C. Haider, F.O. de Franca, B. Burlacu & G. Kronberger - Shape-constrained multi-objective genetic programming for symbolic regression - Applied Soft Computing, Volume 132, 2023
Kammerer, G. Kronberger & M. Kommenda - Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization - Proceedings of the 18th International Conference on Computer Aided Systems Theory - EUROCAST 2022, Las Palmas de Gran Canaria, Spain (2022)
D. Jödicke, D. Parra, G. Kronberger & S.M. Winkler - Identifying Differential Equations to predict Blood Glucose using Sparse Identification of Nonlinear Systems. - Proceedings of the 18th International Conference on Computer Aided Systems Theory - EUROCAST 2022, Las Palmas de Gran Canaria, Spain (2022)
C. Haider - Shape-constrained Symbolic Regression with NSGA-III. - Proceedings of the 18th International Conference on Computer Aided Systems Theory - EUROCAST 2022, Las Palmas de Gran Canaria, Spain (2022)
F. Bachinger - Comparing Shape-Constrained Regression Algorithms for Data Validation - Proceedings of the 18th International Conference on Computer Aided Systems Theory - EUROCAST 2022, Las Palmas de Gran Canaria, Spain (2022)
G. Kronberger, E. Kabliman, J. Kronsteiner & M. Kommenda - Extending a physics-based constitutive model using genetic programming - Applications in Engineering Science, Vol. 9
F. Bachinger, G. Kronberger & M. Affenzeller - Continuous improvement and adaptation of predictive models in smart manufacturing and model management - IET Collaborative Intelligent Manufacturing, Vol. 3, Iss. 1, Special Issue: Selected Papers from Collaborative and Intelligent Manufacturing in Industry 4.0 (ISM @SMM 2019), pp. 48-63, (March 2021)
L. Millán, G. Kronberger, J. I. Hidalgo, R. Fernández, O. Garnica & G. González-Doncel - Estimation of Grain-Level Residual Stresses in a Quenched Cylindrical Sample of Aluminum Alloy AA5083 Using Genetic Programming - Applications of Evolutionary Computation (Conference Proceedings EvoApplications 2021), Vol. 12694, pp. 421.436, (2021)
G. Kronberger, F. O. de Franca, B. Burlacu, C. Haider & M. Kommenda - Shape-constrained Symbolic Regression – Improving Extrapolation with Prior Knowledge - Evolutionary Computation (2021)
E. Kabliman, A. H. Kolody, J. Kronsteiner, M. Kommenda & G. Kronberger - Application of symbolic regression for constitutive modeling of plastic deformation - Applications in Engineering Science, Volume 6, 100052, Elsevier. (June 2021)
L. Millán, G. Bokuchava, J. I. Hidalgo, R. Fernández, G. Kronberger, P. Halodova, A. Sáez, I. Papushkin, O. Garnica, J. Lanchares & G. González-Doncel - Study of Microscopic Residual Stresses in an Extruded Aluminium Alloy Sample after Thermal Treatmen - Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques. 15, pp. 763–767 (2021)
W. Roland, C. Marschik, M. Kommenda, A. Haghofer, S. Dorl & S. Winkler - Predicting the Non-Linear Conveying Behavior in Single-Screw Extrusion: A Comparison of Various Data-Based Modeling Approaches used with CFD Simulations - International Polymer Processing. Vol. 36, Issue 5, pp. 529-544 (2021)
W. La Cava, P. Orzechowski, B. Burlacu, F. Olivetti de França, M. Virgolin, Y. Jin, M. Kommenda & J. H. Moore - Contemporary symbolic regression methods and their relative performance - arXiv preprint
C. Haider, F. O. de França, B. Burlacu & G. Kronberger - Using Shape Constraints for Improving Symbolic Regression Models - arXiv preprint
B. Burlacu, G. Kronberger & M. Kommenda - Operon C++: an efficient genetic programming framework for symbolic regression - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (GECCO ’20), pp. 1562–1570, (July 2020)
G. Kronberger, J. M. Colmenar, S. M. Winkler & J. I. Hidalgo - Multilayer analysis of population diversity in grammatical evolution for symbolic regression - Soft Computing. Springer. (2020)
J. Zenisek, G. Kronberger, J. Wolfartsberger, N. Wild & M. Affenzeller - Concept Drift Detection with Variable Interaction Networks - Computer Aided Systems Theory - EUROCAST 2019, pp. 296-303. Springer. (2020)
L. Kammerer, G. Kronberger & M. Kommenda - Data Aggregation for Reducing Training Data in Symbolic Regression - Computer Aided Systems Theory - EUROCAST 2019, pp. 378-386. Springer. (2020)
B. Burlacu, L. Kammerer, M. Affenzeller & G. Kronberger - Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression - Computer Aided Systems Theory - EUROCAST 2019, pp. 361-369. Springer. (2020)
G. Kronberger, L. Kammerer & M. Kommenda - Identification of Dynamical Systems Using Symbolic Regression - Computer Aided Systems Theory - EUROCAST 2019, pp. 370-377. Springer. (2020)
Michael Affenzeller, Bogdan Burlacu, Viktoria Dorfer, Sebastian Dorl, Gerhard Halmerbauer, Tilman Königswieser, Michael Kommenda, Julia Vetter & Stephan Winkler - White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems - Computer Aided Systems Theory - EUROCAST 2019, pp. 288-295. Springer. (2020)
F. Bachinger & G. Kronberger - Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications - Computer Aided Systems Theory - EUROCAST 2019, pp. 263-270. Springer. (2020)
L. Kammerer, G. Kronberger, B. Burlacu, S. M. Winkler, M. Kommenda & M. Affenzeller - Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication - Genetic Programming Theory and Practice XVII., pp. 79-99. Springer. (2020)
G. Kronberger, F. Bachinger & M. Affenzeller - Smart Manufacturing and Continuous Improvement and Adaptation of Predictive Models - Procedia Manufacturing, Volume 42. (2020). Part of special issue: International Conference on Industry 4.0 and Smart Manufacturing (ISM 2019)