123 citations · 282 across the 44 of their papers we have counts for
8 papers · 1 filter
Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-Valley Clustering
S. C. Maree, T. Alderliesten, P. A. N. Bosman
In model-based evolutionary algorithms (EAs), the underlying search distribution is adapted to the problem at hand, for example based on dependencies between decision variables. Hi…
Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent
Timo M. Deist, Stefanus C. Maree, Tanja Alderliesten +1
Evolutionary algorithms (EAs) are the preferred method for solving black-box multi-objective optimization problems, but when gradients of the objective functions are available, it…
Ensuring smoothly navigable approximation sets by Bezier curve parameterizations in evolutionary bi-objective optimization -- applied to brachytherapy treatment planning for prostate cancer
S. C. Maree, T. Alderliesten, P. A. N. Bosman
The aim of bi-objective optimization is to obtain an approximation set of (near) Pareto optimal solutions. A decision maker then navigates this set to select a final desired soluti…
Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing
S. C. Maree, T. Alderliesten, P. A. N. Bosman
Domination-based multi-objective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods however stagnate when the majority of th…
Local Search is a Remarkably Strong Baseline for Neural Architecture Search
T. Den Ottelander, A. Dushatskiy, M. Virgolin +1
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search algorithms being propos…
Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy
M. Virgolin, Z. Wang, B. V. Balgobind +11
To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent…
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms
Arkadiy Dushatskiy, Adriënne M. Mendrik, Peter A. N. Bosman +1
There has recently been great progress in automatic segmentation of medical images with deep learning algorithms. In most works observer variation is acknowledged to be a problem a…
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
Monika Grewal, Timo M. Deist, Jan Wiersma +2
Anatomical landmark correspondences in medical images can provide additional guidance information for the alignment of two images, which, in turn, is crucial for many medical appli…