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2020 – today
- 2023
[c52]Philipp Fleck
, Stephan M. Winkler, Michael Kommenda, Sara Silva, Leonardo Vanneschi, Michael Affenzeller:
Evolutionary Algorithms for Segment Optimization in Vectorial GP. GECCO Companion 2023: 439-442- [i21]Philipp Fleck, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller:
Vectorial Genetic Programming - Optimizing Segments for Feature Extraction. CoRR abs/2303.03200 (2023) - [i20]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) - 2022
- [j7]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) - [c51]Lukas Kammerer, Gabriel Kronberger, Michael Kommenda:
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization. EUROCAST 2022: 139-146 - [c50]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 - [c49]Wolfgang Roland, Michael Kommenda, Gerald R. Berger-Weber:
Application of Symbolic Regression in Polymer Processing. SYNASC 2022: 311-318 - [i19]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) - [i18]Lukas Kammerer, Gabriel Kronberger, Michael Kommenda:
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization. CoRR abs/2209.09675 (2022) - 2021
- [c48]Philipp Fleck, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller:
Grammar-Based Vectorial Genetic Programming for Symbolic Regression. GPTP 2021: 21-43 - [c47]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 - [i17]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) - [i16]Gabriel Kronberger, Lukas Kammerer, Michael Kommenda:
Identification of Dynamical Systems using Symbolic Regression. CoRR abs/2107.06131 (2021) - [i15]Gabriel Kronberger, Michael Kommenda, Andreas Promberger, Falk Nickel:
Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables. CoRR abs/2107.09484 (2021) - [i14]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) - [i13]Gabriel Kronberger, Evgeniya Kabliman, Johannes Kronsteiner, Michael Kommenda:
Predicting Calibration Parameter Values for Constitutive Models using Genetic Programming. CoRR abs/2108.01595 (2021) - [i12]Lukas Kammerer, Gabriel Kronberger, Michael Kommenda:
Data Aggregation for Reducing Training Data in Symbolic Regression. CoRR abs/2108.10660 (2021) - [i11]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda:
On the Effectiveness of Genetic Operations in Symbolic Regression. CoRR abs/2108.10661 (2021) - [i10]Michael Kommenda, Andreas Beham, Michael Affenzeller, Gabriel Kronberger:
Complexity Measures for Multi-objective Symbolic Regression. CoRR abs/2109.00238 (2021) - [i9]Florian Holzinger, Michael Kommenda:
Preprocessing and Modeling of Radial Fan Data for Health State Prediction. CoRR abs/2109.03468 (2021) - [i8]Philipp Fleck, Manfred Kügel, Michael Kommenda:
Understanding and Preparing Data of Industrial Processes for Machine Learning Applications. CoRR abs/2109.03469 (2021) - [i7]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) - [i6]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) - [i5]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) - 2020
- [j6]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) - [c46]Bogdan Burlacu, Gabriel Kronberger, Michael Kommenda:
Operon C++: an efficient genetic programming framework for symbolic regression. GECCO Companion 2020: 1562-1570
2010 – 2019
- 2019
- [c45]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 - [c44]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 - [c43]Florian Holzinger, Michael Kommenda:
Preprocessing and Modeling of Radial Fan Data for Health State Prediction. EUROCAST (1) 2019: 312-318 - [c42]Gabriel Kronberger, Lukas Kammerer, Michael Kommenda:
Identification of Dynamical Systems Using Symbolic Regression. EUROCAST (1) 2019: 370-377 - [c41]Lukas Kammerer, Gabriel Kronberger, Michael Kommenda:
Data Aggregation for Reducing Training Data in Symbolic Regression. EUROCAST (1) 2019: 378-386 - [c40]Philipp Fleck, Manfred Kügel, Michael Kommenda:
Understanding and Preparing Data of Industrial Processes for Machine Learning Applications. EUROCAST (1) 2019: 413-420 - [c39]Bogdan Burlacu, Gabriel Kronberger, Michael Kommenda, Michael Affenzeller:
Parsimony measures in multi-objective genetic programming for symbolic regression. GECCO (Companion) 2019: 338-339 - [c38]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 - [i4]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) - 2018
- [j5]Gabriel Kronberger, Michael Kommenda, Edwin Lughofer, Susanne Saminger-Platz, Andreas Promberger, Falk Nickel, Stephan M. Winkler, Michael Affenzeller:
Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems. Appl. Soft Comput. 69: 610-624 (2018) - [j4]Philipp Fleck, Michael Kommenda, Thorsten Prante, Michael Affenzeller:
Novel robustness measures for engineering design optimisation. Int. J. Simul. Process. Model. 13(4): 387-401 (2018) - [c37]Gabriel Kronberger, Michael Kommenda, Andreas Promberger, Falk Nickel:
Predicting friction system performance with symbolic regression and genetic programming with factor variables. GECCO 2018: 1278-1285 - [c36]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
- [c35]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 - [c34]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 - [c33]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 - [c32]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda, Gabriel Kronberger, Stephan M. Winkler:
Analysis of Schema Frequencies in Genetic Programming. EUROCAST (1) 2017: 432-438 - [c31]Johannes Karder, Stefan Wagner, Andreas Beham, Michael Kommenda, Michael Affenzeller:
Towards the design and implementation of optimization networks in HeuristicLab. GECCO (Companion) 2017: 1209-1214 - [c30]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 - [c29]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda, Gabriel Kronberger, Stephan M. Winkler:
Schema Analysis in Tree-Based Genetic Programming. GPTP 2017: 17-37 - 2016
- [c28]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 - [c27]Edwin Lughofer, Gabriel Kronberger, Michael Kommenda, Susanne Saminger-Platz, Andreas Promberger, Falk Nickel, Stephan M. Winkler, Michael Affenzeller:
Robust Fuzzy Modeling and Symbolic Regression for Establishing Accurate and Interpretable Prediction Models in Supervising Tribological Systems. IJCCI (FCTA) 2016: 51-63 - 2015
- [c26]Stephan M. Winkler, Gabriel Kronberger, Michael Kommenda, Stefan Fink, Michael Affenzeller:
Dynamics of Predictability and Variable Influences Identified in Financial Data Using Sliding Window Machine Learning. EUROCAST 2015: 326-333 - [c25]Andreas Beham, Judith Fechter, Michael Kommenda, Stefan Wagner, Stephan M. Winkler, Michael Affenzeller:
Optimization Strategies for Integrated Knapsack and Traveling Salesman Problems. EUROCAST 2015: 359-366 - [c24]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda:
On the Effectiveness of Genetic Operations in Symbolic Regression. EUROCAST 2015: 367-374 - [c23]Michael Kommenda, Andreas Beham, Michael Affenzeller, Gabriel Kronberger:
Complexity Measures for Multi-objective Symbolic Regression. EUROCAST 2015: 409-416 - [c22]Gabriel Kronberger, Michael Kommenda, Stephan M. Winkler, Michael Affenzeller:
Using Contextual Information in Sequential Search for Grammatical Optimization Problems. EUROCAST 2015: 417-424 - [c21]Andreas Scheibenpflug, Andreas Beham, Michael Kommenda, Johannes Karder, Stefan Wagner, Michael Affenzeller:
Simplifying Problem Definitions in the HeuristicLab Optimization Environment. GECCO (Companion) 2015: 1101-1108 - [c20]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 - [p3]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 - [p2]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 - [p1]Gabriel Kronberger, Michael Kommenda:
Search Strategies for Grammatical Optimization Problems - Alternatives to Grammar-Guided Genetic Programming. Computational Intelligence and Efficiency in Engineering Systems 2015: 89-102 - 2014
- [j3]Gerd Bramerdorfer, Stephan M. Winkler, Michael Kommenda, Günther Weidenholzer, Siegfried Silber, Gabriel Kronberger, Michael Affenzeller, Wolfgang Amrhein:
Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs. IEEE Trans. Ind. Electron. 61(11): 6454-6462 (2014) - [c19]Andreas Beham, Johannes Karder, Gabriel Kronberger, Stefan Wagner, Michael Kommenda, Andreas Scheibenpflug:
Scripting and framework integration in heuristic optimization environments. GECCO (Companion) 2014: 1109-1116 - [c18]Michael Kommenda, Michael Affenzeller, Bogdan Burlacu, Gabriel Kronberger, Stephan M. Winkler:
Genetic programming with data migration for symbolic regression. GECCO (Companion) 2014: 1361-1366 - [c17]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
- [j2]Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, Stefan Wagner, Viktoria Dorfer, Witold Jacak, Herbert Stekel:
On the use of estimated tumour marker classifications in tumour diagnosis prediction - a case study for breast cancer. Int. J. Simul. Process. Model. 8(1): 29-41 (2013) - [c16]Stefan Vonolfen, Andreas Beham, Michael Kommenda, Michael Affenzeller:
Structural Synthesis of Dispatching Rules for Dynamic Dial-a-Ride Problems. EUROCAST (1) 2013: 276-283 - [c15]Bogdan Burlacu, Michael Affenzeller, Michael Kommenda:
On the Evolutionary Behavior of Genetic Programming with Constants Optimization. EUROCAST (1) 2013: 284-291 - [c14]Gabriel Kronberger, Michael Kommenda:
Evolution of Covariance Functions for Gaussian Process Regression Using Genetic Programming. EUROCAST (1) 2013: 308-315 - [c13]Michael Kommenda, Michael Affenzeller, Gabriel Kronberger, Stephan M. Winkler:
Nonlinear Least Squares Optimization of Constants in Symbolic Regression. EUROCAST (1) 2013: 420-427 - [c12]Michael Kommenda, Gabriel Kronberger, Stephan M. Winkler, Michael Affenzeller, Stefan Wagner:
Effects of constant optimization by nonlinear least squares minimization in symbolic regression. GECCO (Companion) 2013: 1121-1128 - [c11]Gabriel Kronberger, Michael Kommenda, Stefan Wagner, Heinz Dobler:
GPDL: a framework-independent problem definition language for grammar-guided genetic programming. GECCO (Companion) 2013: 1333-1340 - [c10]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 - [c9]Michael Affenzeller, Stephan M. Winkler, Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, Stefan Wagner:
Gaining Deeper Insights in Symbolic Regression. GPTP 2013: 175-190 - [i3]Gabriel Kronberger, Michael Kommenda:
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming. CoRR abs/1305.3794 (2013) - [i2]Michael Kommenda, Gabriel Kronberger, Christoph Feilmayr, Michael Affenzeller:
Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset. CoRR abs/1309.5931 (2013) - 2012
- [c8]Michael Kommenda, Gabriel Kronberger, Stefan Wagner, Stephan M. Winkler, Michael Affenzeller:
On the architecture and implementation of tree-based genetic programming in HeuristicLab. GECCO (Companion) 2012: 101-108 - [c7]Gabriel Kronberger, Stefan Wagner, Michael Kommenda, Andreas Beham, Andreas Scheibenpflug, Michael Affenzeller:
Knowledge Discovery through Symbolic Regression with HeuristicLab. ECML/PKDD (2) 2012: 824-827 - [i1]Gabriel Kronberger, Stefan Fink, Michael Kommenda, Michael Affenzeller:
Macro-Economic Time Series Modeling and Interaction Networks. CoRR abs/1212.2044 (2012) - 2011
- [c6]Alexandru-Ciprian Zavoianu, Gabriel Kronberger, Michael Kommenda, Daniela Zaharie, Michael Affenzeller:
Improving the Parsimony of Regression Models for an Enhanced Genetic Programming Process. EUROCAST (1) 2011: 264-271 - [c5]Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, Stefan Wagner, Witold Jacak, Herbert Stekel:
Analysis of Selected Evolutionary Algorithms in Feature Selection and Parameter Optimization for Data Based Tumor Marker Modeling. EUROCAST (1) 2011: 335-342 - [c4]Michael Kommenda, Gabriel Kronberger, Christoph Feilmayr, Leonhard Schickmair, Michael Affenzeller, Stephan M. Winkler, Stefan Wagner:
Application of Symbolic Regression on Blast Furnace and Temper Mill Datasets. EUROCAST (1) 2011: 400-407 - [c3]Gabriel Kronberger, Stefan Fink, Michael Kommenda, Michael Affenzeller:
Macro-economic Time Series Modeling and Interaction Networks. EvoApplications (2) 2011: 101-110 - [c2]Michael Kommenda, Gabriel Kronberger, Christoph Feilmayr, Michael Affenzeller:
Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset. EvoApplications (1) 2011: 274-283 - [c1]Gabriel Kronberger, Michael Kommenda, Michael Affenzeller:
Overfitting detection and adaptive covariant parsimony pressure for symbolic regression. GECCO (Companion) 2011: 631-638
2000 – 2009
- 2009
- [j1]Christian Frech, Michael Kommenda, Viktoria Dorfer, Thomas Kern, Helmut Hintner, Johann W. Bauer, Kamil Önder:
Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis. BMC Bioinform. 10 (2009)
Coauthor Index
[c52] [i21] [c50] [i19] [c48] [i11] [i10] [i7] [i6] [i5] [j6] [c45] [c44] [c39] [c38] [i4] [j5] [j4] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [p3] [p2] [j3] [c18] [c17] [j2] [c16] [c15] [c13] [c12] [c10] [c9] [i2] [c8] [c7] [i1] [c6] [c5] [c4] [c3] [c2] [c1]
[j7] [c51] [c50] [i19] [i18] [i17] [i16] [i15] [i13] [i12] [i10] [i7] [i6] [i5] [j6] [c46] [c45] [c42] [c41] [c39] [c38] [i4] [j5] [c37] [c36] [c35] [c34] [c33] [c32] [c30] [c29] [c28] [c27] [c26] [c23] [c22] [c20] [p3] [p2] [p1] [j3] [c19] [c18] [c17] [j2] [c14] [c13] [c12] [c11] [c10] [c9] [i3] [i2] [c8] [c7] [i1] [c6] [c5] [c4] [c3] [c2] [c1]
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