Bogdan Burlacu

763 total citations
23 papers, 258 citations indexed

About

Bogdan Burlacu is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Bogdan Burlacu has authored 23 papers receiving a total of 258 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 6 papers in Computational Theory and Mathematics and 4 papers in Molecular Biology. Recurrent topics in Bogdan Burlacu's work include Evolutionary Algorithms and Applications (18 papers), Metaheuristic Optimization Algorithms Research (16 papers) and Advanced Multi-Objective Optimization Algorithms (6 papers). Bogdan Burlacu is often cited by papers focused on Evolutionary Algorithms and Applications (18 papers), Metaheuristic Optimization Algorithms Research (16 papers) and Advanced Multi-Objective Optimization Algorithms (6 papers). Bogdan Burlacu collaborates with scholars based in Austria, Brazil and France. Bogdan Burlacu's co-authors include Gabriel Kronberger, Michael Kommenda, Michael Affenzeller, Fabrício Olivetti de França, Stephan Winkler, Mihail L. Sichitiu, Kaifeng Yang, Pedro G. Ferreira, Sami Rizkalla and Rudra Dutta and has published in prestigious journals such as Astronomy and Astrophysics, Applied Soft Computing and Evolutionary Computation.

In The Last Decade

Bogdan Burlacu

22 papers receiving 255 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bogdan Burlacu Austria 8 173 45 38 31 26 23 258
George Papamakarios United Kingdom 5 55 0.3× 8 0.2× 11 0.3× 10 0.3× 18 0.7× 9 185
Zhang Shi-jun China 6 80 0.5× 15 0.3× 8 0.2× 51 1.6× 23 215
Roger C. Alperin United States 13 21 0.1× 54 1.2× 7 0.2× 14 0.5× 5 0.2× 51 380
Miguel Cárdenas‐Montes Spain 7 51 0.3× 26 0.6× 7 0.2× 8 0.3× 13 0.5× 36 136
Laurent Carraro France 9 32 0.2× 50 1.1× 7 0.2× 11 0.4× 15 185
Chao Pang China 9 24 0.1× 67 1.5× 99 2.6× 13 0.4× 15 279
Keisuke Yamazaki Japan 8 192 1.1× 16 0.4× 12 0.3× 11 0.4× 36 234
Luke Metz United States 5 104 0.6× 10 0.2× 6 0.2× 20 0.6× 2 0.1× 12 223
Qian Wan China 10 118 0.7× 4 0.1× 23 0.6× 62 2.0× 29 250

Countries citing papers authored by Bogdan Burlacu

Since Specialization
Citations

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

Fields of papers citing papers by Bogdan Burlacu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bogdan Burlacu

This figure shows the co-authorship network connecting the top 25 collaborators of Bogdan Burlacu. A scholar is included among the top collaborators of Bogdan Burlacu 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 Bogdan Burlacu. Bogdan Burlacu 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.
Bomarito, Geoffrey, et al.. (2025). Call for Action: towards the next generation of symbolic regression benchmark. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2529–2538.
2.
Bartlett, Deaglan J., Gabriel Kronberger, Harry Desmond, et al.. (2024). A precise symbolic emulator of the linear matter power spectrum. Astronomy and Astrophysics. 686. A209–A209. 8 indexed citations
3.
França, Fabrício Olivetti de, Konstantin Malanchev, Bogdan Burlacu, et al.. (2024). Multiview Symbolic Regression. Proceedings of the Genetic and Evolutionary Computation Conference. 961–970. 2 indexed citations
4.
Kronberger, Gabriel, Bogdan Burlacu, Michael Kommenda, Stephan Winkler, & Michael Affenzeller. (2024). Symbolic Regression. 14 indexed citations
5.
Burlacu, Bogdan, Kaifeng Yang, & Michael Affenzeller. (2023). Population diversity and inheritance in genetic programming for symbolic regression. Natural Computing. 23(3). 531–566. 4 indexed citations
6.
Burlacu, Bogdan. (2023). GECCO'2022 Symbolic Regression Competition: Post-Analysis of the Operon Framework. 2412–2419. 7 indexed citations
7.
França, Fabrício Olivetti de, et al.. (2022). Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression. Proceedings of the Genetic and Evolutionary Computation Conference. 938–945. 1 indexed citations
8.
França, Fabrício Olivetti de, et al.. (2022). Shape-constrained multi-objective genetic programming for symbolic regression. Applied Soft Computing. 132. 109855–109855. 16 indexed citations
9.
Kronberger, Gabriel, et al.. (2021). Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge. Evolutionary Computation. 30(1). 75–98. 34 indexed citations
10.
Burlacu, Bogdan, Gabriel Kronberger, Michael Kommenda, & Michael Affenzeller. (2019). Parsimony measures in multi-objective genetic programming for symbolic regression. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 338–339. 7 indexed citations
11.
Kommenda, Michael, Bogdan Burlacu, Gabriel Kronberger, & Michael Affenzeller. (2019). Parameter identification for symbolic regression using nonlinear least squares. Genetic Programming and Evolvable Machines. 21(3). 471–501. 66 indexed citations
12.
Burlacu, Bogdan & Michael Affenzeller. (2018). Schema-based diversification in genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference. 2. 1111–1118. 1 indexed citations
13.
Affenzeller, Michael, Stephan Winkler, Bogdan Burlacu, et al.. (2017). Dynamic observation of genotypic and phenotypic diversity for different symbolic regression GP variants. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1553–1558. 6 indexed citations
14.
Burlacu, Bogdan, Michael Kommenda, & Michael Affenzeller. (2015). Building Blocks Identification Based on Subtree Sample Counts for Genetic Programming. 152–157. 3 indexed citations
15.
Kommenda, Michael, et al.. (2015). Heat treatment process parameter estimation using heuristic optimization algorithms. 222–227. 1 indexed citations
16.
Kommenda, Michael, Michael Affenzeller, Bogdan Burlacu, Gabriel Kronberger, & Stephan Winkler. (2014). Genetic programming with data migration for symbolic regression. 1361–1366. 5 indexed citations
17.
Burlacu, Bogdan, Michael Affenzeller, Michael Kommenda, Stephan Winkler, & Gabriel Kronberger. (2013). Visualization of genetic lineages and inheritance information in genetic programming. 1351–1358. 13 indexed citations
18.
Wagner, Ştefan, et al.. (2012). On the analysis, classification and prediction of metaheuristic algorithm behavior for combinatorial optimization problems. 368–372. 1 indexed citations
19.
Burlacu, Bogdan, et al.. (2011). Multiobjective design of evolutionary hybrid neural networks. 195–200. 3 indexed citations
20.
Burlacu, Bogdan, et al.. (2011). Multiobjective genetic programming with adaptive clustering. 55. 27–32. 1 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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