Manuel Clergue

591 total citations
10 papers, 110 citations indexed

About

Manuel Clergue is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Manuel Clergue has authored 10 papers receiving a total of 110 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 3 papers in Computational Theory and Mathematics and 2 papers in Molecular Biology. Recurrent topics in Manuel Clergue's work include Metaheuristic Optimization Algorithms Research (7 papers), Evolutionary Algorithms and Applications (6 papers) and Evolution and Genetic Dynamics (2 papers). Manuel Clergue is often cited by papers focused on Metaheuristic Optimization Algorithms Research (7 papers), Evolutionary Algorithms and Applications (6 papers) and Evolution and Genetic Dynamics (2 papers). Manuel Clergue collaborates with scholars based in France and Switzerland. Manuel Clergue's co-authors include Philippe Collard, Marco Tomassini, Leonardo Vanneschi, Sebástien Vérel, David Simoncini, Olivier Michel, Philippe Collard, Jin‐Kao Hao and Enrico Formenti and has published in prestigious journals such as Applied Soft Computing, Evolutionary Computation and International Journal of Artificial Intelligence Tools.

In The Last Decade

Manuel Clergue

7 papers receiving 106 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Manuel Clergue France	5	94	32	22	16	3	10	110
Philippe Collard France	7	131 1.4×	63 2.0×	24 1.1×	23 1.4×	4 1.3×	11	157
Yuto Nakashima Japan	5	66 0.7×	35 1.1×	9 0.4×	42 2.6×	1 0.3×	29	73
Morgan Magnin France	6	22 0.2×	35 1.1×	9 0.4×	52 3.3×	5 1.7×	24	93
Will Smart New Zealand	5	89 0.9×	13 0.4×	3 0.1×	20 1.3×	1 0.3×	6	101
Soonho Kong United States	5	27 0.3×	33 1.0×	4 0.2×	9 0.6×	7 2.3×	10	60
Victor Mitrana Spain	7	91 1.0×	93 2.9×	25 1.1×	148 9.3×	8 2.7×	23	175
Tom Gur United States	6	59 0.6×	47 1.5×	90 4.1×	39 2.4×	17 5.7×	27	172
Florencia Leonardi Brazil	6	55 0.6×	4 0.1×	4 0.2×	17 1.1×	4 1.3×	16	89
Robert Mercaş Germany	7	96 1.0×	95 3.0×	3 0.1×	81 5.1×	3 1.0×	32	127
Cyril Nicaud France	4	41 0.4×	49 1.5×	2 0.1×	13 0.8×	5 1.7×	19	63

Countries citing papers authored by Manuel Clergue

Since Specialization

Citations

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

Fields of papers citing papers by Manuel Clergue

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manuel Clergue

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

All Works

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10 of 10 papers shown

1.

Clergue, Manuel, et al.. (2023). A One-Vs-One Approach to Improve Tangled Program Graph Performance on Classification Tasks.

2.

Clergue, Manuel, Sebástien Vérel, & Enrico Formenti. (2018). An Iterated Local Search to find many solutions of the 6-states Firing Squad Synchronization Problem. Applied Soft Computing. 66. 449–461. 1 indexed citations

3.

Simoncini, David, Sebástien Vérel, Philippe Collard, & Manuel Clergue. (2011). Centric selection: a way to tune the exploration/exploitation trade-off. arXiv (Cornell University). 6 indexed citations

4.

Tomassini, Marco, Leonardo Vanneschi, Philippe Collard, & Manuel Clergue. (2005). A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming. Evolutionary Computation. 13(2). 213–239. 72 indexed citations

5.

Vérel, Sebástien, et al.. (2004). How to use the scuba diving metaphor to solve problem with neutrality. European Conference on Artificial Intelligence. 166–170.

6.

Vanneschi, Leonardo, Marco Tomassini, Philippe Collard, & Manuel Clergue. (2003). Fitness distance correlation in genetic programming: a constructive counterexample. 289–296 Vol.1. 4 indexed citations

7.

Clergue, Manuel, et al.. (2003). Homology gives size control in genetic programming. 281–288 Vol.1. 1 indexed citations

8.

Clergue, Manuel, Philippe Collard, Marco Tomassini, & Leonardo Vanneschi. (2002). Fitness Distance Correlation And Problem Difficulty For Genetic Programming. IRIS. 724–732. 15 indexed citations

9.

Collard, Philippe, et al.. (1998). Fitness Distance Correlation, as statistical measure of Genetic Algorithm difficulty, revisited.. European Conference on Artificial Intelligence. 650–654. 8 indexed citations

10.

Michel, Olivier, Manuel Clergue, & Philippe Collard. (1997). Artificial Neurogenesis: Applications to the Cart-Pole Problem and to an Autonomous Mobile Robot. International Journal of Artificial Intelligence Tools. 6(4). 613–634. 3 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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