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Frederik Rehbach
Frederik Rehbach
Cologne University of Applied Sciences
Verified email at th-koeln.de
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Year
Expected improvement versus predicted value in surrogate-based optimization
F Rehbach, M Zaefferer, B Naujoks, T Bartz-Beielstein
Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 868-876, 2020
432020
Comparison of parallel surrogate-assisted optimization approaches
F Rehbach, M Zaefferer, J Stork, T Bartz-Beielstein
Proceedings of the genetic and evolutionary computation conference, 1348-1355, 2018
302018
A novel dynamic multi-criteria ensemble selection mechanism applied to drinking water quality anomaly detection
VHA Ribeiro, S Moritz, F Rehbach, G Reynoso-Meza
Science of The Total Environment 749, 142368, 2020
162020
Hospital capacity planning using discrete event simulation under special consideration of the COVID-19 pandemic
T Bartz-Beielstein, F Rehbach, O Mersmann, E Bartz
arXiv preprint arXiv:2012.07188, 2020
152020
In a Nutshell--The Sequential Parameter Optimization Toolbox
T Bartz-Beielstein, M Zaefferer, F Rehbach
arXiv preprint arXiv:1712.04076, 2017
112017
Continuous optimization benchmarks by simulation
M Zaefferer, F Rehbach
Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference …, 2020
92020
Gecco industrial challenge 2018 dataset: A water quality dataset for the’internet of things: Online anomaly detection for drinking water quality’competition at the genetic and …
S Moritz, F Rehbach, S Chandrasekaran, M Rebolledo, T Bartz-Beielstein
Kyoto, Japan, 2018
92018
Optimization and adaptation of a resource planning tool for hospitals under special consideration of the COVID-19 pandemic
T Bartz-Beielstein, M Dröscher, A Gür, A Hinterleitner, T Lawton, ...
2021 IEEE Congress on Evolutionary Computation (CEC), 728-735, 2021
82021
Variable reduction for surrogate-based optimization
F Rehbach, L Gentile, T Bartz-Beielstein
Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020
72020
GECCO 2018 Industrial Challenge: Monitoring of drinking-water quality
F Rehbach, S Moritz, S Chandrasekaran, M Rebolledo, M Friese, ...
Accessed: Feb 19, 2019, 2018
72018
Feature selection for surrogate model-based optimization
F Rehbach, L Gentile, T Bartz-Beielstein
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019
62019
Benchmark-driven configuration of a parallel model-based optimization algorithm
F Rehbach, M Zaefferer, A Fischbach, G Rudolph, T Bartz-Beielstein
IEEE Transactions on Evolutionary Computation 26 (6), 1365-1379, 2022
52022
Parallelized bayesian optimization for expensive robot controller evolution
M Rebolledo, F Rehbach, AE Eiben, T Bartz-Beielstein
Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference …, 2020
52020
Optimization of High-dimensional Simulation Models Using Synthetic Data
T Bartz-Beielstein, E Bartz, F Rehbach, O Mersmann
arXiv preprint arXiv:2009.02781, 2020
42020
Surrogate-assisted learning of neural networks
J Stork, M Zaefferer, A Fischbach, F Rehbach, T Bartz-Beielstein
42017
Case Study III: Tuning of Deep Neural Networks
T Bartz-Beielstein, S Chandrasekaran, F Rehbach
Hyperparameter Tuning for Machine and Deep Learning with R: A Practical …, 2023
32023
Resource planning for hospitals under special consideration of the covid-19 pandemic: Optimization and sensitivity analysis
T Bartz-Beielstein, M Dröscher, A Gür, A Hinterleitner, O Mersmann, ...
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
32021
Parallelized Bayesian optimization for problems with expensive evaluation functions
M Rebolledo, F Rehbach, AE Eiben, T Bartz-Beielstein
Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020
32020
Case Study I: Tuning Random Forest (Ranger)
T Bartz-Beielstein, S Chandrasekaran, F Rehbach, M Zaefferer
Hyperparameter Tuning for Machine and Deep Learning with R: A Practical …, 2023
12023
Case study II: tuning of gradient boosting (xgboost)
T Bartz-Beielstein, S Chandrasekaran, F Rehbach
Hyperparameter Tuning for Machine and Deep Learning with R: A Practical …, 2023
12023
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