Aneta Neumann

852 total citations
61 papers, 333 citations indexed

About

Aneta Neumann is a scholar working on Industrial and Manufacturing Engineering, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Aneta Neumann has authored 61 papers receiving a total of 333 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Industrial and Manufacturing Engineering, 28 papers in Computational Theory and Mathematics and 26 papers in Artificial Intelligence. Recurrent topics in Aneta Neumann's work include Advanced Multi-Objective Optimization Algorithms (21 papers), Metaheuristic Optimization Algorithms Research (21 papers) and Vehicle Routing Optimization Methods (15 papers). Aneta Neumann is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (21 papers), Metaheuristic Optimization Algorithms Research (21 papers) and Vehicle Routing Optimization Methods (15 papers). Aneta Neumann collaborates with scholars based in Australia, Germany and Netherlands. Aneta Neumann's co-authors include Frank Neumann, Jakob Bossek, Yue Xie, Mingyu Guo, Denis Antipov, Markus Wagner, Hung Nguyen, Hung T. Nguyen, Pascal Kerschke and Max Ward and has published in prestigious journals such as Theoretical Computer Science, Evolutionary Computation and Algorithmica.

In The Last Decade

Aneta Neumann

52 papers receiving 328 citations

Peers

Aneta Neumann

CTM

IME

CNC

CSE

Farid Nouioua France

María-Cristina Riff Chile

Arit Thammano Thailand

Radu I. Siminiceanu United States

Christine L. Valenzuela United Kingdom

Quentin Cappart Canada

Lev Kazakovtsev Russia

Christian L. Camacho‐Villalón Belgium

Kati Wolter Germany

Patrick Eyerich Germany

Farid Nouioua France

Aneta Neumann

170 ×1.1

84 ×0.6

CTM

40 ×0.3

IME

39 ×0.6

CNC

35 ×1.3

CSE

Citations per year, relative to Aneta Neumann Aneta Neumann (= 1×) peers Farid Nouioua

Countries citing papers authored by Aneta Neumann

Since Specialization

Citations

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

Fields of papers citing papers by Aneta Neumann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aneta Neumann

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

Friedrich, Tobias, et al.. (2025). Fixed Parameter Multi-Objective Evolutionary Algorithms for the W-Separator Problem. Algorithmica. 87(4). 537–571.

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

Stanford, Ty, et al.. (2024). Quality Diversity Approaches for Time-Use Optimisation to Improve Health Outcomes. Proceedings of the Genetic and Evolutionary Computation Conference. 1318–1326.

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

Guo, Mingyu, et al.. (2024). Limited Query Graph Connectivity Test. Proceedings of the AAAI Conference on Artificial Intelligence. 38(18). 20718–20725. 2 indexed citations

Neumann, Frank, et al.. (2024). Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. Proceedings of the Genetic and Evolutionary Computation Conference. 520–528. 7 indexed citations

Doerr, Benjamin, Joshua Knowles, Aneta Neumann, & Frank Neumann. (2024). A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis. Proceedings of the Genetic and Evolutionary Computation Conference. 493–501. 1 indexed citations

Neumann, Frank, et al.. (2024). Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem. Proceedings of the Genetic and Evolutionary Computation Conference. 187–195. 7 indexed citations

Antipov, Denis, Aneta Neumann, & Frank Neumann. (2024). A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax. Proceedings of the Genetic and Evolutionary Computation Conference. 467–475. 2 indexed citations

10.

Ward, Max, et al.. (2024). Hardening Active Directory Graphs via Evolutionary Diversity Optimization-based Policies. UWA Profiles and Research Repository (University of Western Australia). 5(3). 1–36. 1 indexed citations

11.

Neumann, Aneta, et al.. (2024). Multi-Objective Evolutionary Algorithms with Sliding Window Selection for the Dynamic Chance-Constrained Knapsack Problem. Proceedings of the Genetic and Evolutionary Computation Conference. 223–231. 5 indexed citations

12.

Neumann, Aneta & Frank Neumann. (2024). Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multiobjective Algorithms. Evolutionary Computation. 33(3). 363–393.

13.

Neumann, Aneta, et al.. (2024). The Chance Constrained Travelling Thief Problem: Problem Formulations and Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference. 214–222.

14.

Antipov, Denis, Aneta Neumann, & Frank Neumann. (2023). Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. 3–14. 2 indexed citations

15.

Neumann, Aneta, et al.. (2023). Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems. Proceedings of the Genetic and Evolutionary Computation Conference. 1348–1356. 7 indexed citations

16.

Reid, William J., et al.. (2023). Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting. 2 indexed citations

17.

Guo, Mingyu, et al.. (2023). Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint. 5558–5566. 2 indexed citations

18.

Ward, Max, et al.. (2022). Defending active directory by combining neural network based dynamic program and evolutionary diversity optimisation. Proceedings of the Genetic and Evolutionary Computation Conference. 1191–1199. 11 indexed citations

19.

Guo, Mingyu, et al.. (2022). Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 36(9). 9360–9367. 12 indexed citations

20.

Doerr, Benjamin, Carola Doerr, Aneta Neumann, Frank Neumann, & Andrew M. Sutton. (2020). Optimization of Chance-Constrained Submodular Functions. Proceedings of the AAAI Conference on Artificial Intelligence. 34(2). 1460–1467.

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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