4 citations · 7 across the 7 of their papers we have counts for
8 papers
GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis
Jasper Post, Johannes Koch, Anton Bouter +2
GP-GOMEA is a state-of-the-art evolutionary algorithm for symbolic regression, known for discovering small and interpretable models. However, its computational cost remains substan…
End-user validation of BRIGHT with custom-developed graphical user interface applied to cervical cancer brachytherapy
Leah R. M. Dickhoff, Ellen M. Kerkhof, Heloisa H. Deuzeman +12
Multi-objective optimisation using BRIGHT has proven insightful and effective in prostate cancer brachytherapy treatment planning. BRachytherapy via artificially Intelligent GOMEA-…
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…
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…
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…
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…
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…
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter +2
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencie…