Yu Setoguchi
Impact in
- Computational Theory and Mathematics top 0.5%
- Advanced Multi-Objective Optimization Algorithms
- Artificial Intelligence top 2%
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
Papers in
-
- Advanced Multi-Objective Optimization Algorithms 9
-
- Evolutionary Algorithms and Applications 8
- Metaheuristic Optimization Algorithms Research 8
- Co-authors
- Hisao Ishibuchi (9 shared papers)Yusuke Nojima (8 shared papers)Ryo Imada (5 shared papers)Hiroyuki Masuda (3 shared papers)Yuki Tanigaki (2 shared papers)Bernhard Sendhoff (1 shared paper)Markus Olhofer (1 shared paper)Kaname Narukawa (1 shared paper)
- Journals
- IEEE Transactions on Evolutionary Computation (2 papers)Evolutionary Computation (1 paper)Soft Computing (1 paper)Proceedings of the Genetic and Evolutionary Computation Conference (1 paper)
In The Last Decade
Yu Setoguchi
9 papers receiving 933 citations
Yu Setoguchi's Hit Papers
Peers
Comparison fields: 5 of 70
- Computational Theory and Mathematics 712
- Artificial Intelligence 621
- Management Science and Operations Research 185
- Statistics, Probability and Uncertainty 60
- Industrial and Manufacturing Engineering 68
Countries citing papers authored by Yu Setoguchi
This map shows the geographic impact of Yu Setoguchi'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 Yu Setoguchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu Setoguchi more than expected).
Fields of papers citing papers by Yu Setoguchi
This network shows the impact of papers produced by Yu Setoguchi. 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 Yu Setoguchi. The network helps show where Yu Setoguchi may publish in the future.
Co-authors
The 8 scholars most cited alongside Yu Setoguchi, 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 | Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes Hit paper breakdown → | 2016 | 419 |
| 2 | 2018 | 149 | |
| 3 | 2016 | 134 | |
| 4 | 2018 | 116 | |
| 5 | 2017 | 70 | |
| 6 | 2016 | 25 | |
| 7 | 2015 | 15 | |
| 8 | 2017 | 12 | |
| 9 | 2015 | 3 |
About Yu Setoguchi
Yu Setoguchi is a scholar working on Computational Theory and Mathematics, Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research and Infectious Diseases, having authored 9 papers that have together received 943 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (9 papers), Evolutionary Algorithms and Applications (8 papers), Metaheuristic Optimization Algorithms Research (8 papers), Optimal Experimental Design Methods (1 paper) and Advanced Control Systems Optimization (1 paper). The work is most often cited by research in Computational Theory and Mathematics (712 citations), Artificial Intelligence (621 citations), Management Science and Operations Research (185 citations), Statistics, Probability and Uncertainty (60 citations) and Industrial and Manufacturing Engineering (68 citations). Yu Setoguchi has collaborated with scholars based in Japan, China and Germany. Frequent co-authors include Hisao Ishibuchi, Yusuke Nojima, Ryo Imada, Hiroyuki Masuda, Yuki Tanigaki, Bernhard Sendhoff, Markus Olhofer and Kaname Narukawa. Their work appears in journals such as IEEE Transactions on Evolutionary Computation, Evolutionary Computation, Soft Computing and Proceedings of the Genetic and Evolutionary Computation Conference.
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.