Christopher W. Cleghorn

780 total citations
32 papers, 464 citations indexed

About

Christopher W. Cleghorn is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Christopher W. Cleghorn has authored 32 papers receiving a total of 464 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 12 papers in Computational Theory and Mathematics and 4 papers in Molecular Biology. Recurrent topics in Christopher W. Cleghorn's work include Metaheuristic Optimization Algorithms Research (24 papers), Evolutionary Algorithms and Applications (13 papers) and Advanced Multi-Objective Optimization Algorithms (12 papers). Christopher W. Cleghorn is often cited by papers focused on Metaheuristic Optimization Algorithms Research (24 papers), Evolutionary Algorithms and Applications (13 papers) and Advanced Multi-Objective Optimization Algorithms (12 papers). Christopher W. Cleghorn collaborates with scholars based in South Africa, United Kingdom and United States. Christopher W. Cleghorn's co-authors include Andries P. Engelbrecht, Steven James, Julian Togelius, Gary Pamparà, Gabriela Ochoa, Pravesh Debba, Kshitij Thorat, C. J. van Rooyen and Roger Deane and has published in prestigious journals such as Information Sciences, Swarm and Evolutionary Computation and Swarm Intelligence.

In The Last Decade

Christopher W. Cleghorn

30 papers receiving 453 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Christopher W. Cleghorn South Africa 13 363 200 65 33 31 32 464
Yinglong Zhang China 7 297 0.8× 168 0.8× 63 1.0× 45 1.4× 19 0.6× 17 408
Jani Rönkkönen Finland 5 422 1.2× 322 1.6× 43 0.7× 31 0.9× 11 0.4× 6 553
Xingguang Peng China 13 204 0.6× 117 0.6× 41 0.6× 116 3.5× 25 0.8× 63 488
Kazuhiro Ohkura Japan 10 209 0.6× 68 0.3× 51 0.8× 59 1.8× 29 0.9× 104 399
Udit Halder United States 9 253 0.7× 164 0.8× 102 1.6× 31 0.9× 22 0.7× 20 438
Pradnya Vikhar India 3 106 0.3× 44 0.2× 29 0.4× 47 1.4× 23 0.7× 3 289
V William Porto United States 8 297 0.8× 57 0.3× 51 0.8× 35 1.1× 11 0.4× 15 417
Shangqin Tang China 9 218 0.6× 80 0.4× 31 0.5× 61 1.8× 15 0.5× 19 374
Wencen Wu United States 14 132 0.4× 14 0.1× 135 2.1× 52 1.6× 17 0.5× 52 488
Sancho Oliveira Portugal 11 212 0.6× 25 0.1× 43 0.7× 61 1.8× 7 0.2× 44 474

Countries citing papers authored by Christopher W. Cleghorn

Since Specialization
Citations

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

Fields of papers citing papers by Christopher W. Cleghorn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christopher W. Cleghorn. 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 Christopher W. Cleghorn. The network helps show where Christopher W. Cleghorn may publish in the future.

Co-authorship network of co-authors of Christopher W. Cleghorn

This figure shows the co-authorship network connecting the top 25 collaborators of Christopher W. Cleghorn. A scholar is included among the top collaborators of Christopher W. Cleghorn 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 Christopher W. Cleghorn. Christopher W. Cleghorn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Engelbrecht, Andries P., et al.. (2024). Training feedforward neural networks with Bayesian hyper-heuristics. Information Sciences. 686. 121363–121363.
2.
Togelius, Julian, et al.. (2024). LLMatic: Neural Architecture Search Via Large Language Models And Quality Diversity Optimization. Proceedings of the Genetic and Evolutionary Computation Conference. 1110–1118. 13 indexed citations
3.
James, Steven, et al.. (2023). Augmentative Topology Agents For Open-Ended Learning. 671–674. 1 indexed citations
4.
Cleghorn, Christopher W., et al.. (2022). Procedural content generation using neuroevolution and novelty search for diverse video game levels. Proceedings of the Genetic and Evolutionary Computation Conference. 1028–1037. 5 indexed citations
5.
Cleghorn, Christopher W., et al.. (2022). A Local Optima Network Analysis of the Feedforward Neural Architecture Space. 2022 International Joint Conference on Neural Networks (IJCNN). 6 indexed citations
6.
Cleghorn, Christopher W. & Gabriela Ochoa. (2021). Understanding parameter spaces using local optima networks. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1657–1664. 4 indexed citations
7.
Cleghorn, Christopher W., et al.. (2021). Point Proposal Network: Accelerating Point Source Detection Through Deep Learning. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). 1–8. 1 indexed citations
8.
Cleghorn, Christopher W., et al.. (2021). Improving transformer model translation for low resource South African languages using BERT. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). 1–8. 3 indexed citations
9.
Engelbrecht, Andries P. & Christopher W. Cleghorn. (2021). Recent advances in particle swarm optimization analysis and understanding 2021. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 341–368. 1 indexed citations
10.
Engelbrecht, Andries P., et al.. (2020). Heuristic Space Diversity Measures for Population-based Hyper-heuristics. 42. 1–9. 1 indexed citations
11.
Cleghorn, Christopher W., et al.. (2020). Topology-Linked Self-Adaptive Quantum Particle Swarm optimization for Dynamic Environments. 1. 1565–1572. 1 indexed citations
12.
Engelbrecht, Andries P., et al.. (2019). Degrees of stochasticity in particle swarm optimization. Swarm Intelligence. 13(3-4). 193–215. 11 indexed citations
13.
Engelbrecht, Andries P., et al.. (2017). The merits of velocity clamping particle swarm optimisation in high dimensional spaces. 1–8. 23 indexed citations
14.
Cleghorn, Christopher W. & Andries P. Engelbrecht. (2017). Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption. Swarm Intelligence. 12(1). 1–22. 68 indexed citations
15.
Cleghorn, Christopher W. & Andries P. Engelbrecht. (2017). Firefly optimization: A study on frame invariance. 1–6. 2 indexed citations
16.
Cleghorn, Christopher W. & Andries P. Engelbrecht. (2016). Unified particle swarm optimizer: Convergence analysis. 447–454. 9 indexed citations
17.
Cleghorn, Christopher W. & Andries P. Engelbrecht. (2015). Fully informed particle swarm optimizer: Convergence analysis. 164–170. 19 indexed citations
18.
Cleghorn, Christopher W. & Andries P. Engelbrecht. (2015). Particle swarm variants: standardized convergence analysis. Swarm Intelligence. 9(2-3). 177–203. 40 indexed citations
19.
Cleghorn, Christopher W. & Andries P. Engelbrecht. (2014). Particle swarm convergence: An empirical investigation. 2524–2530. 47 indexed citations
20.
Cleghorn, Christopher W. & Andries P. Engelbrecht. (2014). A generalized theoretical deterministic particle swarm model. Swarm Intelligence. 8(1). 35–59. 65 indexed citations

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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