Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDThomas Bartz-Beielstein
Thomas Beielstein
Person information
- affiliation: TH Köln, Institute for Data Science, Engineering, and Analytics, Germany
- affiliation (former): Technical University of Dortmund, Germany
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[c62]Sowmya Chandrasekaran
, Thomas Bartz-Beielstein:
A Robust Statistical Framework for the Analysis of the Performances of Stochastic Optimization Algorithms Using the Principles of Severity. EvoApplications@EvoStar 2023: 426-441- [p18]Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann:
Tuning: Methodology. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 7-26 - [p17]Thomas Bartz-Beielstein, Martin Zaefferer:
Models. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 27-69 - [p16]Thomas Bartz-Beielstein, Martin Zaefferer:
Hyperparameter Tuning Approaches. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 71-119 - [p15]Thomas Bartz-Beielstein, Olaf Mersmann, Sowmya Chandrasekaran:
Ranking and Result Aggregation. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 121-161 - [p14]Thomas Bartz-Beielstein:
Hyperparameter Tuning and Optimization Applications. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 165-175 - [p13]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 - [p12]Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach:
Case Study II: Tuning of Gradient Boosting (xgboost). Hyperparameter Tuning for Machine and Deep Learning with R 2023: 221-234 - [p11]Thomas Bartz-Beielstein, Sowmya Chandrasekaran, Frederik Rehbach:
Case Study III: Tuning of Deep Neural Networks. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 235-269 - [p10]Martin Zaefferer, Olaf Mersmann, Thomas Bartz-Beielstein:
Global Study: Influence of Tuning. Hyperparameter Tuning for Machine and Deep Learning with R 2023: 283-301 - [e5]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] - [i30]Thomas Bartz-Beielstein:
PyTorch Hyperparameter Tuning - A Tutorial for spotPython. CoRR abs/2305.11930 (2023) - [i29]Thomas Bartz-Beielstein:
Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython. CoRR abs/2307.10262 (2023) - 2022
- [j16]Aljosa Vodopija, Jörg Stork, Thomas Bartz-Beielstein, Bogdan Filipic:
Elevator group control as a constrained multiobjective optimization problem. Appl. Soft Comput. 115: 108277 (2022) - [j15]Margarita Rebolledo, Daan Zeeuwe, Thomas Bartz-Beielstein, A. E. Eiben:
Co-optimizing for task performance and energy efficiency in evolvable robots. Eng. Appl. Artif. Intell. 113: 104968 (2022) - [j14]Jörg Stork, A. E. Eiben, Thomas Bartz-Beielstein:
A new taxonomy of global optimization algorithms. Nat. Comput. 21(2): 219-242 (2022) - [j13]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) - 2021
- [c61]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 - [c60]Margarita Rebolledo, A. E. Eiben, Thomas Bartz-Beielstein:
Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments. EvoApplications 2021: 373-387 - [c59]Margarita Rebolledo, Daan Zeeuwe, Thomas Bartz-Beielstein, A. E. Eiben:
Impact of energy efficiency on the morphology and behaviour of evolved robots. GECCO Companion 2021: 109-110 - [c58]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 - [c57]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 - [i28]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) - [i27]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) - [i26]Thomas Bartz-Beielstein:
Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT. CoRR abs/2105.14625 (2021) - [i25]Margarita Rebolledo, Daan Zeeuwe, Thomas Bartz-Beielstein, A. E. Eiben:
Impact of Energy Efficiency on the Morphology and Behaviour of Evolved Robots. CoRR abs/2107.05249 (2021) - [i24]Eva Bartz, Martin Zaefferer, Olaf Mersmann, Thomas Bartz-Beielstein:
Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization. CoRR abs/2107.08761 (2021) - [i23]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
- [j12]Andreas Fischbach, Thomas Bartz-Beielstein:
Improving the reliability of test functions generators. Appl. Soft Comput. 92: 106315 (2020) - [c56]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, A. E. Eiben:
Understanding the Behavior of Reinforcement Learning Agents. BIOMA 2020: 148-160 - [c55]Margarita Rebolledo, Ruxandra Stoean, A. E. Eiben, Thomas Bartz-Beielstein:
Hybrid Variable Selection and Support Vector Regression for Gas Sensor Optimization. BIOMA 2020: 281-293 - [c54]Lorenzo Gentile, Gianluca Filippi, Edmondo A. Minisci, Thomas Bartz-Beielstein, Massimiliano Vasile:
Preliminary spacecraft design by means of Structured-Chromosome Genetic Algorithms. CEC 2020: 1-8 - [c53]Lorenzo Gentile, Elisa Morales, Domenico Quagliarella, Edmondo A. Minisci, Thomas Bartz-Beielstein, Renato Tognaccini:
High-Lift Devices Topology Optimisation using Structured-Chromosome Genetic Algorithm. CEC 2020: 1-9 - [c52]Margarita Alejandra Rebolledo Coy, Frederik Rehbach, A. E. Eiben, Thomas Bartz-Beielstein:
Parallelized bayesian optimization for problems with expensive evaluation functions. GECCO Companion 2020: 231-232 - [c51]Frederik Rehbach, Martin Zaefferer, Boris Naujoks, Thomas Bartz-Beielstein:
Expected improvement versus predicted value in surrogate-based optimization. GECCO 2020: 868-876 - [c50]Frederik Rehbach, Lorenzo Gentile, Thomas Bartz-Beielstein:
Variable reduction for surrogate-based optimization. GECCO 2020: 1177-1185 - [c49]Margarita Alejandra Rebolledo Coy, Frederik Rehbach, A. E. Eiben, Thomas Bartz-Beielstein:
Parallelized Bayesian Optimization for Expensive Robot Controller Evolution. PPSN (1) 2020: 243-256 - [p9]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 - [e4]Thomas Bartz-Beielstein, Bogdan Filipic, Peter Korosec, El-Ghazali Talbi:
High-Performance Simulation-Based Optimization. Studies in Computational Intelligence 833, Springer 2020, ISBN 978-3-030-18763-7 [contents] - [i22]Frederik Rehbach, Martin Zaefferer, Boris Naujoks, Thomas Bartz-Beielstein:
Expected Improvement versus Predicted Value in Surrogate-Based Optimization. CoRR abs/2001.02957 (2020) - [i21]Andreas Fischbach, Jan Strohschein, Andreas Bunte, Jörg Stork, Heide Faeskorn-Woyke, Natalia Moriz, Thomas Bartz-Beielstein:
CAAI - A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems. CoRR abs/2003.00925 (2020) - [i20]Thomas Bartz-Beielstein, Carola Doerr, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, Manuel López-Ibáñez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise:
Benchmarking in Optimization: Best Practice and Open Issues. CoRR abs/2007.03488 (2020) - [i19]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) - [i18]Thomas Bartz-Beielstein, Eva Bartz, Frederik Rehbach, Olaf Mersmann:
Optimization of High-dimensional Simulation Models Using Synthetic Data. CoRR abs/2009.02781 (2020) - [i17]Margarita Alejandra Rebolledo Coy, Sowmya Chandrasekaran, Thomas Bartz-Beielstein:
Sensor Placement for Contamination Detection in Water Distribution Systems. CoRR abs/2011.06406 (2020) - [i16]Sowmya Chandrasekaran, Margarita Rebolledo, Thomas Bartz-Beielstein:
EventDetectR - An Open-Source Event Detection System. CoRR abs/2011.09833 (2020) - [i15]Jan Strohschein, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz, Thomas Bartz-Beielstein:
Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems. CoRR abs/2012.01823 (2020) - [i14]Thomas Bartz-Beielstein, Frederik Rehbach, Olaf Mersmann, Eva Bartz:
Hospital Capacity Planning Using Discrete Event Simulation Under Special Consideration of the COVID-19 Pandemic. CoRR abs/2012.07188 (2020) - [i13]Margarita Rebolledo, Sowmya Chandrasekaran, Thomas Bartz-Beielstein:
Technical Report: Flushing Strategies in Drinking Water Systems. CoRR abs/2012.13574 (2020)
2010 – 2019
- 2019
- [j11]Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph:
An empirical approach for probing the definiteness of kernels. Soft Comput. 23(21): 10939-10952 (2019) - [c48]Cristian Greco, Lorenzo Gentile, Gianluca Filippi, Edmondo A. Minisci, Massimiliano Vasile, Thomas Bartz-Beielstein:
Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation. CEC 2019: 497-505 - [c47]Andreas Bunte, Andreas Fischbach, Jan Strohschein, Thomas Bartz-Beielstein, Heide Faeskorn-Woyke, Oliver Niggemann:
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems. ETFA 2019: 729-736 - [c46]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein:
Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels. EvoApplications 2019: 504-519 - [c45]Frederik Rehbach, Lorenzo Gentile, Thomas Bartz-Beielstein:
Feature selection for surrogate model-based optimization. GECCO (Companion) 2019: 399-400 - [c44]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 - [c43]Lorenzo Gentile, Cristian Greco, Edmondo A. Minisci, Thomas Bartz-Beielstein, Massimiliano Vasile:
Structured-chromosome GA optimisation for satellite tracking. GECCO (Companion) 2019: 1955-1963 - [i12]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein:
Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels. CoRR abs/1902.03419 (2019) - [i11]Andreas Bunte, Andreas Fischbach, Jan Strohschein, Thomas Bartz-Beielstein, Heide Faeskorn-Woyke, Oliver Niggemann:
Evaluation of Cognitive Architectures for Cyber-Physical Production Systems. CoRR abs/1902.08448 (2019) - [i10]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) - [i9]Thomas Bartz-Beielstein:
Why we need an AI-resilient society. CoRR abs/1912.08786 (2019) - 2018
- [j10]Frederik Rehbach, Jörg Stork, Thomas Bartz-Beielstein:
Bridging Theory and Practice Through Modular Graphical User Interfaces. J. Multim. Process. Technol. 9(4): 134-140 (2018) - [c42]Lorenzo Gentile, Martin Zaefferer, Dario Giugliano, Haofeng Chen, Thomas Bartz-Beielstein:
Surrogate assisted optimization of particle reinforced metal matrix composites. GECCO 2018: 1238-1245 - [c41]Frederik Rehbach, Martin Zaefferer, Jörg Stork, Thomas Bartz-Beielstein:
Comparison of parallel surrogate-assisted optimization approaches. GECCO 2018: 1348-1355 - [c40]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 - [i8]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) - [i7]Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph:
An Empirical Approach For Probing the Definiteness of Kernels. CoRR abs/1807.03555 (2018) - [i6]Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein:
Distance-based Kernels for Surrogate Model-based Neuroevolution. CoRR abs/1807.07839 (2018) - [i5]Jörg Stork, A. E. Eiben, Thomas Bartz-Beielstein:
A new Taxonomy of Continuous Global Optimization Algorithms. CoRR abs/1808.08818 (2018) - 2017
- [j9]Thomas Bartz-Beielstein, Martin Zaefferer:
Model-based methods for continuous and discrete global optimization. Appl. Soft Comput. 55: 154-167 (2017) - [j8]Alexis Sardá, Subanatarajan Subbiah, Thomas Bartz-Beielstein:
Conditional inference trees for knowledge extraction from motor health condition data. Eng. Appl. Artif. Intell. 62: 26-37 (2017) - [j7]Steffen Moritz, Thomas Bartz-Beielstein:
imputeTS: Time Series Missing Value Imputation in R. R J. 9(1): 207 (2017) - [c39]Jacqueline Heinerman, Jörg Stork, Margarita Alejandra Rebolledo Coy, Julien Hubert, Thomas Bartz-Beielstein, A. E. Eiben, Evert Haasdijk:
Can social learning increase learning speed, performance or both? ECAL 2017: 200-207 - [c38]Jacqueline Heinerman, Jörg Stork, Margarita Alejandra Rebolledo Coy, Julien Hubert, A. E. Eiben, Thomas Bartz-Beielstein, Evert Haasdijk:
Is social learning more than parameter tuning? GECCO (Companion) 2017: 63-64 - [c37]Martin Zaefferer, Andreas Fischbach, Boris Naujoks, Thomas Bartz-Beielstein:
Simulation-based test functions for optimization algorithms. GECCO 2017: 905-912 - [i4]Thomas Bartz-Beielstein, Lorenzo Gentile, Martin Zaefferer:
In a Nutshell: Sequential Parameter Optimization. CoRR abs/1712.04076 (2017) - 2016
- [j6]Martin Zaefferer, Daniel Gaida, Thomas Bartz-Beielstein:
Multi-fidelity modeling and optimization of biogas plants. Appl. Soft Comput. 48: 13-28 (2016) - [c36]Martin Zaefferer, Thomas Bartz-Beielstein:
Efficient Global Optimization with Indefinite Kernels. PPSN 2016: 69-79 - [c35]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
- [p8]Thomas Bartz-Beielstein:
How to Create Generalizable Results. Handbook of Computational Intelligence 2015: 1127-1142 - [i3]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
- [j5]Thomas Bartz-Beielstein, Jürgen Branke, Jörn Mehnen, Olaf Mersmann:
Evolutionary Algorithms. WIREs Data Mining Knowl. Discov. 4(3): 178-195 (2014) - [c34]Martin Zaefferer, Jörg Stork, Martina Friese, Andreas Fischbach, Boris Naujoks, Thomas Bartz-Beielstein:
Efficient global optimization for combinatorial problems. GECCO 2014: 871-878 - [c33]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 - [c32]Martin Zaefferer, Jörg Stork, Thomas Bartz-Beielstein:
Distance Measures for Permutations in Combinatorial Efficient Global Optimization. PPSN 2014: 373-383 - [p7]Thomas Bartz-Beielstein, Mike Preuss:
Experimental Analysis of Optimization Algorithms: Tuning and Beyond. Theory and Principled Methods for the Design of Metaheuristics 2014: 205-245 - [e3]Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipic, Jim Smith:
Parallel Problem Solving from Nature - PPSN XIII - 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings. Lecture Notes in Computer Science 8672, Springer 2014, ISBN 978-3-319-10761-5 [contents] - 2013
- [c31]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 - [c30]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 - [c29]Thomas Bartz-Beielstein, Martin Zaefferer, Boris Naujoks:
How to create meaningful and generalizable results. GECCO (Companion) 2013: 979-1004 - 2012
- [j4]Gabriela Ochoa, Mike Preuss, Thomas Bartz-Beielstein, Marc Schoenauer:
Editorial for the Special Issue on Automated Design and Assessment of Heuristic Search Methods. Evol. Comput. 20(2): 161-163 (2012) - [j3]Patrick Koch, Bernd Bischl, Oliver Flasch, Thomas Bartz-Beielstein, Claus Weihs, Wolfgang Konen:
Tuning and evolution of support vector kernels. Evol. Intell. 5(3): 153-170 (2012) - [c28]Thomas Bartz-Beielstein, Oliver Flasch, Martin Zaefferer:
Sequential parameter optimization for symbolic regression. GECCO (Companion) 2012: 495-496 - [c27]Thomas Bartz-Beielstein, Martina Friese, Boris Naujoks, Martin Zaefferer:
SPOT applied to non-stochastic optimization problems: an experimental study. GECCO (Companion) 2012: 645-646 - [c26]Thomas Bartz-Beielstein, Mike Preuß, Martin Zaefferer:
Statistical analysis of optimization algorithms with R. GECCO (Companion) 2012: 1259-1286 - [c25]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
- [c24]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 - [c23]Thomas Bartz-Beielstein, Mike Preuss:
Automatic and interactive tuning of algorithms. GECCO (Companion) 2011: 1361-1380 - [c22]Wolfgang Konen, Patrick Koch, Oliver Flasch, Thomas Bartz-Beielstein, Martina Friese, Boris Naujoks:
Tuned data mining: a benchmark study on different tuners. GECCO 2011: 1995-2002 - 2010
- [c21]Oliver Flasch, Thomas Bartz-Beielstein, Artur Davtyan, Patrick Koch, Wolfgang Konen, Tosin Daniel Oyetoyan, Michael Tamutan:
Comparing SPO-tuned GP and NARX prediction models for stormwater tank fill level prediction. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c20]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 - [c19]Oliver Flasch, Olaf Mersmann, Thomas Bartz-Beielstein:
RGP: an open source genetic programming system for the R environment. GECCO (Companion) 2010: 2071-2072 - [c18]Thomas Bartz-Beielstein, Mike Preuss:
Tuning and experimental analysis in evolutionary computation: what we still have wrong. GECCO (Companion) 2010: 2625-2646 - [p6]Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss:
Introduction. Experimental Methods for the Analysis of Optimization Algorithms 2010: 1-13 - [p5]Thomas Bartz-Beielstein, Mike Preuss:
The Future of Experimental Research. Experimental Methods for the Analysis of Optimization Algorithms 2010: 17-49 - [p4]Thomas Bartz-Beielstein, Christian Lasarczyk, Mike Preuss:
The Sequential Parameter Optimization Toolbox. Experimental Methods for the Analysis of Optimization Algorithms 2010: 337-362 - [p3]Frank Hutter, Thomas Bartz-Beielstein, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy:
Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches. Experimental Methods for the Analysis of Optimization Algorithms 2010: 363-414 - [e2]Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss:
Experimental Methods for the Analysis of Optimization Algorithms. Springer 2010, ISBN 978-3-642-02537-2 [contents] - [i2]Thomas Bartz-Beielstein:
SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization. CoRR abs/1006.4645 (2010)
2000 – 2009
- 2009
- [j2]Wolfgang Konen, Tobias Zimmer, Thomas Bartz-Beielstein:
Optimierte Modellierung von Füllständen in Regenüberlaufbecken mittels CI-basierter Parameterselektion (Optimized Modelling of Fill Levels in Stormwater Tanks Using CI-based Parameter Selection Schemes). Autom. 57(3): 155-166 (2009) - [c17]Wolfgang Konen, Thomas Bartz-Beielstein:
Reinforcement learning for games: failures and successes. GECCO (Companion) 2009: 2641-2648 - [c16]Thomas Bartz-Beielstein, Mike Preuss:
the future of experimental research. GECCO (Companion) 2009: 3185-3226 - [i1]Thomas Bartz-Beielstein:
Sequential Parameter Optimization. Sampling-based Optimization in the Presence of Uncertainty 2009 - 2008
- [j1]Thomas Bartz-Beielstein:
How experimental algorithmics can benefit from Mayo's extensions to Neyman-Pearson theory of testing. Synth. 163(3): 385-396 (2008) - [c15]Thomas Bartz-Beielstein, Mike Preuss:
Experimental research in evolutionary computation. GECCO (Companion) 2008: 2517-2534 - [c14]