James McDermott

2.5k citations
51 papers · 984 · h-index 16

Impact in

    • Evolutionary Algorithms and Applications
    • Metaheuristic Optimization Algorithms Research
    • Anomaly Detection Techniques and Applications
    • Reinforcement Learning in Robotics

Papers in

James McDermott

47 papers receiving 937 citations

Peers

James McDermott
Comparison fields: 5 of 111
  • Artificial Intelligence 721
  • Architecture 31
  • Computational Theory and Mathematics 133
  • Signal Processing 82
  • Computer Networks and Communications 103
Replace Ian C. Parmee with:
Ian C. Parmee United Kingdom
Brian J. Ross Canada
B. Bullnheimer Austria
Autilia Vitiello Italy
Edgar Galván Ireland
Jochen Hipp Germany
Matthias Feurer Germany
Mara Abel Brazil
David André United States
Albert Orriols-Puig Spain
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Citations per field
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Ian C. Parmee · 1×
Citations per year

Countries citing papers authored by James McDermott

Since Specialization
Citations

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

Fields of papers citing papers by James McDermott

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside James McDermott, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with James McDermott Line = papers co-authored together James McDermott links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012161
2 2018126
3 2012125
4 201758
5 201849
6 201646
7 200844
8 201032
9 201128
10 200924
11 201424
12 201523
13 201919
14 201018
15 202215
16 202015
17 201514
18 201212
19 201512
20 201210

About James McDermott

James McDermott is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computational Theory and Mathematics and Molecular Biology, having authored 51 papers that have together received 984 indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (27 papers), Metaheuristic Optimization Algorithms Research (15 papers), Music Technology and Sound Studies (10 papers), Music and Audio Processing (6 papers), Advanced Multi-Objective Optimization Algorithms (6 papers), Time Series Analysis and Forecasting (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Design Education and Practice (4 papers). The work is most often cited by research in Artificial Intelligence (721 citations), Architecture (31 citations), Computational Theory and Mathematics (133 citations), Signal Processing (82 citations) and Computer Networks and Communications (103 citations). James McDermott has collaborated with scholars based in Ireland, United States and Portugal. Frequent co-authors include Michael O’Neill, Miguel Nicolau, Van Loi Cao, Una-May O’Reilly, Mauro Castelli, Erik Hemberg, Luca Manzoni, David R. White, Sean Luke and Wojciech Jaśkowski. Their work appears in journals such as Genetic Programming and Evolvable Machines, Forests, Information Sciences, Soft Computing and IEEE Transactions on Neural Networks and Learning Systems.

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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