James McDermott

2.5k citations
94 papers · 1.9k · h-index 21

Impact in

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

Papers in

James McDermott

90 papers receiving 1.8k citations

Peers

James McDermott
Comparison fields: 5 of 118
  • Artificial Intelligence 1.4k
  • Architecture 65
  • Signal Processing 277
  • Computer Networks and Communications 322
  • Computational Theory and Mathematics 195
Replace Erik Hemberg with:
Erik Hemberg United States
Youssef Hamadi United Kingdom
Ian C. Parmee United Kingdom
Annie S. Wu United States
Peter Nelson United States
David André United States
Nicholas Freitag McPhee United States
Brian J. Ross Canada
Mitchell A. Potter United States
Chuan-Kang Ting Taiwan
James McDermott relative to Erik Hemberg United States Erik Hemberg's profile →
Citations per field
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Erik Hemberg · 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 94 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012218
2 2012183
3 2016172
4 2018138
5 201788
6 201687
7 200863
8 201858
9 201652
10 201047
11 201136
12 201635
13 200934
14 201131
15 201429
16 201228
17 201528
18 201026
19 201224
20 201022

About James McDermott

James McDermott is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Molecular Biology and Computational Theory and Mathematics, having authored 94 papers that have together received 1.9k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (54 papers), Metaheuristic Optimization Algorithms Research (33 papers), Music Technology and Sound Studies (22 papers), Music and Audio Processing (18 papers), Anomaly Detection Techniques and Applications (9 papers), Neuroscience and Music Perception (9 papers), Advanced Multi-Objective Optimization Algorithms (8 papers) and Evolution and Genetic Dynamics (7 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Architecture (65 citations), Signal Processing (277 citations), Computer Networks and Communications (322 citations) and Computational Theory and Mathematics (195 citations). James McDermott has collaborated with scholars based in Ireland, United States and United Kingdom. Frequent co-authors include Van Loi Cao, Michael O’Neill, Miguel Nicolau, Una-May O’Reilly, Anthony Brabazon, Erik Hemberg, Mauro Castelli, Nhien‐An Le‐Khac, Luca Manzoni and Wojciech Jaśkowski. Their work appears in journals such as Genetic Programming and Evolvable Machines, Lecture notes in computer science, Forests, Pattern Recognition Letters and Sustainability.

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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