no code implementations • 9 Apr 2024 • C. J. Rodriguez, S. L. Thomson, T. Alderliesten, P. A. N. Bosman
The results of the fitness landscape analysis reveals significant differences between true and surrogate features at different time points during optimisation.
no code implementations • 19 Mar 2024 • E. M. C. Sijben, J. C. Jansen, M. de Ridder, P. A. N. Bosman, T. Alderliesten
We assess the performance of the model based on visual inspection by a senior otorhinolaryngologist and several quantitative metrics by comparing model outputs with manual delineations, including a comparison with variation in manual delineation by multiple observers.
no code implementations • 19 Feb 2024 • E. M. C. Sijben, J. C. Jansen, P. A. N. Bosman, T. Alderliesten
The aim of this work is to learn the general underlying growth pattern of paragangliomas from multiple tumor growth data sets, in which each data set contains a tumor's volume over time.
no code implementations • 24 Mar 2022 • E. M. C. Sijben, T. Alderliesten, P. A. N. Bosman
Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 28 Oct 2020 • S. C. Maree, T. Alderliesten, P. A. N. Bosman
This can be used to adapt the search distribution of an EA to the number of modes, exploring each mode separately.
no code implementations • 11 Jun 2020 • S. C. Maree, T. Alderliesten, P. A. N. Bosman
We analyze the behavior of BezEA and compare it to optimization of the UHV with GOMEA as well as the domination-based multi-objective GOMEA.
2 code implementations • 20 Apr 2020 • T. Den Ottelander, A. Dushatskiy, M. Virgolin, P. A. N. Bosman
The proposed LS algorithm is compared with RS and two evolutionary algorithms (EAs), as these are often heralded as being ideal for multi-objective optimization.
2 code implementations • 10 Apr 2020 • S. C. Maree, T. Alderliesten, P. A. N. Bosman
The resulting algorithm, UHV-GOMEA, is compared to Sofomore equipped with GOMEA, and the domination-based MO-GOMEA.
no code implementations • 17 Feb 2020 • M. Virgolin, Z. Wang, B. V. Balgobind, I. W. E. M. van Dijk, J. Wiersma, P. S. Kroon, G. O. Janssens, M. van Herk, D. C. Hodgson, L. Zadravec Zaletel, C. R. N. Rasch, A. Bel, P. A. N. Bosman, T. Alderliesten
Each artificial plan was automatically emulated on the 142 CTs, resulting in 42, 600 3D dose distributions from which dose-volume metrics were derived.
1 code implementation • 25 Jul 2019 • S. C. Maree, T. Alderliesten, P. A. N. Bosman
This report presents benchmarking results of the Hill-Valley Evolutionary Algorithm version 2019 (HillVallEA19) on the CEC2013 niching benchmark suite under the restrictions of the GECCO 2019 niching competition on multimodal optimization.
no code implementations • 16 Oct 2018 • S. C. Maree, T. Alderliesten, D. Thierens, P. A. N. Bosman
The performance of EAs often deteriorates as multiple modes in the fitness landscape are modelled with a unimodal search model.
1 code implementation • 30 Jun 2018 • S. C. Maree, T. Alderliesten, D. Thierens, P. A. N. Bosman
This report presents benchmarking results of the latest version of the Hill-Valley Evolutionary Algorithm (HillVallEA) on the CEC2013 niching benchmark suite.