Ivan Tanev
Impact in
- Artificial Intelligence top 10%
- Evolutionary Algorithms and Applications
- Reinforcement Learning in Robotics
Papers in
-
- Evolutionary Algorithms and Applications 33
- Reinforcement Learning in Robotics 14
- Metaheuristic Optimization Algorithms Research 10
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- Modular Robots and Swarm Intelligence 15
- Co-authors
- Katsunori Shimohara (78 shared papers)Thomas S. Ray (4 shared papers)Ruediger C. Braun‐Dullaeus (11 shared papers)Andrzej Buller (1 shared paper)Alexander Schmeißer (8 shared papers)Thomas Rauwolf (8 shared papers)Blerim Luani (7 shared papers)Takamasa Iio (9 shared papers)
In The Last Decade
Ivan Tanev
102 papers receiving 643 citations
Peers
Comparison fields: 5 of 115
- Applied Microbiology and Biotechnology 10
- Artificial Intelligence 154
- Cardiology and Cardiovascular Medicine 83
- Human-Computer Interaction 23
- Automotive Engineering 47
Countries citing papers authored by Ivan Tanev
This map shows the geographic impact of Ivan Tanev'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 Ivan Tanev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Tanev more than expected).
Fields of papers citing papers by Ivan Tanev
This network shows the impact of papers produced by Ivan Tanev. 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 Ivan Tanev. The network helps show where Ivan Tanev may publish in the future.
Co-authors
The 25 scholars most cited alongside Ivan Tanev, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 120 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2005 | 66 | |
| 2 | 2020 | 64 | |
| 3 | 2012 | 55 | |
| 4 | 2017 | 38 | |
| 5 | 2021 | 33 | |
| 6 | 2000 | 29 | |
| 7 | 2017 | 19 | |
| 8 | 2010 | 19 | |
| 9 | 2005 | 19 | |
| 10 | 2014 | 17 | |
| 11 | 2005 | 17 | |
| 12 | 2017 | 13 | |
| 13 | 2021 | 12 | |
| 14 | 2017 | 12 | |
| 15 | 2016 | 11 | |
| 16 | 2016 | 10 | |
| 17 | 2019 | 9 | |
| 18 | 2017 | 9 | |
| 19 | 2016 | 9 | |
| 20 | 2006 | 9 |
About Ivan Tanev
Ivan Tanev is a scholar working on Artificial Intelligence, Mechanical Engineering, Automotive Engineering, Social Psychology and Biomedical Engineering, having authored 120 papers that have together received 671 indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (33 papers), Modular Robots and Swarm Intelligence (15 papers), Autonomous Vehicle Technology and Safety (14 papers), Reinforcement Learning in Robotics (14 papers), Metaheuristic Optimization Algorithms Research (10 papers), Social Robot Interaction and HRI (8 papers), Robotic Path Planning Algorithms (8 papers) and Vehicle Dynamics and Control Systems (7 papers). The work is most often cited by research in Applied Microbiology and Biotechnology (10 citations), Artificial Intelligence (154 citations), Cardiology and Cardiovascular Medicine (83 citations), Human-Computer Interaction (23 citations) and Automotive Engineering (47 citations). Ivan Tanev has collaborated with scholars based in Japan, Bulgaria and Germany. Frequent co-authors include Katsunori Shimohara, Thomas S. Ray, Ruediger C. Braun‐Dullaeus, Andrzej Buller, Alexander Schmeißer, Thomas Rauwolf, Blerim Luani, Takamasa Iio, Siegfried Kropf and Mitsuhiko Kimoto. Their work appears in journals such as Genetic Programming and Evolvable Machines, European Heart Journal, ZooKeys, Journal of Personalized Medicine and Clinics in Dermatology.
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.