Anna V. Kononova
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- Advanced Multi-Objective Optimization Algorithms 21
- Artificial Intelligence top 5%
- Metaheuristic Optimization Algorithms Research 19
- Evolutionary Algorithms and Applications 14
- Machine Learning and Data Classification 5
- Machine Learning and Algorithms 3
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- Optimal Experimental Design Methods 3
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- Constraint Satisfaction and Optimization 3
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- Hearing Loss and Rehabilitation 3
- Co-authors
- Fabio CaraffiniThomas BäckDavid CorneAske PlaatDiederick VermettenVsevolod ShneerPhilippe De WildeBas van Stein
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceManagement Science and Operations Research
- Journals
- IEEE Access (1 paper)Information Sciences (2 papers)IEEE Transactions on Evolutionary Computation (1 paper)
- Partner nations
- NetherlandsUnited KingdomGermany
In The Last Decade
Anna V. Kononova
45 papers receiving 486 citations
Peers
Comparison fields: 5 of 97
- Computational Theory and Mathematics 179
- Artificial Intelligence 293
- Management Science and Operations Research 52
- Industrial and Manufacturing Engineering 38
- Health Informatics 5
Countries citing papers authored by Anna V. Kononova
This map shows the geographic impact of Anna V. Kononova'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 Anna V. Kononova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anna V. Kononova more than expected).
Fields of papers citing papers by Anna V. Kononova
This network shows the impact of papers produced by Anna V. Kononova. 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 Anna V. Kononova. The network helps show where Anna V. Kononova may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Anna V. Kononova, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 4 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 1 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 4 | |
| 8 | 2024 | 8 | |
| 9 | 2023 | 6 | |
| 10 | 2023 | 28 | |
| 11 | 2023 | 8 | |
| 12 | 2023 | 13 | |
| 13 | 2023 | 2 | |
| 14 | 2022 | 91 | |
| 15 | 2022 | 27 | |
| 16 | 2022 | 2 | |
| 17 | 2021 | 2 | |
| 18 | 2021 | 2 | |
| 19 | 2020 | 1 | |
| 20 | 2019 | 21 |
About Anna V. Kononova
Anna V. Kononova is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Management Science and Operations Research, having authored 50 papers that have together received 493 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (21 papers), Metaheuristic Optimization Algorithms Research (19 papers), Evolutionary Algorithms and Applications (14 papers), Machine Learning and Data Classification (5 papers), Machine Learning and Algorithms (3 papers), Constraint Satisfaction and Optimization (3 papers), Hearing Loss and Rehabilitation (3 papers) and Optimal Experimental Design Methods (3 papers). The work is most often cited by research in Computational Theory and Mathematics (179 citations), Artificial Intelligence (293 citations) and Management Science and Operations Research (52 citations). Anna V. Kononova has collaborated with scholars based in Netherlands, United Kingdom and Germany. Frequent co-authors include Fabio Caraffini, Thomas Bäck, David Corne, Aske Plaat, Diederick Vermetten, Vsevolod Shneer, Philippe De Wilde, Bas van Stein, Hao Wang and Mohamed Pourkashanian. Their work appears in journals such as IEEE Access, Information Sciences and IEEE Transactions on Evolutionary Computation.
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.