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Anton Bouter - IEEE Xplore Author Profile
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Anton Bouter

Publications
3
Citations
4
Publications by Year
20212024
Co-Authors:
Show All Co-Authors (6)
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Anton Bouter

Affiliation

Evolutionary Intelligence Group
Centrum Wiskunde and Informatica
Amsterdam, 1098XG, the Netherlands

Publication Topics

Biography

Anton Bouter received the B.Sc. and M.Sc. degrees in computer science from the Delft University of Technology, Delft, The Netherlands, in 2014 and 2016, respectively. He is currently pursuing the Ph.D. degree with the Life Sciences and Health Group, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands.,His research includes the design and application of model-based evolutionary algorithms, generally within the scope of numerical optimization, and their applications to the field of radiation oncology. More specifically, current research is focused on the application of multiobjective model-based evolutionary algorithms to the optimization of treatment plans for high-dose-rate prostate brachytherapy. This includes the development of sta...
Publications
3
Citations
4
Publications by Year
20212024
Co-Authors:
Show All Co-Authors (6)
Author's Published Works

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Parameterless Gene-Pool Optimal Mixing Evolutionary Algorithms

Arkadiy Dushatskiy;Marco Virgolin;Anton Bouter;Dirk Thierens;Peter A. N. Bosman

Evolutionary Computation
Year: 2024 | Volume: 32, Issue: 4 | Journal Article |
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, that is, dependencies between variables, can be key. In this paper, we present the latest version of, and propose substantial enhancements to, the gene-pool optimal mixing evolutionary algorithm (GOMEA): an EA explicitly designed to esti...Show More

Parameterless Gene-Pool Optimal Mixing Evolutionary Algorithms

Arkadiy Dushatskiy;Marco Virgolin;Anton Bouter;Dirk Thierens;Peter A. N. Bosman

Year: 2024 | Volume: 32, Issue: 4 | Journal Article |

GPU-Accelerated Parallel Gene-pool Optimal Mixing Applied to Multi-Objective Deformable Image Registration

Anton Bouter;Tanja Alderliesten;Peter A.N. Bosman

2021 IEEE Congress on Evolutionary Computation (CEC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
The Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has previously been successfully used to achieve highly scalable optimization of various real-world problems in a gray-box optimization setting. Deformable Image Registration (DIR) is a multi-objective problem, aimed at finding the most likely non-rigid deformation of a given source image so that it matches a given target i...Show More

GPU-Accelerated Parallel Gene-pool Optimal Mixing Applied to Multi-Objective Deformable Image Registration

Anton Bouter;Tanja Alderliesten;Peter A.N. Bosman

Year: 2021 | Conference Paper |

Fitness-Based Linkage Learning in the Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm

Chantal Olieman;Anton Bouter;Peter A. N. Bosman

IEEE Transactions on Evolutionary Computation
Year: 2021 | Volume: 25, Issue: 2 | Journal Article |
Cited by: Papers (3)
The recently introduced real-valued gene-pool optimal mixing evolutionary algorithm (RV-GOMEA) has been shown to be among the state of the art for solving gray-box optimization problems where partial evaluations can be leveraged. A core strength is its ability to effectively exploit the linkage structure of a problem, which often is unknown a priori and has to be learned online. Previously publish...Show More

Fitness-Based Linkage Learning in the Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm

Chantal Olieman;Anton Bouter;Peter A. N. Bosman

Year: 2021 | Volume: 25, Issue: 2 | Journal Article |

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