Benjamin Doerr

926 total citations
33 papers, 487 citations indexed

About

Benjamin Doerr is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Benjamin Doerr has authored 33 papers receiving a total of 487 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 12 papers in Computational Theory and Mathematics and 3 papers in Molecular Biology. Recurrent topics in Benjamin Doerr's work include Evolutionary Algorithms and Applications (23 papers), Metaheuristic Optimization Algorithms Research (22 papers) and Advanced Multi-Objective Optimization Algorithms (12 papers). Benjamin Doerr is often cited by papers focused on Evolutionary Algorithms and Applications (23 papers), Metaheuristic Optimization Algorithms Research (22 papers) and Advanced Multi-Objective Optimization Algorithms (12 papers). Benjamin Doerr collaborates with scholars based in France, Germany and Denmark. Benjamin Doerr's co-authors include Carsten Witt, Frank Neumann, Mahmoud Fouz, Jing Yang, Dirk Sudholt, Zhongdi Qu, Andrew M. Sutton, Denis Antipov, Timo Kötzing and Martin S. Krejca and has published in prestigious journals such as Artificial Intelligence, Theoretical Computer Science and Soft Computing.

In The Last Decade

Benjamin Doerr

32 papers receiving 482 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Benjamin Doerr France	14	405	274	62	31	31	33	487
Franco Mascia Italy	7	169 0.4×	105 0.4×	90 1.5×	28 0.9×	65 2.1×	10	305
Javier G. Marı́n-Blázquez Spain	10	192 0.5×	51 0.2×	63 1.0×	76 2.5×	77 2.5×	20	292
Andrei Lissovoi United Kingdom	9	219 0.5×	139 0.5×	29 0.5×	17 0.5×	41 1.3×	22	269
Leliane Nunes de Barros Brazil	10	177 0.4×	49 0.2×	43 0.7×	21 0.7×	11 0.4×	43	238
Cláudio N. Meneses Brazil	9	131 0.3×	90 0.3×	41 0.7×	10 0.3×	73 2.4×	21	271
Ewald Speckenmeyer Germany	8	129 0.3×	341 1.2×	199 3.2×	15 0.5×	33 1.1×	26	446
Nils Hebbinghaus Germany	10	225 0.6×	214 0.8×	24 0.4×	23 0.7×	33 1.1×	20	306
Alexey Ignatiev Australia	12	368 0.9×	93 0.3×	55 0.9×	4 0.1×	15 0.5×	38	433
Shuangbao Song Japan	11	215 0.5×	96 0.4×	33 0.5×	24 0.8×	12 0.4×	23	353
Luís M. S.Russo Portugal	9	187 0.5×	112 0.4×	21 0.3×	33 1.1×	9 0.3×	28	277

Countries citing papers authored by Benjamin Doerr

Since Specialization

Citations

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

Fields of papers citing papers by Benjamin Doerr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benjamin Doerr

This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin Doerr. A scholar is included among the top collaborators of Benjamin 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 Benjamin Doerr. Benjamin Doerr 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

Doerr, Benjamin, et al.. (2025). Speeding Up the NSGA-II with a Simple Tie-Breaking Rule. Proceedings of the AAAI Conference on Artificial Intelligence. 39(25). 26964–26972. 1 indexed citations

Doerr, Benjamin & Zhongdi Qu. (2023). Hot off the Press: A First Runtime Analysis of the NSGA-II on a Multimodal Problem. SPIRE - Sciences Po Institutional REpository. 15–16. 2 indexed citations

Doerr, Benjamin & Zhongdi Qu. (2023). Hot off the Press: From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds. SPIRE - Sciences Po Institutional REpository. 17–18. 1 indexed citations

Doerr, Benjamin, et al.. (2023). Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus. Proceedings of the Genetic and Evolutionary Computation Conference. 1555–1564. 2 indexed citations

Doerr, Benjamin, Andrei Lissovoi, & Pietro S. Oliveto. (2023). (1+1) genetic programming with functionally complete instruction sets can evolve Boolean conjunctions and disjunctions with arbitrarily small error. Artificial Intelligence. 319. 103906–103906. 1 indexed citations

Doerr, Benjamin. (2023). A Gentle Introduction to Theory (for Non-Theoreticians). SPIRE - Sciences Po Institutional REpository. 946–975. 1 indexed citations

Doerr, Benjamin & Zhongdi Qu. (2023). From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds. Proceedings of the AAAI Conference on Artificial Intelligence. 37(10). 12408–12416. 26 indexed citations

Doerr, Benjamin, et al.. (2023). How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs. Proceedings of the Genetic and Evolutionary Computation Conference. 990–999. 5 indexed citations

Antipov, Denis & Benjamin Doerr. (2020). Runtime Analysis of a Heavy-Tailed $(1+(λ,λ))$ Genetic Algorithm on Jump Functions. arXiv (Cornell University). 545–559. 10 indexed citations

10.

Doerr, Benjamin & Frank Neumann. (2019). Theory of Evolutionary Computation. SPIRE - Sciences Po Institutional REpository. 95 indexed citations

11.

Doerr, Benjamin, Andrei Lissovoi, & Pietro S. Oliveto. (2019). Evolving Boolean Functions with Conjunctions and Disjunctions via\n Genetic Programming. arXiv (Cornell University). 5 indexed citations

12.

Doerr, Benjamin & Andrew M. Sutton. (2019). When resampling to cope with noise, use median, not mean. Proceedings of the Genetic and Evolutionary Computation Conference. 242–248. 19 indexed citations

13.

Doerr, Benjamin. (2019). A Tight Runtime Analysis for the cGA on Jump Functions---EDAs Can Cross Fitness Valleys at No Extra Cost. arXiv (Cornell University). 20 indexed citations

14.

Doerr, Benjamin. (2018). Better runtime guarantees via stochastic domination (hot-off-the-press track at GECCO 2018). Proceedings of the Genetic and Evolutionary Computation Conference Companion. 13–14.

15.

Doerr, Benjamin, Carsten Witt, & Jing Yang. (2018). Runtime analysis for self-adaptive mutation rates. Proceedings of the Genetic and Evolutionary Computation Conference. 1475–1482. 15 indexed citations

16.

Doerr, Benjamin, et al.. (2017). Bounding bloat in genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference. 921–928. 13 indexed citations

17.

Doerr, Benjamin, et al.. (2017). The (1+ λ ) evolutionary algorithm with self-adjusting mutation rate. Proceedings of the Genetic and Evolutionary Computation Conference. 1351–1358. 30 indexed citations

18.

Asano, Tetsuo & Benjamin Doerr. (2011). Memory-Constrained Algorithms for Shortest Path Problem. Max Planck Digital Library. 111(256). 1–319. 6 indexed citations

19.

Doerr, Benjamin, Mahmoud Fouz, & Carsten Witt. (2011). Sharp bounds by probability-generating functions and variable drift. Max Planck Institute for Plasma Physics. 2083–2090. 35 indexed citations

20.

Doerr, Benjamin, Frank Neumann, Dirk Sudholt, & Carsten Witt. (2007). On the runtime analysis of the 1-ANT ACO algorithm. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 33–40. 28 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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