Sushil J. Louis

3.1k total citations
135 papers, 1.9k citations indexed

About

Sushil J. Louis is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Sociology and Political Science. According to data from OpenAlex, Sushil J. Louis has authored 135 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Artificial Intelligence, 33 papers in Computer Vision and Pattern Recognition and 15 papers in Sociology and Political Science. Recurrent topics in Sushil J. Louis's work include Artificial Intelligence in Games (40 papers), Evolutionary Algorithms and Applications (34 papers) and Metaheuristic Optimization Algorithms Research (22 papers). Sushil J. Louis is often cited by papers focused on Artificial Intelligence in Games (40 papers), Evolutionary Algorithms and Applications (34 papers) and Metaheuristic Optimization Algorithms Research (22 papers). Sushil J. Louis collaborates with scholars based in United States, Australia and Japan. Sushil J. Louis's co-authors include John R. McDonnell, Christopher E. Miles, George Bebis, Sergiu M. Dascalu, Gregory J. E. Rawlins, Chris Miles, Xiaojing Yuan, Zehang Sun, Hung Manh La and Juan C. Quiroz and has published in prestigious journals such as Physical Review Letters, Information Sciences and IEEE Transactions on Evolutionary Computation.

In The Last Decade

Sushil J. Louis

131 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sushil J. Louis United States 24 990 436 221 197 147 135 1.9k
Sylvain Gelly France 17 1.2k 1.2× 591 1.4× 235 1.1× 67 0.3× 86 0.6× 46 1.7k
Kumar Chellapilla United States 23 1.2k 1.3× 544 1.2× 175 0.8× 209 1.1× 210 1.4× 60 2.3k
Wojciech Marian Czarnecki Poland 15 827 0.8× 285 0.7× 78 0.4× 165 0.8× 161 1.1× 36 1.5k
David Duke United Kingdom 18 363 0.4× 554 1.3× 115 0.5× 185 0.9× 146 1.0× 81 1.6k
David Chapman United States 14 1.3k 1.3× 339 0.8× 60 0.3× 213 1.1× 356 2.4× 45 2.0k
G. Tesauro United States 15 1.1k 1.1× 230 0.5× 64 0.3× 129 0.7× 697 4.7× 22 2.2k
Olivier Teytaud France 19 843 0.9× 116 0.3× 164 0.7× 205 1.0× 69 0.5× 101 1.2k
Vladimir Estivill‐Castro Australia 19 791 0.8× 244 0.6× 99 0.4× 254 1.3× 224 1.5× 122 1.7k
Moshe Sipper Israel 26 1.5k 1.5× 232 0.5× 102 0.5× 952 4.8× 250 1.7× 143 2.6k
Michael Georgiopoulos United States 25 1.2k 1.3× 547 1.3× 39 0.2× 175 0.9× 224 1.5× 213 3.0k

Countries citing papers authored by Sushil J. Louis

Since Specialization
Citations

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

Fields of papers citing papers by Sushil J. Louis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sushil J. Louis. 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 Sushil J. Louis. The network helps show where Sushil J. Louis may publish in the future.

Co-authorship network of co-authors of Sushil J. Louis

This figure shows the co-authorship network connecting the top 25 collaborators of Sushil J. Louis. A scholar is included among the top collaborators of Sushil J. Louis 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 Sushil J. Louis. Sushil J. Louis 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.
Louis, Sushil J., et al.. (2022). Transfer Learning-based Hybrid Approach for Bayesian Network Structure Learning. International Journal of Artificial Intelligence Tools. 31(7). 2 indexed citations
2.
Louis, Sushil J., et al.. (2022). Routing for bridge inspecting robots using a metaheuristic genetic algorithm. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 703–706. 1 indexed citations
3.
Aksoy, Ahmet, Sushil J. Louis, & Mehmet Hadi Güneş. (2017). Operating system fingerprinting via automated network traffic analysis. 2502–2509. 19 indexed citations
4.
Nagayama, Taisuke, Roberto Mancini, Sushil J. Louis, et al.. (2006). Multiobjective method for fitting pinhole image intensity profiles of implosion cores driven by a Pareto genetic algorithm. Review of Scientific Instruments. 77(10). 6 indexed citations
5.
Louis, Sushil J. & Chris Miles. (2005). Combining Case-Based Memory with Genetic Algorithm Search for Competent Game AI.. 193–205. 10 indexed citations
6.
Louis, Sushil J., et al.. (2005). Better Personalization Using Learning Classifier Systems.. Indian International Conference on Artificial Intelligence. 229(Pt B). 2573–2588. 6 indexed citations
7.
Miles, Chris & Sushil J. Louis. (2005). Case-Injection Improves Response Time for a Real-Time Strategy Game.. 12(6). 4931–4944. 9 indexed citations
8.
Louis, Sushil J.. (2004). Genetic learning from experience. 3. 2118–2125. 4 indexed citations
9.
Louis, Sushil J., et al.. (2002). Evolution of Complex Behavior Controllers using Genetic Algorithms.. International Conference on Artificial Intelligence. 902–908. 1 indexed citations
10.
McDonnell, John R., et al.. (2002). Strike Force Asset Allocation using Genetic Search.. International Conference on Artificial Intelligence. 897–901. 7 indexed citations
11.
Louis, Sushil J., et al.. (1999). Interactive genetic algorithms for the traveling salesman problem. Genetic and Evolutionary Computation Conference. 385–392. 16 indexed citations
12.
Louis, Sushil J., et al.. (1999). A sequential similarity metric for case injected genetic algorithms applied to TSPs. Genetic and Evolutionary Computation Conference. 377–384. 6 indexed citations
13.
Golovkin, I., Roberto Mancini, & Sushil J. Louis. (1999). Plasma X-ray spectra analysis using genetic algorithms. Genetic and Evolutionary Computation Conference. 1529–1534. 3 indexed citations
14.
Louis, Sushil J. & J.M. Johnson. (1999). Robustness of Case-Initialized Genetic Algorithms. The Florida AI Research Society. 129–133. 3 indexed citations
15.
Louis, Sushil J., et al.. (1998). An Empirical Comparison of Randomized Algorithms for Large Join Query Optimization. The Florida AI Research Society. 95–100. 1 indexed citations
16.
Zeng, Xiaogang, et al.. (1997). Genetic Algorithms for Inverse Problem Solutions. Computing in Civil Engineering. 725–732. 8 indexed citations
17.
Louis, Sushil J. & J.M. Johnson. (1997). Solving Similar Problems Using Genetic Algorithms and Case-Based Memory.. 283–290. 31 indexed citations
18.
Louis, Sushil J. & Gregory J. E. Rawlins. (1993). Pareto OptimalityGA-Easiness and Deception (Extended Abstract). international conference on Genetic algorithms. 118–123. 18 indexed citations
19.
Louis, Sushil J., et al.. (1993). Case-based reasoning assisted explanation of genetic algorithm results. Journal of Experimental & Theoretical Artificial Intelligence. 5(1). 21–37. 20 indexed citations
20.
Louis, Sushil J. & Gregory J. E. Rawlins. (1991). Designer Genetic Algorithms: Genetic Algorithms in Structure Design.. 53–60. 73 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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