Projects per year
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Dive into the research topics where Bogdan Burlacu is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Projects
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Josef Ressel Zentrum für Symbolische Regression
Kronberger, G., Kommenda, M., Burlacu, B., Holzleitner, E., Haider, C., Kammerer, L., Bachinger, F. & Jödicke, D.
01.01.2018 → 31.12.2022
Project: Research Project
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HEAL - Virtual Sensor Design by Genetic Programming
Affenzeller, M., Leitner, S. J., Burlacu, B. & Holzleitner, E.
01.02.2006 → 31.01.2030
Project: Research Project
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Fakultätsübergreifendes Institut für intelligente Produktion - Hgbg
Affenzeller, M., Beham, A., Burlacu, B., Fleck, P., Traugott, G., Holzleitner, E. & Holzinger, F. C.
01.07.2014 → 30.06.2019
Project: Research Project
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K-Projekt HOPL
Affenzeller, M., Wagner, S., Lengauer, E., Beham, A., Leitner, S. J., Karder, J. A., Petschnik, E., Kommenda, M., Kellermayr-Scheucher, M., Burlacu, B., Krauss, O., Holzleitner, E., Hauder, V. & Hübl, A.
01.05.2014 → 30.04.2018
Project: Research Project
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Research output
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Genetic Programming Based Evolvement of Models of Models
Semenkina, M., Burlacu, B. & Affenzeller, M., 2020, Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Moreno-Díaz, R., Quesada-Arencibia, A. & Pichler, F. (eds.). Springer, p. 387-395 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12013 LNCS).Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review
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Models set pre-processing for genetic programming based evolvement of models of models
Semenkina, M., Burlacu, B., Affenzeller, M. & Pitzer, E., 29 Jan 2020, In: IOP Conference Series: Materials Science and Engineering. 734, 1, 012108.Research output: Contribution to journal › Conference article › peer-review
Open Access -
Operon C++: An efficient genetic programming framework for symbolic regression
Burlacu, B., Kronberger, G. & Kommenda, M., 8 Jul 2020, GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, p. 1562-1570 9 p. (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion).Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review
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Parameter identification for symbolic regression using nonlinear least squares
Kommenda, M., Burlacu, B., Kronberger, G. & Affenzeller, M., 1 Sep 2020, In: Genetic Programming and Evolvable Machines. 21, 3, p. 471-501 31 p.Research output: Contribution to journal › Article › peer-review
Open Access -
White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems
Affenzeller, M., Burlacu, B., Dorfer, V., Dorl, S., Halmerbauer, G., Königswieser, T., Kommenda, M., Vetter, J. & Winkler, S., 2020, Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Moreno-Díaz, R., Quesada-Arencibia, A. & Pichler, F. (eds.). Springer, p. 288-295 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12013 LNCS).Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review