Jonathan E. Rowe

2.9k total citations
81 papers, 1.5k citations indexed

About

Jonathan E. Rowe is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Jonathan E. Rowe has authored 81 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Artificial Intelligence, 17 papers in Computational Theory and Mathematics and 16 papers in Molecular Biology. Recurrent topics in Jonathan E. Rowe's work include Evolutionary Algorithms and Applications (36 papers), Metaheuristic Optimization Algorithms Research (36 papers) and Algorithms and Data Compression (11 papers). Jonathan E. Rowe is often cited by papers focused on Evolutionary Algorithms and Applications (36 papers), Metaheuristic Optimization Algorithms Research (36 papers) and Algorithms and Data Compression (11 papers). Jonathan E. Rowe collaborates with scholars based in United Kingdom, United States and Germany. Jonathan E. Rowe's co-authors include Colin R. Reeves, Dirk Sudholt, Chrisantha Fernando, Michael D. Vose, Alden H. Wright, Riccardo Poli, Nicholas Freitag McPhee, Darrell Whitley, C. Cannings and Thorsten Lenser and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Jonathan E. Rowe

78 papers receiving 1.4k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Jonathan E. Rowe 756 370 235 127 115 81 1.5k
Ingo Rechenberg 814 1.1× 455 1.2× 120 0.5× 153 1.2× 80 0.7× 14 1.8k
Lashon B. Booker 1000 1.3× 356 1.0× 166 0.7× 71 0.6× 70 0.6× 22 1.7k
Adam Prügel‐Bennett 1.1k 1.4× 271 0.7× 206 0.9× 176 1.4× 140 1.2× 96 2.3k
Nicol N. Schraudolph 1.4k 1.8× 424 1.1× 425 1.8× 94 0.7× 73 0.6× 45 2.5k
Joel Lehman 1.6k 2.1× 295 0.8× 122 0.5× 127 1.0× 158 1.4× 50 2.2k
Conor Ryan 1.6k 2.1× 275 0.7× 400 1.7× 361 2.8× 113 1.0× 190 2.7k
Peter J. Angeline 1.6k 2.1× 360 1.0× 180 0.8× 219 1.7× 65 0.6× 46 2.2k
Marco Tomassini 742 1.0× 631 1.7× 305 1.3× 151 1.2× 450 3.9× 107 2.3k
Kumar Chellapilla 1.2k 1.7× 209 0.6× 157 0.7× 150 1.2× 41 0.4× 60 2.3k
Chao Luo 406 0.5× 160 0.4× 153 0.7× 121 1.0× 114 1.0× 111 1.7k

Countries citing papers authored by Jonathan E. Rowe

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan E. Rowe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan E. Rowe. 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 Jonathan E. Rowe. The network helps show where Jonathan E. Rowe may publish in the future.

Co-authorship network of co-authors of Jonathan E. Rowe

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan E. Rowe. A scholar is included among the top collaborators of Jonathan E. Rowe 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 Jonathan E. Rowe. Jonathan E. Rowe 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.
Rowe, Jonathan E., et al.. (2024). El Botellón: Modeling the Movement of Crowds in a City. Complex Systems. 14(4). 363–370.
2.
3.
Kirubarajan, T., et al.. (2023). UAV path planning in presence of occlusions as noisy combinatorial multi-objective optimisation. International Journal of Bio-Inspired Computation. 21(4). 209–217. 7 indexed citations
4.
Rowe, Jonathan E., et al.. (2020). Simultaneous diffuse optical and bioluminescence tomography to account for signal attenuation to improve source localization. Biomedical Optics Express. 11(11). 6428–6428. 3 indexed citations
5.
Rowe, Jonathan E.. (2018). Linear multi-objective drift analysis. Theoretical Computer Science. 736. 25–40. 2 indexed citations
6.
Friedrich, Tobias & Jonathan E. Rowe. (2014). Genetic and Evolutionary Computation. Theoretical Computer Science. 545. 1–1. 2 indexed citations
7.
Rowe, Jonathan E. & Dirk Sudholt. (2013). The choice of the offspring population size in the (1,λ) evolutionary algorithm. Theoretical Computer Science. 545. 20–38. 72 indexed citations
8.
Rowe, Jonathan E., Michael D. Vose, & Alden H. Wright. (2007). Neighborhood graphs and symmetric genetic operators. 110–122. 5 indexed citations
9.
Fernando, Chrisantha & Jonathan E. Rowe. (2007). Natural selection in chemical evolution. Journal of Theoretical Biology. 247(1). 152–167. 31 indexed citations
10.
Whitley, Darrell & Jonathan E. Rowe. (2006). Subthreshold-seeking local search. Theoretical Computer Science. 361(1). 2–17. 5 indexed citations
11.
Rowe, Jonathan E., Michael D. Vose, & Alden H. Wright. (2006). Differentiable coarse graining. Theoretical Computer Science. 361(1). 111–129. 5 indexed citations
12.
Rowe, Jonathan E., et al.. (2006). Some results about the Markov chains associated to GPs and general EAs. Theoretical Computer Science. 361(1). 72–110. 13 indexed citations
13.
Rowe, Jonathan E., et al.. (2006). Markov models of integrating spheres for hyperspectral imaging. University of Birmingham Research Portal (University of Birmingham). 45(21). 5248–5248. 7 indexed citations
14.
Yao, Xin, Edmund Burke, José A. Lozano, et al.. (2004). Parallel Problem Solving from Nature - PPSN VIII : 8th International Conference, Birmingham, UK, September 18-22, 2004. Proceedings. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 19 indexed citations
15.
Poli, Riccardo, Christopher R. Stephens, Alden H. Wright, & Jonathan E. Rowe. (2002). On the search biases of homologous crossover in linear genetic programming and variable-length genetic algorithms. Genetic and Evolutionary Computation Conference. 868–876. 9 indexed citations
16.
Stephens, Christopher R., Riccardo Poli, Alden H. Wright, & Jonathan E. Rowe. (2002). Exact Results From A Coarse Grained Formulation Of The Dynamics Of Variable-length Genetic Algorithms. Genetic and Evolutionary Computation Conference. 578–585. 4 indexed citations
17.
Wright, Alden H., Jonathan E. Rowe, Riccardo Poli, & Christopher R. Stephens. (2002). A fixed point analysis of a gene pool GA with mutation. Genetic and Evolutionary Computation Conference. 642–649. 10 indexed citations
18.
Rowe, Jonathan E. & Nicholas Freitag McPhee. (2001). The effects of crossover and mutation operators on variable length linear structures. OpenGrey (Institut de l'Information Scientifique et Technique). 535–542. 12 indexed citations
19.
Poli, Riccardo, Jonathan E. Rowe, & Nicholas Freitag McPhee. (2001). Markov chain models for GP and variable-length GAs with homologous crossover. Genetic and Evolutionary Computation Conference. 112–119. 15 indexed citations
20.
Bower, Mark, et al.. (1999). An evolutionary approach to constructing prognostic models. Artificial Intelligence in Medicine. 15(2). 155–165. 8 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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