77 citations · 148 across the 7 of their papers we have counts for
8 papers
Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization
Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers +2
Real-world problems are often multi-objective with decision-makers unable to specify a priori which trade-off between the conflicting objectives is preferable. Intuitively, buildin…
Identifying Properties of Real-World Optimisation Problems through a Questionnaire
Koen van der Blom, Timo M. Deist, Vanessa Volz +5
Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences. However, it is not clear how closely benchmarks match the properties of rea…
Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent
Timo M. Deist, Stefanus C. Maree, Tanja Alderliesten +1
Evolutionary algorithms (EAs) are the preferred method for solving black-box multi-objective optimization problems, but when gradients of the objective functions are available, it…
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems
Koen van der Blom, Timo M. Deist, Tea Tušar +5
Benchmarks are a useful tool for empirical performance comparisons. However, one of the main shortcomings of existing benchmarks is that it remains largely unclear how they relate…
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
Monika Grewal, Timo M. Deist, Jan Wiersma +2
Anatomical landmark correspondences in medical images can provide additional guidance information for the alignment of two images, which, in turn, is crucial for many medical appli…
A Drug Recommendation System (Dr.S) for cancer cell lines
Marleen Balvert, Georgios Patoulidis, Andrew Patti +5
Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring ge…
Simulation assisted machine learning
Timo M. Deist, Andrew Patti, Zhaoqi Wang +3
Motivation: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient sys…
High-dose-rate prostate brachytherapy inverse planning on dose-volume criteria by simulated annealing
Timo M. Deist, Bram L. Gorissen
High-dose-rate brachytherapy is a tumor treatment method where a highly radioactive source is brought in close proximity to the tumor. In this paper we develop a simulated annealin…