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.

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.