Tea Tušar

1.4k total citations
48 papers, 623 citations indexed

About

Tea Tušar is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Tea Tušar has authored 48 papers receiving a total of 623 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computational Theory and Mathematics, 28 papers in Artificial Intelligence and 7 papers in Control and Systems Engineering. Recurrent topics in Tea Tušar's work include Advanced Multi-Objective Optimization Algorithms (30 papers), Metaheuristic Optimization Algorithms Research (20 papers) and Evolutionary Algorithms and Applications (13 papers). Tea Tušar is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (30 papers), Metaheuristic Optimization Algorithms Research (20 papers) and Evolutionary Algorithms and Applications (13 papers). Tea Tušar collaborates with scholars based in Slovenia, France and Germany. Tea Tušar's co-authors include Bogdan Filipič, Dimo Brockhoff, Nikolaus Hansen, Erik Dovgan, Matjaž Gams, Peter Korošec, Gregor Papa, Jurij Šilc, Eric Medvet and Erkki K. Laitinen and has published in prestigious journals such as European Journal of Operational Research, Expert Systems with Applications and Information Sciences.

In The Last Decade

Tea Tušar

46 papers receiving 610 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tea Tušar Slovenia 14 306 285 92 85 85 48 623
Sebastian Schmitt Germany 10 202 0.7× 251 0.9× 67 0.7× 80 0.9× 116 1.4× 28 648
Surafel Luleseged Tilahun South Africa 13 145 0.5× 333 1.2× 98 1.1× 42 0.5× 65 0.8× 37 633
Jarosław Arabas Poland 12 339 1.1× 587 2.1× 111 1.2× 41 0.5× 121 1.4× 48 911
Beatrice Ombuki-Berman Canada 14 257 0.8× 398 1.4× 116 1.3× 44 0.5× 78 0.9× 55 772
Tatsuya Okabe Germany 8 340 1.1× 349 1.2× 80 0.9× 55 0.6× 60 0.7× 12 757
Giacomo Nannicini United States 14 144 0.5× 191 0.7× 75 0.8× 40 0.5× 69 0.8× 41 634
Hira Zaheer India 5 174 0.6× 324 1.1× 101 1.1× 29 0.3× 106 1.2× 6 669
S. Kobayashi Japan 11 494 1.6× 520 1.8× 97 1.1× 93 1.1× 75 0.9× 35 845
Jiao-Hong Yi China 9 222 0.7× 328 1.2× 67 0.7× 36 0.4× 89 1.0× 10 639
Myriam Delgado Brazil 19 244 0.8× 503 1.8× 128 1.4× 88 1.0× 49 0.6× 87 899

Countries citing papers authored by Tea Tušar

Since Specialization
Citations

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

Fields of papers citing papers by Tea Tušar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tea Tušar

This figure shows the co-authorship network connecting the top 25 collaborators of Tea Tušar. A scholar is included among the top collaborators of Tea Tušar 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 Tea Tušar. Tea Tušar 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.
Tušar, Tea, et al.. (2024). Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective. IEEE Transactions on Evolutionary Computation. 29(1). 275–285. 2 indexed citations
2.
Brockhoff, Dimo & Tea Tušar. (2023). GECCO 2023 Tutorial on Benchmarking Multiobjective Optimizers 2.0. 1183–1212. 1 indexed citations
3.
Janko, Vito, Tea Tušar, Anton Gradišek, et al.. (2023). Optimizing non-pharmaceutical intervention strategies against COVID-19 using artificial intelligence. Frontiers in Public Health. 11. 1073581–1073581. 5 indexed citations
4.
Volz, Vanessa, Boris Naujoks, Pascal Kerschke, & Tea Tušar. (2023). Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games. Applied Soft Computing. 136. 110121–110121. 3 indexed citations
5.
Hansen, Nikolaus, et al.. (2022). Anytime Performance Assessment in Blackbox Optimization Benchmarking. IEEE Transactions on Evolutionary Computation. 26(6). 1293–1305. 15 indexed citations
6.
Brockhoff, Dimo, et al.. (2020). Benchmarking large-scale continuous optimizers: The bbob-largescale testbed, a COCO software guide and beyond. Applied Soft Computing. 97. 106737–106737. 14 indexed citations
7.
Volz, Vanessa, Boris Naujoks, Pascal Kerschke, & Tea Tušar. (2019). Single- and multi-objective game-benchmark for evolutionary algorithms. Proceedings of the Genetic and Evolutionary Computation Conference. 647–655. 14 indexed citations
8.
Brockhoff, Dimo & Tea Tušar. (2019). Benchmarking algorithms from the platypus framework on the biobjective bbob-biobj testbed. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1905–1911. 8 indexed citations
9.
Medvet, Eric, Marco Virgolin, Mauro Castelli, et al.. (2018). Unveiling evolutionary algorithm representation with DU maps. Genetic Programming and Evolvable Machines. 19(3). 351–389. 9 indexed citations
10.
Filipič, Bogdan & Tea Tušar. (2018). A taxonomy of methods for visualizing pareto front approximations. Proceedings of the Genetic and Evolutionary Computation Conference. 649–656. 21 indexed citations
11.
Tušar, Tea, et al.. (2018). Comparing black-box differential evolution and classic differential evolution. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1537–1544. 4 indexed citations
12.
Tušar, Tea, et al.. (2017). A study of overfitting in optimization of a manufacturing quality control procedure. Applied Soft Computing. 59. 77–87. 21 indexed citations
13.
Brockhoff, Dimo, et al.. (2016). Using Well-Understood Single-Objective Functions in Multiobjective\n Black-Box Optimization Test Suites. arXiv (Cornell University). 17 indexed citations
14.
Auger, Anne, et al.. (2016). GECCO'16 Black-Box Optimization Benchmarking Workshop (BBOB-2016). 1167–1167. 2 indexed citations
15.
Filipič, Bogdan & Tea Tušar. (2016). Visualization in Multiobjective Optimization. Zenodo (CERN European Organization for Nuclear Research). 735–751. 6 indexed citations
16.
Tušar, Tea & Bogdan Filipič. (2014). Visualization of Pareto Front Approximations in Evolutionary Multiobjective Optimization: A Critical Review and the Prosection Method. IEEE Transactions on Evolutionary Computation. 19(2). 225–245. 175 indexed citations
17.
Tušar, Tea & Bogdan Filipič. (2014). Visualizing Exact and Approximated 3D Empirical Attainment Functions. Mathematical Problems in Engineering. 2014(1). 8 indexed citations
18.
19.
Filipič, Bogdan & Tea Tušar. (2013). Challenges of applying optimization methodology in industry. 1103–1104. 1 indexed citations
20.
Filipič, Bogdan, Tea Tušar, & Erkki K. Laitinen. (2007). Preliminary Numerical Experiments in Multiobjective Optimization of a Metallurgical Production Process. 31(2). 233–240. 14 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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