof-of-concept, offering a pathway to rapidly deploy automated planning systems while ensuring alignment with local clinical expertise. ... Read more

Artificial Intelligence (AI) is an idea, a set of research subfields, and, ultimately, a suite of technologies that is reshaping the world. AI systems are intended to solve problems that would otherwise require biological or human intelligence to address. Since the middle of the 2010s, breakthroughs in the AI subfield of deep learning enabled rapid progress in computer vision, natural language processing, generative modelling, and other areas.

The key idea of deep learning is to use sufficiently large quantities of data to set parameters of a neural network such that it will perform well on a target task. A neural network is a directed graph of parameterized operations that transform a numeric input into a numeric output. The parameters of a network can be optimized via gradient-based techniques, in contrast to the hyperparameters that are often manually set by an expert. Typical hyperparameter categories are the settings of the gradient-based optimizer, the choice of operations used in the network, and the structure of its computational graph. The latter two are commonly referred to as the architecture of a network, and optimizing them is called Neural Architecture Search (NAS).

Hyperparameters can strongly influence both the performance of a network on the target task, and its efficiency. Therefore, it is important to find good hyperparameter values. The goal of hyperparameter optimization algorithms is to automate this process, which is challenging in the deep learning context for several reasons. Firstly, to evaluate how good a set of hyperparameter values is, a network typically needs to be trained, which takes time and expensive hardware, thus restricting how many sets of hyperparameter values can be evaluated on a finite budget. Secondly, neural networks require many hyperparameters to be set, with each having many potential values that non-trivially interact with those of other hyperparameters, leading to large search spaces that may be difficult to optimize in. Finally, hyperparameter optimization is often multi-objective, that is, involving several conflicting objectives, such as maximizing performance of a network while minimizing its inference time.

Multi-objective problems are commonly addressed via Evolutionary Algorithms (EAs). In an EA, several solutions, called a population, are optimized simultaneously, making them natural candidates to search for sets of solutions that represent different trade-offs in multi-objective problems. Other advantages of EAs are their ability to tackle large search spaces and the ease with which they can be parallelized, which is important for practical usage on modern hardware. Additionally, in order to reduce the inefficiency of EAs in terms of the number of evaluations of the objective functions required to reach convergence, these algorithms can be hybridized with approaches such as Bayesian optimization that can achieve excellent results within a budget of only a few evaluations.

The main goal of this thesis is to explore how EAs can be leveraged to perform hyperparameter optimization for deep learning effectively, so that the resulting networks achieve excellent performance, and efficiently, so that minimal computational effort would be required. ... Read more

Crafting and refining high-dose-rate brachytherapy treatment plans for cervical cancer is a time-consuming process. In recent years, BRIGHT was developed, an AI-based automated treatment planning method that provides not just one, but a set of optimized, patient-specific treatment plans, each with a different trade-off between objectives of interest. BRIGHT's plans are optimized using protocols that define guidelines on the delivered doses. In this thesis, we explore an alternative approach using dose-response models. These models provide insights into the estimated outcomes and risks of a treatment plan. Additionally, they offer great adaptability by including external patient characteristics. Currently, the use of these models remains limited to a feedback role. However, we instead include these models in BRIGHT to optimize them directly. Using one Tumor Control Probability (TCP) model and five Normal-Tissue Complication Probability (NTCP) models, we designed and tested several dose-response objective formulations and optimization techniques. We found that with the current models, these new objectives are insufficient as a replacement for BRIGHT's protocol-based coverage and sparing objectives. The produced plans have greatly improved dose-response outcomes but fall short in protocol compliance. Extending the existing objectives rather than replacing them proved more favorable. Average model improvements around 0.004 for NTCP are observed among the best coverage-sparing plans satisfying the protocol. Additionally, by sacrificing some sparing in the protocol-satisfaction range, improvements around 0.005 are possible for TCP and NTCP. Moreover, these dose-response-focused plans show distinct differences in their dose distribution favoring the dose-response targets compared to regular BRIGHT. Ultimately, the improvements we obtained are only marginal, and the clinical implications of this are unclear. The covariates of the models used in this thesis mostly overlapped with BRIGHT's objectives and did not fully extract their potential. Nevertheless, this thesis proves that the concept is viable and builds a foundation for this technique for when more and better dose-response models become available. ... 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

Cervical cancer affects about half a million women globally every year. The treatment of cervical cancer with the aim of healing mainly consists of surgery, radiation treatment, or a combination of radiation treatment with chemotherapy or hyperthermia. Radiation treatment is a type of treatment wherein a high dose of ionizing radiation is used to kill the tumor cells. The radiation dose is usually delivered in the form of External Beam Radiation Treatment (EBRT) with a linear accelerator followed by internal radiation treatment (brachytherapy) during which a small radioactive source is passed through an applicator and needles that are placed temporarily nearby the cervix. EBRT typically spans several weeks with daily sessions (often referred to as fractions), whereas brachytherapy typically consists of three or four fractions based on one to three implantations. The aim of the radiation treatment is to provide effective radiation to kill the tumor cells while sparing the nearby healthy tissue or Organs At Risk (OARs) as much as possible. This is achieved by treatment planning following the contouring of target volumes and OARs, on medical imaging scans, which typically are Computed Tomography (CT) and/orMagnetic Resonance Imaging (MRI).... ... Read more

Solutions to many real-life optimization problems take a long time to evaluate. This limits the number of solutions we can evaluate. When optimizing with an Evolutionary Algorithm (EA) a frequently used approach is to approximate the objective using a surrogate function, replacing the time-consuming real evaluation. This surrogate model is combined with a so-called acquisition function, to select promising candidate solutions. The acquisition function balances the trade-off between exploration of parameter space and the exploitation of the surrogate. These candidates are subject to an expensive evaluation with the true objective function and are used to update the surrogate model. Iteratively applying this process can effectively optimize global optimization problems. In this work, we propose a new multi-objective optimization algorithm with inverse distance weighting as surrogate function, which we call IDW-SAEA (inverse distance weighting surrogate assisted evolutionary algorithm). We introduce a new objective to the optimization problem to improve exploration and reduce the complexity of the acquisition function. We show this algorithm is competitive with state-of-the-art kriging-based surrogate-assisted EAs on certain benchmark problems. Additionally, we use the algorithm to optimize a practical problem: a Finite Element Method simulation of the cervix region with applications in radiotherapy for cervical cancer. ... Read more