123 citations · 282 across the 44 of their papers we have counts for
8 papers · 1 filter
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search
Alexander Chebykin, Arkadiy Dushatskiy, Tanja Alderliesten +1
In this work, we show that simultaneously training and mixing neural networks is a promising way to conduct Neural Architecture Search (NAS). For hyperparameter optimization, reusi…
Multi-Objective Population Based Training
Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten +1
Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems…
A Joint Python/C++ Library for Efficient yet Accessible Black-Box and Gray-Box Optimization with GOMEA
Anton Bouter, Peter A. N. Bosman
Exploiting knowledge about the structure of a problem can greatly benefit the efficiency and scalability of an Evolutionary Algorithm (EA). Model-Based EAs (MBEAs) are capable of d…
The Impact of Asynchrony on Parallel Model-Based EAs
Arthur Guijt, Dirk Thierens, Tanja Alderliesten +1
In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the al…
Convolutions, Transformers, and their Ensembles for the Segmentation of Organs at Risk in Radiation Treatment of Cervical Cancer
Vangelis Kostoulas, Peter A. N. Bosman, Tanja Alderliesten
Segmentation of regions of interest in images of patients, is a crucial step in many medical procedures. Deep neural networks have proven to be particularly adept at this task. How…
MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images
Georgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten
Finding a realistic deformation that transforms one image into another, in case large deformations are required, is considered a key challenge in medical image analysis. Having a p…
Bi-objective optimization of organ properties for the simulation of intracavitary brachytherapy applicator placement in cervical cancer
Cedric J. Rodriguez, Stephanie M. de Boer, Peter A. N. Bosman +1
Validation of deformable image registration techniques is extremely important, but hard, especially when complex deformations or content mismatch are involved. These complex deform…
Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations
Monika Grewal, Dustin van Weersel, Henrike Westerveld +2
Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the aut…