1 code implementation • 8 Feb 2021 • Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, Peter A. N. Bosman
We discuss and illustrate why training processes to approximate Pareto fronts need to optimize on fronts of individual training samples instead of on only the front of average losses.
no code implementations • 11 Nov 2020 • Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tušar
Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences.
3 code implementations • 9 Jul 2020 • Timo M. Deist, Stefanus C. Maree, Tanja Alderliesten, Peter A. N. Bosman
On several bi-objective benchmarks, we find that gradient-based algorithms outperform the tested EAs by obtaining a better hypervolume with fewer evaluations whenever exact gradients of the multiple objective functions are available and in case of small evaluation budgets.
Optimization and Control
no code implementations • 14 Apr 2020 • Koen van der Blom, Timo M. Deist, Tea Tušar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks
This work aims to identify properties of real-world problems through a questionnaire on real-world single-, multi-, and many-objective optimization problems.
2 code implementations • 21 Jan 2020 • Monika Grewal, Timo M. Deist, Jan Wiersma, Peter A. N. Bosman, Tanja Alderliesten
We tested the approach on 22, 206 pairs of 2D slices with varying levels of intensity, affine, and elastic transformations.
no code implementations • 24 Dec 2019 • Marleen Balvert, Georgios Patoulidis, Andrew Patti, Timo M. Deist, Christine Eyler, Bas E. Dutilh, Alexander Schönhuth, David Craft
Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information.
1 code implementation • 15 Feb 2018 • Timo M. Deist, Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, David Craft
When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs.