Carola Doerr

4.5k total citations
145 papers, 1.4k citations indexed

About

Carola Doerr is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Carola Doerr has authored 145 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 109 papers in Artificial Intelligence, 73 papers in Computational Theory and Mathematics and 27 papers in Computer Networks and Communications. Recurrent topics in Carola Doerr's work include Metaheuristic Optimization Algorithms Research (79 papers), Evolutionary Algorithms and Applications (54 papers) and Advanced Multi-Objective Optimization Algorithms (50 papers). Carola Doerr is often cited by papers focused on Metaheuristic Optimization Algorithms Research (79 papers), Evolutionary Algorithms and Applications (54 papers) and Advanced Multi-Objective Optimization Algorithms (50 papers). Carola Doerr collaborates with scholars based in France, Germany and Netherlands. Carola Doerr's co-authors include Benjamin Doerr, Carola Winzen, Daniel Johannsen, Thomas Bäck, Timo Kötzing, Hao Wang, Furong Ye, Mahmoud Fouz, Tobias Friedrich and Christian Klein and has published in prestigious journals such as Artificial Intelligence, Journal of the ACM and IEEE Transactions on Evolutionary Computation.

In The Last Decade

Carola Doerr

131 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Carola Doerr France 21 1.1k 721 205 112 91 145 1.4k
Anne Auger France 15 913 0.8× 600 0.8× 80 0.4× 65 0.6× 92 1.0× 52 1.3k
Timo Kötzing Germany 19 920 0.8× 594 0.8× 107 0.5× 79 0.7× 61 0.7× 80 1.1k
Josep Dı́az Spain 16 344 0.3× 721 1.0× 447 2.2× 93 0.8× 32 0.4× 85 1.3k
Per Kristian Lehre United Kingdom 21 1.0k 0.9× 648 0.9× 91 0.4× 62 0.6× 66 0.7× 72 1.3k
Jiong Guo Germany 21 268 0.2× 826 1.1× 345 1.7× 44 0.4× 117 1.3× 83 1.3k
Catherine C. McGeoch United States 16 478 0.4× 254 0.4× 247 1.2× 115 1.0× 69 0.8× 38 878
Marco Protasi Italy 8 300 0.3× 500 0.7× 373 1.8× 175 1.6× 79 0.9× 29 979
Marek Cygan Poland 16 220 0.2× 1.2k 1.7× 509 2.5× 110 1.0× 68 0.7× 85 1.6k
Torben Hagerup Germany 19 636 0.6× 627 0.9× 502 2.4× 31 0.3× 32 0.4× 65 1.3k
Giorgio Gambosi Italy 13 226 0.2× 322 0.4× 362 1.8× 144 1.3× 54 0.6× 58 878

Countries citing papers authored by Carola Doerr

Since Specialization
Citations

This map shows the geographic impact of Carola Doerr'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 Carola Doerr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carola Doerr more than expected).

Fields of papers citing papers by Carola Doerr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Carola Doerr. 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 Carola Doerr. The network helps show where Carola Doerr may publish in the future.

Co-authorship network of co-authors of Carola Doerr

This figure shows the co-authorship network connecting the top 25 collaborators of Carola Doerr. A scholar is included among the top collaborators of Carola Doerr 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 Carola Doerr. Carola Doerr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Doerr, Carola, et al.. (2025). Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 487–490.
2.
3.
Raponi, Elena, et al.. (2024). Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB. Unicam Scientific Publications (University of Camerino). 4(3). 1–33. 9 indexed citations
4.
Vermetten, Diederick, et al.. (2024). Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks. Evolutionary Computation. 33(4). 485–512. 3 indexed citations
5.
Doerr, Carola, et al.. (2024). Generalization Ability of Feature-Based Performance Prediction Models: A Statistical Analysis Across Benchmarks. SPIRE - Sciences Po Institutional REpository. 1–8. 1 indexed citations
6.
Doerr, Carola, et al.. (2023). Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear Functions. Proceedings of the Genetic and Evolutionary Computation Conference. 1565–1574.
7.
Džeroski, Sašo, et al.. (2023). Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances. Proceedings of the Genetic and Evolutionary Computation Conference. 529–537. 4 indexed citations
8.
Raponi, Elena, et al.. (2023). Comparison of Bayesian Optimization Algorithms for BBOB Problems in Dimensions 10 and 60. SPIRE - Sciences Po Institutional REpository. 2390–2393. 1 indexed citations
9.
Doerr, Carola, et al.. (2023). DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems. Proceedings of the Genetic and Evolutionary Computation Conference. 813–821. 3 indexed citations
10.
Doerr, Carola, et al.. (2023). Benchmarking and analyzing iterative optimization heuristics with IOHprofiler. SPIRE - Sciences Po Institutional REpository. 938–945. 1 indexed citations
11.
Vermetten, Diederick, et al.. (2022). The importance of landscape features for performance prediction of modular CMA-ES variants. Proceedings of the Genetic and Evolutionary Computation Conference. 648–656. 5 indexed citations
12.
Doerr, Carola, et al.. (2022). Explainable Model-specific Algorithm Selection for Multi-Label Classification. SPIRE - Sciences Po Institutional REpository. 39–46. 2 indexed citations
13.
Vermetten, Diederick, et al.. (2022). OPTION: OPTImization Algorithm Benchmarking ONtology. IEEE Transactions on Evolutionary Computation. 27(6). 1618–1632. 5 indexed citations
14.
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
15.
Meunier, Laurent, Baptiste Rozière, Jérémy Rapin, et al.. (2020). Black-Box Optimization Revisited: Improving Algorithm Selection Wizards\n through Massive Benchmarking. arXiv (Cornell University). 21 indexed citations
16.
Afshani, Peyman, Manindra Agrawal, Benjamin Doerr, et al.. (2019). The query complexity of a permutation-based variant of Mastermind. Discrete Applied Mathematics. 260. 28–50. 9 indexed citations
17.
Doerr, Carola & Johannes Lengler. (2016). OneMax in Black-Box Models with Several Restrictions. Algorithmica. 78(2). 610–640. 5 indexed citations
18.
Doerr, Carola & Carola Winzen. (2011). Memory-Restricted Black-Box Complexity.. Electronic colloquium on computational complexity. 18. 92. 1 indexed citations
19.
Doerr, Carola, et al.. (2007). A Tight Bound for the (1+1)-EA on the Single Source Shortest Path Problem. Max Planck Institute for Plasma Physics. 1890–1895. 6 indexed citations
20.
Doerr, Carola, Bruno Durand, & Wolfgang Thomas. (2006). Generating Randomized Roundings with Cardinality Constraints and Derandomizations. Max Planck Institute for Plasma Physics. 571–583. 7 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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