Constraint Handling in RV-GOMEA

The Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) is a state-of-the-art algorithm for single-objective, real-valued optimization. As many practical applications are inherently constrained, evolutionary algorithms are equipped with constraint handling techniques to allow optimizing constrained problems. The approach...

GPU-Accelerated GOMEA: Solving the max-cut problem by large-scale parallelisation of GOMEA using GPGPU

With the advances in General-Purpose computing on Graphics Processing Units (GPGPU), it is worthwhile to explore whether other areas in the field of Artificial Intelligence (AI) can reap the benefits. One such area is Evolutionary Algorithms (EAs), which—among other processes—involves the repetitive exchange of genes among individuals. This...

Applying hybrid evolutionary algorithms to deformable image registration of 3D medical images using common B-spline-based transformation models

Deformable Image Registration (DIR) is a medical imaging process involving the spatial alignment of two or more images using a transformation model that can account for non-rigid deformations. B-spline-based transformation models have emerged as a common approach to express such spatial alignments. However, without additional measures, their...

A surrogate-assisted evolutionary algorithm based on inverse distance weighting: Applied to a multi-objective deformable image registration problem

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...

Neuro-GOMEA: Using Modern Evolutionary Algorithms to Train Neural Networks

Neural networks (NNs) have, in recent years, become a major part of modern pattern recognition, and both theoretical and applied research evolve at an astounding pace. NNs are usually trained via gradient descent (GD), but research has shown that GD is not always capable of training very small networks. As a result, networks trained via GD are...

Warm-starting evolutionary plan optimization for high-dose-rate brachytherapy treatment to reduce optimization time

Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization with an Application to Prostate HDR Brachytherapy Automated Treatment Planning

Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find all locally optimal approximation sets of a Multi-modal Multi-objective Optimization Problem (MMOP), there is a risk that the found approximation sets are not smoothly navigable because the solutions belong to various niches, which reduces the insight for...

Hybridizing Hypervolume based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation

Neural Architecture Search for Medical Image Segmentation

Deep Neural Networks (DNNs) have the potential to make various clinical procedures more time-efficient by automating medical image segmentation; largely due to their strong, in some cases human-level, performance. The design of the best possible medical image segmentation DNN, however, is task-specific. Neural Architecture Search (NAS), i.e.,...

Improving the homogeneity of brachytherapy treatment plans generated by BRIGHT using a hotspot registration method based on connected component analysis

In this thesis BRIGHT, a multi-objective evolutionary algorithm for the creation of treatment plans for high-dose rate brachytherapy for prostate cancer, is extended with a new objective to mitigate the formation of high dose contiguous volumes, i.e. hotspots. Multiple new objectives are tested on their performance to reduce hotspots, while...

Do More Elaborate Search Strategies Lead to Better Neural Architecture Search Performance?

Computer vision tasks, like supervised image classification, are effectively tackled by convolutional neural networks, provided that the architecture, which defines the structure of the network, is set correctly. Neural Architecture Search (NAS) is a relatively young and increasingly popular field that is concerned with automatically optimizing...

Designing the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm and Applying it to Substantially Improve the Efficiency of Multi-Objective Deformable Image Registration

The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) for discrete variables has been shown to be able to efficiently and effectively exploit the decomposability of optimization problems, especially in a grey-box setting, in which a solution can be efficiently updated after a modification of a subset of its variables....

Parallelizing the Linkage Tree Genetic Algorithm and Searching for the Optimal Replacement for the Linkage Tree

The recently introduced Linkage Tree Genetic Algorithm (LTGA) has shown to exhibit excellent scalability on a variety of optimization problems. LTGA employs Linkage Trees (LTs) to identify and exploit linkage information between problem variables. In this work we present two parallel implementations of LTGA that enable us to leverage the...