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2020 – today
- 2024
[j6]Gerhard Hellstern
, Vanessa Dehn, Martin Zaefferer:
Quantum computer based feature selection in machine learning. IET Quantum Commun. 5(3): 232-252 (2024)- 2023
- [j5]Yingqian Zhang, Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Robbert Reijnen, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel López-Ibáñez, Ekhine Irurozki:
The first AI4TSP competition: Learning to solve stochastic routing problems. Artif. Intell. 319: 103918 (2023) - [p8]Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann:
Tuning: Methodology. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 7-26 - [p7]Thomas Bartz-Beielstein, Martin Zaefferer:
Models. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 27-69 - [p6]Thomas Bartz-Beielstein, Martin Zaefferer:
Hyperparameter Tuning Approaches. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 71-119 - [p5]Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach, Martin Zaefferer:
Case Study I: Tuning Random Forest (Ranger). Hyperparameter Tuning for Machine and Deep Learning with R 2023: 187-220 - [p4]Martin Zaefferer, Sowmya Chandrasekaran:
Case Study IV: Tuned Reinforcement Learning (in Python). Hyperparameter Tuning for Machine and Deep Learning with R 2023: 271-281 - [p3]Martin Zaefferer, Olaf Mersmann, Thomas Bartz-Beielstein:
Global Study: Influence of Tuning. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 283-301 - [e1]Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann:
Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide. Springer 2023, ISBN 978-981-19-5169-5 [contents] - 2022
- [j4]Frederik Rehbach, Martin Zaefferer, Andreas Fischbach, Günter Rudolph, Thomas Bartz-Beielstein:
Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm. IEEE Trans. Evol. Comput. 26(6): 1365-1379 (2022) - [i17]Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Yingqian Zhang, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, André Hottung, Tapan Shah, Meinolf Sellmann, Kevin Tierney, Carl Perreault-Lafleur, Caroline Leboeuf, Federico Bobbio, Justine Pepin, Warley Almeida Silva, Ricardo Gama, Hugo L. Fernandes, Martin Zaefferer, Manuel López-Ibáñez, Ekhine Irurozki:
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems. CoRR abs/2201.10453 (2022) - 2021
- [c29]Thomas Bartz-Beielstein, Marcel Dröscher, Alpar Gür, Alexander Hinterleitner, Tom Lawton, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Nicolas Rehbach, Frederik Rehbach, Amrita Sen, Aleksandr Subbotin, Martin Zaefferer:
Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic. CEC 2021: 728-735 - [c28]Thomas Bartz-Beielstein, Marcel Dröscher, Alpar Gür, Alexander Hinterleitner, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Nicolas Rehbach, Frederik Rehbach, A. Sen, Aleksandr Subbotin, Martin Zaefferer:
Resource planning for hospitals under special consideration of the COVID-19 pandemic: optimization and sensitivity analysis. GECCO Companion 2021: 293-294 - [c27]Jörg Stork, Martin Zaefferer, Nils Eisler, Patrick Tichelmann, Thomas Bartz-Beielstein, A. E. Eiben:
Behavior-based neuroevolutionary training in reinforcement learning. GECCO Companion 2021: 1753-1761 - [i16]Thomas Bartz-Beielstein, Marcel Dröscher, Alpar Gür, Alexander Hinterleitner, Olaf Mersmann, Dessislava Peeva, Lennard Reese, Nicolas Rehbach, Frederik Rehbach, Amrita Sen, Aleksandr Subbotin, Martin Zaefferer:
Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis. CoRR abs/2105.07420 (2021) - [i15]Jörg Stork, Martin Zaefferer, Nils Eisler, Patrick Tichelmann, Thomas Bartz-Beielstein, A. E. Eiben:
Behavior-based Neuroevolutionary Training in Reinforcement Learning. CoRR abs/2105.07960 (2021) - [i14]Eva Bartz, Martin Zaefferer, Olaf Mersmann, Thomas Bartz-Beielstein:
Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization. CoRR abs/2107.08761 (2021) - [i13]Jörg Stork, Philip Wenzel, Severin Landwein, María-Elena Algorri, Martin Zaefferer, Wolfgang Kusch, Martin Staubach, Thomas Bartz-Beielstein, Hartmut Köhn, Hermann Dejager, Christian Wolf:
Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs. CoRR abs/2107.13977 (2021) - 2020
- [c26]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, A. E. Eiben:
Understanding the Behavior of Reinforcement Learning Agents. BIOMA 2020: 148-160 - [c25]Frederik Rehbach, Martin Zaefferer, Boris Naujoks, Thomas Bartz-Beielstein:
Expected improvement versus predicted value in surrogate-based optimization. GECCO 2020: 868-876 - [c24]Martin Zaefferer, Frederik Rehbach:
Continuous Optimization Benchmarks by Simulation. PPSN (1) 2020: 273-286 - [p2]Tinkle Chugh, Alma A. M. Rahat, Vanessa Volz, Martin Zaefferer:
Towards Better Integration of Surrogate Models and Optimizers. High-Performance Simulation-Based Optimization 2020: 137-163 - [p1]Jörg Stork, Martina Friese, Martin Zaefferer, Thomas Bartz-Beielstein, Andreas Fischbach, Beate Breiderhoff, Boris Naujoks, Tea Tusar:
Open Issues in Surrogate-Assisted Optimization. High-Performance Simulation-Based Optimization 2020: 225-244 - [i12]Frederik Rehbach, Martin Zaefferer, Boris Naujoks, Thomas Bartz-Beielstein:
Expected Improvement versus Predicted Value in Surrogate-Based Optimization. CoRR abs/2001.02957 (2020) - [i11]Martin Zaefferer, Frederik Rehbach:
Continuous Optimization Benchmarks by Simulation. CoRR abs/2008.06249 (2020) - [i10]Tom Peetz, Sebastian Vogt, Martin Zaefferer, Thomas Bartz-Beielstein:
Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools. CoRR abs/2009.01696 (2020)
2010 – 2019
- 2019
- [j3]Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph:
An empirical approach for probing the definiteness of kernels. Soft Comput. 23(21): 10939-10952 (2019) - [c23]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein:
Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels. EvoApplications 2019: 504-519 - [c22]Daniel Horn, Jörg Stork, Nils-Jannik Schüßler, Martin Zaefferer:
Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis. GECCO 2019: 916-924 - [c21]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, A. E. Eiben:
Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning. GECCO 2019: 934-942 - [c20]Alexander Hagg, Martin Zaefferer, Jörg Stork, Adam Gaier:
Prediction of neural network performance by phenotypic modeling. GECCO (Companion) 2019: 1576-1582 - [i9]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein:
Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels. CoRR abs/1902.03419 (2019) - [i8]Alexander Hagg, Martin Zaefferer, Jörg Stork, Adam Gaier:
Prediction of neural network performance by phenotypic modeling. CoRR abs/1907.07075 (2019) - [i7]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, A. E. Eiben:
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning. CoRR abs/1907.09300 (2019) - 2018
- [b1]Martin Zaefferer:
Surrogate models for discrete optimization problems. Dortmund University, Germany, 2018 - [c19]Lorenzo Gentile, Martin Zaefferer, Dario Giugliano, Haofeng Chen, Thomas Bartz-Beielstein:
Surrogate assisted optimization of particle reinforced metal matrix composites. GECCO 2018: 1238-1245 - [c18]Frederik Rehbach, Martin Zaefferer, Jörg Stork, Thomas Bartz-Beielstein:
Comparison of parallel surrogate-assisted optimization approaches. GECCO 2018: 1348-1355 - [c17]Martin Zaefferer, Jörg Stork, Oliver Flasch, Thomas Bartz-Beielstein:
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming. PPSN (2) 2018: 220-231 - [c16]Martin Zaefferer, Daniel Horn:
A First Analysis of Kernels for Kriging-Based Optimization in Hierarchical Search Spaces. PPSN (2) 2018: 399-410 - [i6]Martin Zaefferer, Daniel Horn:
A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces. CoRR abs/1807.01011 (2018) - [i5]Martin Zaefferer, Jörg Stork, Oliver Flasch, Thomas Bartz-Beielstein:
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming. CoRR abs/1807.01019 (2018) - [i4]Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph:
An Empirical Approach For Probing the Definiteness of Kernels. CoRR abs/1807.03555 (2018) - [i3]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein:
Distance-based Kernels for Surrogate Model-based Neuroevolution. CoRR abs/1807.07839 (2018) - 2017
- [j2]Thomas Bartz-Beielstein, Martin Zaefferer:
Model-based methods for continuous and discrete global optimization. Appl. Soft Comput. 55: 154-167 (2017) - [c15]Martin Zaefferer, Andreas Fischbach, Boris Naujoks, Thomas Bartz-Beielstein:
Simulation-based test functions for optimization algorithms. GECCO 2017: 905-912 - [i2]Thomas Bartz-Beielstein, Lorenzo Gentile, Martin Zaefferer:
In a Nutshell: Sequential Parameter Optimization. CoRR abs/1712.04076 (2017) - 2016
- [j1]Martin Zaefferer, Daniel Gaida, Thomas Bartz-Beielstein:
Multi-fidelity modeling and optimization of biogas plants. Appl. Soft Comput. 48: 13-28 (2016) - [c14]Martin Zaefferer, Thomas Bartz-Beielstein:
Efficient Global Optimization with Indefinite Kernels. PPSN 2016: 69-79 - [c13]Carola Doerr, Nicolas Bredèche, Enrique Alba, Thomas Bartz-Beielstein, Dimo Brockhoff, Benjamin Doerr, Gusz Eiben, Michael G. Epitropakis, Carlos M. Fonseca, Andreia P. Guerreiro, Evert Haasdijk, Jacqueline Heinerman, Julien Hubert, Per Kristian Lehre, Luigi Malagò, Juan Julián Merelo Guervós, Julian Francis Miller, Boris Naujoks, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Patricia Ryser-Welch, Giovanni Squillero, Jörg Stork, Dirk Sudholt, Alberto Paolo Tonda, L. Darrell Whitley, Martin Zaefferer:
Tutorials at PPSN 2016. PPSN 2016: 1012-1022 - 2015
- [i1]Steffen Moritz, Alexis Sardá, Thomas Bartz-Beielstein, Martin Zaefferer, Jörg Stork:
Comparison of different Methods for Univariate Time Series Imputation in R. CoRR abs/1510.03924 (2015) - 2014
- [c12]Martin Zaefferer, Jörg Stork, Martina Friese, Andreas Fischbach, Boris Naujoks, Thomas Bartz-Beielstein:
Efficient global optimization for combinatorial problems. GECCO 2014: 871-878 - [c11]Martin Zaefferer, Beate Breiderhoff, Boris Naujoks, Martina Friese, Jörg Stork, Andreas Fischbach, Oliver Flasch, Thomas Bartz-Beielstein:
Tuning multi-objective optimization algorithms for cyclone dust separators. GECCO 2014: 1223-1230 - [c10]Martin Zaefferer, Jörg Stork, Thomas Bartz-Beielstein:
Distance Measures for Permutations in Combinatorial Efficient Global Optimization. PPSN 2014: 373-383 - 2013
- [c9]Martin Zaefferer, Thomas Bartz-Beielstein, Boris Naujoks, Tobias Wagner, Michael Emmerich:
A Case Study on Multi-Criteria Optimization of an Event Detection Software under Limited Budgets. EMO 2013: 756-770 - [c8]Oliver Flasch, Martina Friese, Katya Vladislavleva, Thomas Bartz-Beielstein, Olaf Mersmann, Boris Naujoks, Jörg Stork, Martin Zaefferer:
Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data. EvoApplications 2013: 172-181 - [c7]Thomas Bartz-Beielstein, Martin Zaefferer, Boris Naujoks:
How to create meaningful and generalizable results. GECCO (Companion) 2013: 979-1004 - 2012
- [c6]Thomas Bartz-Beielstein, Oliver Flasch, Martin Zaefferer:
Sequential parameter optimization for symbolic regression. GECCO (Companion) 2012: 495-496 - [c5]Thomas Bartz-Beielstein, Martina Friese, Boris Naujoks, Martin Zaefferer:
SPOT applied to non-stochastic optimization problems: an experimental study. GECCO (Companion) 2012: 645-646 - [c4]Thomas Bartz-Beielstein, Mike Preuß, Martin Zaefferer:
Statistical analysis of optimization algorithms with R. GECCO (Companion) 2012: 1259-1286 - [c3]Martin Zaefferer, Thomas Bartz-Beielstein, Martina Friese, Boris Naujoks, Oliver Flasch:
Multi-criteria optimization for hard problems under limited budgets. GECCO (Companion) 2012: 1451-1452 - 2011
- [c2]Thomas Bartz-Beielstein, Martina Friese, Martin Zaefferer, Boris Naujoks, Oliver Flasch, Wolfgang Konen, Patrick Koch:
Noisy optimization with sequential parameter optimization and optimal computational budget allocation. GECCO (Companion) 2011: 119-120 - 2010
- [c1]Jörg Ziegenhirt, Thomas Bartz-Beielstein, Oliver Flasch, Wolfgang Konen, Martin Zaefferer:
Optimization of biogas production with computational intelligence a comparative study. IEEE Congress on Evolutionary Computation 2010: 1-8
Coauthor Index
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last updated on 2024-12-10 20:43 CET by the dblp team
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