Thomas Bartz–Beielstein

TH Köln - University of Applied Sciences — Germany

Computer Science · Physical Sciences

31h-index4.8kcitations273works0.42yr avg
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Research Topics

Advanced Multi-Objective Optimization Algorithms(101), Metaheuristic Optimization Algorithms Research(82), Evolutionary Algorithms and Applications(72), Machine Learning and Data Classification(37), Data Stream Mining Techniques(22)

Publications271 total

Multi-Objective Optimization with Desirability and Morris-Mitchell Criterion
ArXiv.org·2025Open Access
Multi-Objective Optimization with Desirability and Morris-Mitchell Criterion
arXiv (Cornell University)·2025Open Access
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
arXiv (Cornell University)·2025Open Access
Automated Prediction of Compressor Performance Maps: Surrogate-Based Optimization with RNNs for Enhanced Extrapolation and Interpolation
Proceedings of the Genetic and Evolutionary Computation Conference Companion·2025Open Access
Surrogate Model-Based Multi-Objective Optimization Using Desirability Functions
Proceedings of the Genetic and Evolutionary Computation Conference Companion·2025Open Access
Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
arXiv (Cornell University)·2025Open Access
Bed-Attached Vibration Sensor System: A Machine Learning Approach for Fall Detection in Nursing Homes
arXiv (Cornell University)·2024Open Access
A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity
2024Open Access
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution
arXiv (Cornell University)·2024· 4 citedOpen Access
A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity
arXiv (Cornell University)·2024Open Access
Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI
arXiv (Cornell University)·2024Open Access
Initial Selection and Subsequent Updating of OML Models
Machine learning·2024
Ein experimenteller Vergleich von Batch- und Online Machine Learning-Algorithmen
2024
Drifterkennung und –behandlung
2024
Introduction: From Batch to Online Machine Learning
Machine learning·2024
Online Machine Learning
Machine learning·2024· 6 citedOpen Access
Initiale Auswahl und nachträgliche Aktualisierung von OML Modellen
2024
Open-Source-Software für Online Machine Learning
2024
Besondere Anforderungen an OML-Verfahren
2024
Hyperparameter Tuning
2024
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Frequent Co-authors

Martin Zaefferer(11), Jörg Stork(9), Mike Preuß(8), Boris Naujoks(6), Michael N. Vrahatis(4), Sandor Markon(4), Andreas Fischbach(4), Steffen Möritz(3), Konstantinos E. Parsopoulos(3), Martina Friese(3), Bogdan Filipič(3), Christian W. G. Lasarczyk(2), Jörn Mehnen(2), Olaf Mersmann(2), Alexis Sardá(2), Jürgen Branke(2), Wolfgang Konen(2), Patrick Koch(2), Oliver Flasch(2), Marco Chiarandini(1)