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Journal Articles
- 2024
[j4]Bogdan Burlacu
, Kaifeng Yang, Michael Affenzeller:
Population diversity and inheritance in genetic programming for symbolic regression. Nat. Comput. 23(3): 531-566 (2024)- 2023
- [j3]Christian Haider, Fabrício Olivetti de França, Bogdan Burlacu, Gabriel Kronberger:
Shape-constrained multi-objective genetic programming for symbolic regression. Appl. Soft Comput. 132: 109855 (2023) - 2022
- [j2]Gabriel Kronberger, Fabrício Olivetti de França, Bogdan Burlacu, Christian Haider, Michael Kommenda:
Shape-Constrained Symbolic Regression - Improving Extrapolation with Prior Knowledge. Evol. Comput. 30(1): 75-98 (2022) - 2020
- [j1]Michael Kommenda, Bogdan Burlacu, Gabriel Kronberger, Michael Affenzeller:
Parameter identification for symbolic regression using nonlinear least squares. Genet. Program. Evolvable Mach. 21(3): 471-501 (2020)
Conference and Workshop Papers
- 2024
- [c33]Bogdan Burlacu:
Backend-agnostic Tree Evaluation for Genetic Programming. GECCO Companion 2024: 1649-1657 - [c32]Etienne Russeil, Fabrício Olivetti de França, Konstantin L. Malanchev, Bogdan Burlacu, Emille E. O. Ishida, Marion Leroux, Clément Michelin, Guillaume Moinard, Emmanuel Gangler:
Multiview Symbolic Regression. GECCO 2024 - [c31]Christopher Crary, Bogdan Burlacu, Wolfgang Banzhaf:
Enhancing the Computational Efficiency of Genetic Programming Through Alternative Floating-Point Primitives. PPSN (1) 2024: 322-339 - 2023
- [c30]Bogdan Burlacu:
GECCO'2022 Symbolic Regression Competition: Post-Analysis of the Operon Framework. GECCO Companion 2023: 2412-2419 - [c29]Christian Haider, Fabrício Olivetti de França, Bogdan Burlacu, Florian Bachinger, Gabriel Kronberger, Michael Affenzeller:
Shape-constrained Symbolic Regression: Real-World Applications in Magnetization, Extrusion and Data Validation. GPTP 2023: 225-240 - 2022
- [c28]Christian Haider, Fabrício Olivetti de França, Gabriel Kronberger, Bogdan Burlacu:
Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression. GECCO 2022: 938-945 - [c27]Bogdan Burlacu, Michael Kommenda, Gabriel Kronberger, Stephan M. Winkler, Michael Affenzeller:
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data. GPTP 2022: 1-30 - 2021
- [c26]William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore:
Contemporary Symbolic Regression Methods and their Relative Performance. NeurIPS Datasets and Benchmarks 2021 - 2020
- [c25]Bogdan Burlacu, Gabriel Kronberger, Michael Kommenda:
Operon C++: an efficient genetic programming framework for symbolic regression. GECCO Companion 2020: 1562-1570 - 2019
- [c24]Bogdan Burlacu, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda:
Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure. CEC 2019: 2175-2182 - [c23]Michael Affenzeller, Bogdan Burlacu, Viktoria Dorfer, Sebastian Dorl, Gerhard Halmerbauer, Tilman Königswieser, Michael Kommenda, Julia Vetter, Stephan M. Winkler:
White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems. EUROCAST (1) 2019: 288-295 - [c22]Bogdan Burlacu, Lukas Kammerer, Michael Affenzeller, Gabriel Kronberger:
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression. EUROCAST (1) 2019: 361-369 - [c21]Mariia Semenkina, Bogdan Burlacu, Michael Affenzeller:
Genetic Programming Based Evolvement of Models of Models. EUROCAST (1) 2019: 387-395 - [c20]Bogdan Burlacu, Gabriel Kronberger, Michael Kommenda, Michael Affenzeller:
Parsimony measures in multi-objective genetic programming for symbolic regression. GECCO (Companion) 2019: 338-339 - [c19]Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller:
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication. GPTP 2019: 79-99 - 2018
- [c18]Bogdan Burlacu, Michael Affenzeller:
Schema-based diversification in genetic programming. GECCO 2018: 1111-1118 - [c17]Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller:
Cluster Analysis of a Symbolic Regression Search Space. GPTP 2018: 85-102 - 2017
- [c16]Gabriel Kronberger, Bogdan Burlacu, Michael Kommenda, Stephan M. Winkler, Michael Affenzeller:
Measures for the Evaluation and Comparison of Graphical Model Structures. EUROCAST (1) 2017: 283-290 - [c15]Michael Kommenda, Johannes Karder, Andreas Beham, Bogdan Burlacu, Gabriel Kronberger, Stefan Wagner, Michael Affenzeller:
Optimization Networks for Integrated Machine Learning. EUROCAST (1) 2017: 392-399 - [c14]Michael Affenzeller, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Gabriel Kronberger, Stefan Wagner:
Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals. EUROCAST (1) 2017: 424-431 - [c13]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda, Gabriel Kronberger, Stephan M. Winkler:
Analysis of Schema Frequencies in Genetic Programming. EUROCAST (1) 2017: 432-438 - [c12]Michael Affenzeller, Stephan M. Winkler, Bogdan Burlacu, Gabriel Kronberger, Michael Kommenda, Stefan Wagner:
Dynamic observation of genotypic and phenotypic diversity for different symbolic regression GP variants. GECCO (Companion) 2017: 1553-1558 - [c11]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda, Gabriel Kronberger, Stephan M. Winkler:
Schema Analysis in Tree-Based Genetic Programming. GPTP 2017: 17-37 - 2016
- [c10]Stephan M. Winkler, Michael Affenzeller, Bogdan Burlacu, Gabriel Kronberger, Michael Kommenda, Philipp Fleck:
Similarity-Based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression. GPTP 2016: 1-17 - 2015
- [c9]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda:
On the Effectiveness of Genetic Operations in Symbolic Regression. EUROCAST 2015: 367-374 - [c8]Michael Kommenda, Gabriel Kronberger, Michael Affenzeller, Stephan M. Winkler, Bogdan Burlacu:
Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming. GPTP 2015: 1-19 - 2014
- [c7]Michael Kommenda, Michael Affenzeller, Bogdan Burlacu, Gabriel Kronberger, Stephan M. Winkler:
Genetic programming with data migration for symbolic regression. GECCO (Companion) 2014: 1361-1366 - [c6]Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, Stefan Wagner:
Sliding Window Symbolic Regression for Detecting Changes of System Dynamics. GPTP 2014: 91-107 - 2013
- [c5]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda:
On the Evolutionary Behavior of Genetic Programming with Constants Optimization. EUROCAST (1) 2013: 284-291 - [c4]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda, Stephan M. Winkler, Gabriel Kronberger:
Visualization of genetic lineages and inheritance information in genetic programming. GECCO (Companion) 2013: 1351-1358 - [c3]Michael Affenzeller, Stephan M. Winkler, Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, Stefan Wagner:
Gaining Deeper Insights in Symbolic Regression. GPTP 2013: 175-190 - 2011
- [c2]Lavinia Ferariu, Bogdan Burlacu:
Multiobjective genetic programming with adaptive clustering. ICCP 2011: 27-32 - [c1]Lavinia Ferariu, Bogdan Burlacu:
Multiobjective design of evolutionary hybrid neural networks. ICAC 2011: 195-200
Parts in Books or Collections
- 2015
- [p2]Bogdan Burlacu, Michael Affenzeller, Stephan M. Winkler, Michael Kommenda, Gabriel Kronberger:
Methods for Genealogy and Building Block Analysis in Genetic Programming. Computational Intelligence and Efficiency in Engineering Systems 2015: 61-74 - [p1]Michael Kommenda, Michael Affenzeller, Gabriel Kronberger, Bogdan Burlacu, Stephan M. Winkler:
Multi-Population Genetic Programming with Data Migration for Symbolic Regression. Computational Intelligence and Efficiency in Engineering Systems 2015: 75-87
Informal and Other Publications
- 2024
- [i14]Etienne Russeil, Fabrício Olivetti de França, Konstantin L. Malanchev, Bogdan Burlacu, Emille E. O. Ishida, Marion Leroux, Clément Michelin, Guillaume Moinard, Emmanuel Gangler:
Multi-View Symbolic Regression. CoRR abs/2402.04298 (2024) - 2023
- [i13]Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava:
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023) - [i12]Deaglan J. Bartlett, Lukas Kammerer, Gabriel Kronberger, Harry Desmond, Pedro G. Ferreira, Benjamin D. Wandelt, Bogdan Burlacu, David Alonso, Matteo Zennaro:
A precise symbolic emulator of the linear matter power spectrum. CoRR abs/2311.15865 (2023) - 2022
- [i11]Bogdan Burlacu:
Rank-based Non-dominated Sorting. CoRR abs/2203.13654 (2022) - [i10]Bogdan Burlacu, Michael Kommenda, Gabriel Kronberger, Stephan M. Winkler, Michael Affenzeller:
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data. CoRR abs/2206.06422 (2022) - 2021
- [i9]Gabriel Kronberger, Fabrício Olivetti de França, Bogdan Burlacu, Christian Haider, Michael Kommenda:
Shape-constrained Symbolic Regression - Improving Extrapolation with Prior Knowledge. CoRR abs/2103.15624 (2021) - [i8]Christian Haider, Fabrício Olivetti de França, Bogdan Burlacu, Gabriel Kronberger:
Using Shape Constraints for Improving Symbolic Regression Models. CoRR abs/2107.09458 (2021) - [i7]Bogdan Burlacu, Lukas Kammerer, Michael Affenzeller, Gabriel Kronberger:
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression. CoRR abs/2107.10640 (2021) - [i6]William G. La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore:
Contemporary Symbolic Regression Methods and their Relative Performance. CoRR abs/2107.14351 (2021) - [i5]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda:
On the Effectiveness of Genetic Operations in Symbolic Regression. CoRR abs/2108.10661 (2021) - [i4]Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller:
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication. CoRR abs/2109.13895 (2021) - [i3]Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller:
Cluster Analysis of a Symbolic Regression Search Space. CoRR abs/2109.13898 (2021) - [i2]Michael Kommenda, Johannes Karder, Andreas Beham, Bogdan Burlacu, Gabriel Kronberger, Stefan Wagner, Michael Affenzeller:
Optimization Networks for Integrated Machine Learning. CoRR abs/2110.00415 (2021) - 2019
- [i1]Bogdan Burlacu, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda:
Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure. CoRR abs/1902.00882 (2019)
Coauthor Index
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