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.

Deformable Image Registration (DIR) is a process in which the point-to-point correspondence between two or more medical images is estimated. This could allow spatial data to be transferred between these images, easing the work of practitioners in the field of radiation oncology. Many DIR approaches already exist. These are not yet applicable in all clinical settings, however, as DIR is a complex problem, partly because of the difficulty of evaluating image registration results. This is why often multiple objectives are considered in this evaluation. In this thesis, we combine two separate multi-objective DIR approaches into hybrids while evaluating their performances, with the aim to improve upon the individual approaches, thus reducing the gap toward clinical practice. In these hybrids, we also use an approach called Automated Landmark Detection which automatically detects corresponding landmarks between the source and target images. The first DIR approach, the Digital Phantom, creates a biomechanical model of the patient and uses an Evolutionary Algorithm (EA) to optimize parameters of a Finite Element Method (FEM)-based simulation. The second DIR approach, MOREA, creates a dual-dynamic biomechanical transformation model of the patient, using an EA to optimize its parameters. In the first hybrid approach, called the Landmark Guided Digital Phantom (LGDP), the Automated Landmark Detection approach is used to automatically find corresponding landmarks, which are then used as a guidance objective in the Digital Phantom. The second hybrid is called DP-MOREA, where the output of the Digital Phantom is used to initialize deformations in the MOREA approach. The final hybrid is called LGDP-MOREA. This hybrid combines all three components, using the generated landmarks to guide the Digital Phantom and initializing MOREA with the result. We analyze the effect of these hybrids on the quality of the final registration, both quantitatively and qualitatively. Additionally, we compare these hybrids with the components they are composed of. We test the performance of the approaches and hybrids on pelvic CT scans of three cervical cancer patients with large deformations. We observe that the individual Digital Phantom component outperforms the LGDP hybrid both quantitatively and qualitatively on these three patients. In addition, we observe that initializing MOREA with the output of the Digital Phantom and LGDP improves on how fast the optimization converges, that it improved the end results quantitatively and that it also has an effect on the resulting Deformation Vector Fields (DVFs). We conclude that in future research, these effects on the DVFs and their clinical relevance should be studied in more detail.

Deformable Image Registration (DIR) is a process in which the point-to-point correspondence between two or more medical images is estimated. This could allow spatial data to be transferred between these images, easing the work of practitioners in the field of radiation oncology. Many DIR approaches already exist. These are not yet applicable in all clinical settings, however, as DIR is a complex problem, partly because of the difficulty of evaluating image registration results. This is why often multiple objectives are considered in this evaluation. In this thesis, we combine two separate multi-objective DIR approaches into hybrids while evaluating their performances, with the aim to improve upon the individual approaches, thus reducing the gap toward clinical practice. In these hybrids, we also use an approach called Automated Landmark Detection which automatically detects corresponding landmarks between the source and target images. The first DIR approach, the Digital Phantom, creates a biomechanical model of the patient and uses an Evolutionary Algorithm (EA) to optimize parameters of a Finite Element Method (FEM)-based simulation. The second DIR approach, MOREA, creates a dual-dynamic biomechanical transformation model of the patient, using an EA to optimize its parameters. In the first hybrid approach, called the Landmark Guided Digital Phantom (LGDP), the Automated Landmark Detection approach is used to automatically find corresponding landmarks, which are then used as a guidance objective in the Digital Phantom. The second hybrid is called DP-MOREA, where the output of the Digital Phantom is used to initialize deformations in the MOREA approach. The final hybrid is called LGDP-MOREA. This hybrid combines all three components, using the generated landmarks to guide the Digital Phantom and initializing MOREA with the result. We analyze the effect of these hybrids on the quality of the final registration, both quantitatively and qualitatively. Additionally, we compare these hybrids with the components they are composed of. We test the performance of the approaches and hybrids on pelvic CT scans of three cervical cancer patients with large deformations. We observe that the individual Digital Phantom component outperforms the LGDP hybrid both quantitatively and qualitatively on these three patients. In addition, we observe that initializing MOREA with the output of the Digital Phantom and LGDP improves on how fast the optimization converges, that it improved the end results quantitatively and that it also has an effect on the resulting Deformation Vector Fields (DVFs). We conclude that in future research, these effects on the DVFs and their clinical relevance should be studied in more detail.