Thomas Bäck

22.5k total citations · 5 hit papers
315 papers, 12.3k citations indexed

About

Thomas Bäck is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Thomas Bäck has authored 315 papers receiving a total of 12.3k indexed citations (citations by other indexed papers that have themselves been cited), including 163 papers in Artificial Intelligence, 122 papers in Computational Theory and Mathematics and 40 papers in Management Science and Operations Research. Recurrent topics in Thomas Bäck's work include Advanced Multi-Objective Optimization Algorithms (107 papers), Metaheuristic Optimization Algorithms Research (92 papers) and Evolutionary Algorithms and Applications (68 papers). Thomas Bäck is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (107 papers), Metaheuristic Optimization Algorithms Research (92 papers) and Evolutionary Algorithms and Applications (68 papers). Thomas Bäck collaborates with scholars based in Netherlands, Germany and France. Thomas Bäck's co-authors include Hans–Paul Schwefel, Ulrich Hammel, Frank Hoffmeister, Michael Emmerich, Bas van Stein, Sami Khuri, Wolfgang Konen, Hao Wang, Joost N. Kok and Ofer M. Shir and has published in prestigious journals such as Physical Review A, International Journal of Hydrogen Energy and IEEE Access.

In The Last Decade

Thomas Bäck

298 papers receiving 11.5k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Evolutionary Algorithms in Theory and Practice

1996 2.8k citations Thomas Bäck profile →
Evolutionary algorithms in theory and practice

1996 1.5k citations Thomas Bäck profile →
An Overview of Evolutionary Algorithms for Parameter Optimization

1993 1.4k citations Thomas Bäck et al. profile →
Evolutionary computation: comments on the history and current state

1997 978 citations Thomas Bäck et al. profile →
A Survey of Evolution Strategies.

1991 453 citations Thomas Bäck et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Thomas Bäck	6.4k	3.9k	1.1k	1.1k	1.1k	315	12.3k
Daniel Molina	8.7k 1.4×	3.5k 0.9×	1.1k 0.9×	1.0k 0.9×	1.1k 1.0×	64	13.5k
William G. Macready	6.2k 1.0×	3.0k 0.8×	811 0.7×	951 0.9×	1.3k 1.1×	35	10.6k
A. E. Eiben	5.1k 0.8×	2.4k 0.6×	933 0.8×	846 0.8×	840 0.8×	167	9.2k
Andries P. Engelbrecht	9.0k 1.4×	4.5k 1.2×	891 0.8×	1.9k 1.7×	1.3k 1.2×	366	13.5k
Gary B. Lamont	4.8k 0.7×	5.0k 1.3×	1.3k 1.1×	1.4k 1.2×	1.1k 1.0×	129	10.9k
Gary G. Yen	7.4k 1.2×	5.2k 1.3×	719 0.6×	1.6k 1.4×	888 0.8×	316	12.1k
Yew-Soon Ong	10.7k 1.7×	7.3k 1.9×	1.6k 1.5×	1.1k 1.0×	1.2k 1.1×	448	17.4k
David B. Fogel	5.5k 0.9×	1.9k 0.5×	688 0.6×	986 0.9×	988 0.9×	170	10.0k
Ke Tang	6.5k 1.0×	4.6k 1.2×	1.1k 1.0×	566 0.5×	639 0.6×	280	10.1k
Bahriye Akay	7.2k 1.1×	3.1k 0.8×	1.4k 1.3×	1.8k 1.6×	2.0k 1.8×	56	13.5k

Countries citing papers authored by Thomas Bäck

Since Specialization

Citations

This map shows the geographic impact of Thomas Bäck's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thomas Bäck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Bäck more than expected).

Fields of papers citing papers by Thomas Bäck

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Bäck. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thomas Bäck. The network helps show where Thomas Bäck may publish in the future.

Co-authorship network of co-authors of Thomas Bäck

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Bäck. A scholar is included among the top collaborators of Thomas Bäck based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thomas Bäck. Thomas Bäck is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Bäck, Thomas, et al.. (2025). Machine learning for hydrogen technologies: A comprehensive review of challenges, opportunities, and emerging trends. International Journal of Hydrogen Energy. 197. 152556–152556.

Plaat, Aske, et al.. (2025). Multi-Step Reasoning with Large Language Models, a Survey. ACM Computing Surveys. 58(6). 1–35.

Briaire, Jeroen J., et al.. (2024). Biophysics-inspired spike rate adaptation for computationally efficient phenomenological nerve modeling. Hearing Research. 447. 109011–109011. 3 indexed citations

Vermetten, Diederick, et al.. (2024). A Critical Analysis of Raven Roost Optimization. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1993–2001. 2 indexed citations

Bäck, Thomas, et al.. (2024). Landscape Analysis Based vs. Domain-Specific Optimization for Engineering Design Applications: A Clear Case. Leiden Repository (Leiden University). 776–781.

Neumann, Aneta, et al.. (2024). Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem. Proceedings of the Genetic and Evolutionary Computation Conference. 1036–1044. 4 indexed citations

Vermetten, Diederick, et al.. (2024). Transfer Learning of Surrogate Models via Domain Affine Transformation. Proceedings of the Genetic and Evolutionary Computation Conference. 385–393. 2 indexed citations

Ye, Furong, et al.. (2024). What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference. 787–795. 1 indexed citations

Stein, Bas van, et al.. (2023). Evaluation of deep unsupervised anomaly detection methods with a data-centric approach for on-line inspection. Computers in Industry. 146. 103852–103852. 25 indexed citations

10.

Bonet-Monroig, Xavier, Hao Wang, Diederick Vermetten, et al.. (2023). Performance comparison of optimization methods on variational quantum algorithms. Physical review. A. 107(3). 56 indexed citations

11.

Dunjko, Vedran, et al.. (2023). Hyperparameter importance and optimization of quantum neural networks across small datasets. Machine Learning. 113(4). 1941–1966. 7 indexed citations

12.

Schmitt, Sebastian, et al.. (2022). Finding efficient trade-offs in multi-fidelity response surface modelling. Engineering Optimization. 55(6). 946–963. 1 indexed citations

13.

Stein, Bas van, et al.. (2022). Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines. Journal of Aerospace Information Systems. 19(6). 447–454. 4 indexed citations

14.

Naujoks, Boris, et al.. (2022). Dominance-based variable analysis for large-scale multi-objective problems. Natural Computing. 22(2). 243–257. 3 indexed citations

15.

Bäck, Thomas, et al.. (2022). Deep multiagent reinforcement learning: challenges and directions. Artificial Intelligence Review. 56(6). 5023–5056. 91 indexed citations

16.

Vermetten, Diederick, et al.. (2021). Tuning as a Means of Assessing the Benefits of New Ideas in Interplay\n with Existing Algorithmic Modules. arXiv (Cornell University). 27 indexed citations

17.

Konen, Wolfgang, et al.. (2021). Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing. 112. 107751–107751. 127 indexed citations

18.

Geraedts, Victor J., Thomas Bäck, Jacobus J. van Hilten, et al.. (2021). Preoperative Electroencephalography‐Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation. Movement Disorders. 36(10). 2324–2334. 15 indexed citations

19.

Stein, Bas van, et al.. (2020). Neural network design: learning from Neural Architecture Search. Leiden Repository (Leiden University). 7 indexed citations

20.

Stein, Bas van, Hao Wang, Wojtek Kowalczyk, Michael Emmerich, & Thomas Bäck. (2019). Cluster-based Kriging approximation algorithms for complexity reduction. Leiden Repository (Leiden University). 34 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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