Geoff Nitschke
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Mechanical Engineering
- Sociology and Political Science
- Control and Systems Engineering
- Co-authors
- Luke TaylorMartijn C. SchutA. E. EibenNeville SweijdDavid HowardD. W. F. van KrevelenOlaf WitkowskiEmma Hart
- Topics
- Reinforcement Learning in Robotics (35 papers)Evolutionary Algorithms and Applications (28 papers)Evolutionary Game Theory and Cooperation (26 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionIndustrial and Manufacturing Engineering
- Partner nations
- South AfricaNetherlandsAustralia
In The Last Decade
Geoff Nitschke
61 papers receiving 695 citations
Hit Papers
Peers
Comparison fields: 5 of 131
- Artificial Intelligence 299
- Computer Vision and Pattern Recognition 163
- Mechanical Engineering 104
- Sociology and Political Science 84
- Control and Systems Engineering 56
Countries citing papers authored by Geoff Nitschke
This map shows the geographic impact of Geoff Nitschke'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 Geoff Nitschke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geoff Nitschke more than expected).
Fields of papers citing papers by Geoff Nitschke
This network shows the impact of papers produced by Geoff Nitschke. 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 Geoff Nitschke. The network helps show where Geoff Nitschke may publish in the future.
Co-authorship network of co-authors of Geoff Nitschke
This figure shows the co-authorship network connecting the top 25 collaborators of Geoff Nitschke. A scholar is included among the top collaborators of Geoff Nitschke 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 Geoff Nitschke. Geoff Nitschke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 15 | |
| 8 | 9 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 4 | |
| 13 | 0 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 39 | |
| 17 | 4 | |
| 18 | 15 | |
| 19 | 0 | |
| 20 | Emergence of cooperation in a pursuit-evasion game | 0 |
About Geoff Nitschke
Geoff Nitschke is a scholar working on Artificial Intelligence, Anatomy and Architecture, having authored 74 papers that have together received 724 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (35 papers), Evolutionary Algorithms and Applications (28 papers) and Evolutionary Game Theory and Cooperation (26 papers). The work is most often cited by research in Artificial Intelligence (299 citations), Computer Vision and Pattern Recognition (163 citations) and Industrial and Manufacturing Engineering (41 citations). Geoff Nitschke has collaborated with scholars based in South Africa, Netherlands and Australia. Frequent co-authors include Luke Taylor, Martijn C. Schut, A. E. Eiben, Neville Sweijd, David Howard, D. W. F. van Krevelen, Olaf Witkowski, Emma Hart, Akira Kakugo and Andrew M. Garrett. Their work appears in journals such as PLoS ONE, Molecular Neurobiology and Lecture notes in computer science.
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.