Philippe Collard

627 total citations
11 papers, 157 citations indexed

About

Philippe Collard is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Philippe Collard has authored 11 papers receiving a total of 157 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 5 papers in Computational Theory and Mathematics and 2 papers in Molecular Biology. Recurrent topics in Philippe Collard's work include Metaheuristic Optimization Algorithms Research (11 papers), Evolutionary Algorithms and Applications (10 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). Philippe Collard is often cited by papers focused on Metaheuristic Optimization Algorithms Research (11 papers), Evolutionary Algorithms and Applications (10 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). Philippe Collard collaborates with scholars based in France, Switzerland and Italy. Philippe Collard's co-authors include Manuel Clergue, Marco Tomassini, Leonardo Vanneschi, Sebástien Vérel, David Simoncini, Giancarlo Mauri and Yuri Pirola and has published in prestigious journals such as Theoretical Computer Science, Evolutionary Computation and International Journal of Artificial Intelligence Tools.

In The Last Decade

Philippe Collard

10 papers receiving 145 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Philippe Collard France	7	131	63	24	23	5	11	157
Manuel Clergue France	5	94 0.7×	32 0.5×	22 0.9×	16 0.7×	3 0.6×	10	110
Stephen Dignum United Kingdom	5	110 0.8×	11 0.2×	7 0.3×	34 1.5×	2 0.4×	6	132
Jean-Philippe Bossuat Switzerland	5	114 0.9×	20 0.3×	5 0.2×	13 0.6×	3 0.6×	7	131
Victor Mitrana Spain	7	91 0.7×	93 1.5×	25 1.0×	148 6.4×		23	175
Yuto Nakashima Japan	5	66 0.5×	35 0.6×	9 0.4×	42 1.8×		29	73
Markus L. Schmid Germany	6	72 0.5×	61 1.0×	4 0.2×	25 1.1×		34	98
Nicoletta Cocco Italy	7	100 0.8×	98 1.6×	2 0.1×	63 2.7×	4 0.8×	18	183
Will Smart New Zealand	5	89 0.7×	13 0.2×	3 0.1×	20 0.9×	5 1.0×	6	101
Soonho Kong United States	5	27 0.2×	33 0.5×	4 0.2×	9 0.4×	4 0.8×	10	60
Yurii Rogozhin Moldova	9	97 0.7×	165 2.6×	10 0.4×	252 11.0×	1 0.2×	37	306

Countries citing papers authored by Philippe Collard

Since Specialization

Citations

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

Fields of papers citing papers by Philippe Collard

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philippe Collard

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

All Works

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11 of 11 papers shown

1.

Vanneschi, Leonardo, Yuri Pirola, Giancarlo Mauri, et al.. (2011). A study of the neutrality of Boolean function landscapes in genetic programming. Theoretical Computer Science. 425. 34–57. 7 indexed citations

2.

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

3.

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

4.

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

5.

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

6.

Collard, Philippe, et al.. (2002). Using a double-based genetic algorithm on a population of computer programs. 1. 418–424. 5 indexed citations

7.

Collard, Philippe, et al.. (2002). Time dependent optimization with a folding genetic algorithm. 125–132. 11 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.. (2002). Genetic operators in a dual genetic algorithm. 12–19. 15 indexed citations

10.

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

11.

Collard, Philippe, et al.. (1997). An Evolutionary Approach for Time Dependent Optimization. International Journal of Artificial Intelligence Tools. 6(4). 665–695. 13 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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