123 citations · 282 across the 44 of their papers we have counts for
44 papers
The Pitfalls and Potentials of Adding Gene-invariance to Optimal Mixing
Anton Bouter, Dirk Thierens, Peter A. N. Bosman
Optimal Mixing (OM) is a variation operator that integrates local search with genetic recombination. EAs with OM are capable of state-of-the-art optimization in discrete spaces, of…
Improving the efficiency of GP-GOMEA for higher-arity operators
Thalea Schlender, Mafalda Malafaia, Tanja Alderliesten +1
Deploying machine learning models into sensitive domains in our society requires these models to be explainable. Genetic Programming (GP) can offer a way to evolve inherently inter…
A Tournament of Transformation Models: B-Spline-based vs. Mesh-based Multi-Objective Deformable Image Registration
Georgios Andreadis, Joas I. Mulder, Anton Bouter +2
The transformation model is an essential component of any deformable image registration approach. It provides a representation of physical deformations between images, thereby defi…
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…
Evolutionary Neural Cascade Search across Supernetworks
Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman
To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery…
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression
Marco Virgolin, Peter A. N. Bosman
Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A…
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning
Marco Virgolin, Eric Medvet, Tanja Alderliesten +1
Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. So far, evolutionary computation (EC), in particular in th…
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models
E. M. C. Sijben, T. Alderliesten, P. A. N. Bosman
Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic. The goal of XAI is to gain trust in a machine learning (ML) model through clear insi…
Adaptive Objective Configuration in Bi-Objective Evolutionary Optimization for Cervical Cancer Brachytherapy Treatment Planning
Leah R. M. Dickhoff, Ellen M. Kerkhof, Heloisa H. Deuzeman +3
The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been proven effective and efficient in solving real-world problems. A prime exampl…
GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting
Anton Bouter, Peter A. N. Bosman
In a Gray-Box Optimization (GBO) setting that allows for partial evaluations, the fitness of an individual can be updated efficiently after a subset of its variables has been modif…
On genetic programming representations and fitness functions for interpretable dimensionality reduction
Thomas Uriot, Marco Virgolin, Tanja Alderliesten +1
Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. However, most of the main DR methods are either linear (e.g., PCA), do not pro…
Solving Multi-Structured Problems by Introducing Linkage Kernels into GOMEA
Arthur Guijt, Dirk Thierens, Tanja Alderliesten +1
Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue of linkage (or variable interaction) learning. This requires, however, that the linkage model can captu…
Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization
Renzo J. Scholman, Anton Bouter, Leah R. M. Dickhoff +2
Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is designed to find solutions well spread over all locally optimal approximation sets of a Multi-modal Multi-ob…
Data variation-aware medical image segmentation
Arkadiy Dushatskiy, Gerry Lowe, Peter A. N. Bosman +1
Deep learning algorithms have become the golden standard for segmentation of medical imaging data. In most works, the variability and heterogeneity of real clinical data is acknowl…
Mixed-Block Neural Architecture Search for Medical Image Segmentation
Martijn M. A. Bosma, Arkadiy Dushatskiy, Monika Grewal +2
Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cas…
Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images -- Proof of Concept
Georgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten
Reliably and physically accurately transferring information between images through deformable image registration with large anatomical differences is an open challenge in medical i…
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression
Dazhuang Liu, Marco Virgolin, Tanja Alderliesten +1
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sortin…