Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis. - Christian Frech; Michael Kommenda; Viktoria Dorfer; Thomas Kern; Helmut Hintner; Johann W. Bauer; Kamil Önder (BMC Bioinform., 2009)

Application of Symbolic Regression on Blast Furnace and Temper Mill Datasets. - Michael Kommenda; Gabriel Kronberger; Christoph Feilmayr; Leonhard Schickmair; Michael Affenzeller; Stephan M. Winkler; Stefan Wagner 002 (EUROCAST (1), 2011)

Analysis of Selected Evolutionary Algorithms in Feature Selection and Parameter Optimization for Data Based Tumor Marker Modeling. - Stephan M. Winkler; Michael Affenzeller; Gabriel Kronberger; Michael Kommenda; Stefan Wagner 002; Witold Jacak; Herbert Stekel (EUROCAST (1), 2011)

Improving the Parsimony of Regression Models for an Enhanced Genetic Programming Process. - Alexandru-Ciprian Zavoianu; Gabriel Kronberger; Michael Kommenda; Daniela Zaharie; Michael Affenzeller (EUROCAST (1), 2011)

Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset. - Michael Kommenda; Gabriel Kronberger; Christoph Feilmayr; Michael Affenzeller (EvoApplications (1), 2011)

Macro-economic Time Series Modeling and Interaction Networks. - Gabriel Kronberger; Stefan Fink; Michael Kommenda; Michael Affenzeller (EvoApplications (2), 2011)

Overfitting detection and adaptive covariant parsimony pressure for symbolic regression. - Gabriel Kronberger; Michael Kommenda; Michael Affenzeller (GECCO (Companion), 2011)

On the architecture and implementation of tree-based genetic programming in HeuristicLab. - Michael Kommenda; Gabriel Kronberger; Stefan Wagner 002; Stephan M. Winkler; Michael Affenzeller (GECCO (Companion), 2012)

Knowledge Discovery through Symbolic Regression with HeuristicLab. - Gabriel Kronberger; Stefan Wagner 002; Michael Kommenda; Andreas Beham; Andreas Scheibenpflug; Michael Affenzeller (ECML/PKDD (2), 2012)

On the Evolutionary Behavior of Genetic Programming with Constants Optimization. - Bogdan Burlacu; Michael Affenzeller; Michael Kommenda (EUROCAST (1), 2013)

Nonlinear Least Squares Optimization of Constants in Symbolic Regression. - Michael Kommenda; Michael Affenzeller; Gabriel Kronberger; Stephan M. Winkler (EUROCAST (1), 2013)

Evolution of Covariance Functions for Gaussian Process Regression Using Genetic Programming. - Gabriel Kronberger; Michael Kommenda (EUROCAST (1), 2013)

Structural Synthesis of Dispatching Rules for Dynamic Dial-a-Ride Problems. - Stefan Vonolfen; Andreas Beham; Michael Kommenda; Michael Affenzeller (EUROCAST (1), 2013)

Visualization of genetic lineages and inheritance information in genetic programming. - Bogdan Burlacu; Michael Affenzeller; Michael Kommenda; Stephan M. Winkler; Gabriel Kronberger (GECCO (Companion), 2013)

Effects of constant optimization by nonlinear least squares minimization in symbolic regression. - Michael Kommenda; Gabriel Kronberger; Stephan M. Winkler; Michael Affenzeller; Stefan Wagner 002 (GECCO (Companion), 2013)

GPDL: a framework-independent problem definition language for grammar-guided genetic programming. - Gabriel Kronberger; Michael Kommenda; Stefan Wagner 002; Heinz Dobler (GECCO (Companion), 2013)

Gaining Deeper Insights in Symbolic Regression. - Michael Affenzeller; Stephan M. Winkler; Gabriel Kronberger; Michael Kommenda; Bogdan Burlacu; Stefan Wagner 002 (GPTP, 2013)

On the use of estimated tumour marker classifications in tumour diagnosis prediction - a case study for breast cancer. - Stephan M. Winkler; Michael Affenzeller; Gabriel Kronberger; Michael Kommenda; Stefan Wagner 002; Viktoria Dorfer; Witold Jacak; Herbert Stekel (Int. J. Simul. Process. Model., 2013)

Scripting and framework integration in heuristic optimization environments. - Andreas Beham; Johannes Karder; Gabriel Kronberger; Stefan Wagner 002; Michael Kommenda; Andreas Scheibenpflug (GECCO (Companion), 2014)

Genetic programming with data migration for symbolic regression. - Michael Kommenda; Michael Affenzeller; Bogdan Burlacu; Gabriel Kronberger; Stephan M. Winkler (GECCO (Companion), 2014)

Sliding Window Symbolic Regression for Detecting Changes of System Dynamics. - Stephan M. Winkler; Michael Affenzeller; Gabriel Kronberger; Michael Kommenda; Bogdan Burlacu; Stefan Wagner 002 (GPTP, 2014)

Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs. - Gerd Bramerdorfer; Stephan M. Winkler; Michael Kommenda; Günther Weidenholzer; Siegfried Silber; Gabriel Kronberger; Michael Affenzeller; Wolfgang Amrhein (IEEE Trans. Ind. Electron., 2014)

Optimization Strategies for Integrated Knapsack and Traveling Salesman Problems. - Andreas Beham; Judith Fechter; Michael Kommenda; Stefan Wagner 002; Stephan M. Winkler; Michael Affenzeller (EUROCAST, 2015)

On the Effectiveness of Genetic Operations in Symbolic Regression. - Bogdan Burlacu; Michael Affenzeller; Michael Kommenda (EUROCAST, 2015)

Complexity Measures for Multi-objective Symbolic Regression. - Michael Kommenda; Andreas Beham; Michael Affenzeller; Gabriel Kronberger (EUROCAST, 2015)

Using Contextual Information in Sequential Search for Grammatical Optimization Problems. - Gabriel Kronberger; Michael Kommenda; Stephan M. Winkler; Michael Affenzeller (EUROCAST, 2015)

Dynamics of Predictability and Variable Influences Identified in Financial Data Using Sliding Window Machine Learning. - Stephan M. Winkler; Gabriel Kronberger; Michael Kommenda; Stefan Fink; Michael Affenzeller (EUROCAST, 2015)

Simplifying Problem Definitions in the HeuristicLab Optimization Environment. - Andreas Scheibenpflug; Andreas Beham; Michael Kommenda; Johannes Karder; Stefan Wagner 002; Michael Affenzeller (GECCO (Companion), 2015)

Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming. - Michael Kommenda; Gabriel Kronberger; Michael Affenzeller; Stephan M. Winkler; Bogdan Burlacu (GPTP, 2015)

Methods for Genealogy and Building Block Analysis in Genetic Programming. - Bogdan Burlacu; Michael Affenzeller; Stephan M. Winkler; Michael Kommenda; Gabriel Kronberger (Computational Intelligence and Efficiency in Engineering Systems, 2015)

Multi-Population Genetic Programming with Data Migration for Symbolic Regression. - Michael Kommenda; Michael Affenzeller; Gabriel Kronberger; Bogdan Burlacu; Stephan M. Winkler (Computational Intelligence and Efficiency in Engineering Systems, 2015)

Search Strategies for Grammatical Optimization Problems - Alternatives to Grammar-Guided Genetic Programming. - Gabriel Kronberger; Michael Kommenda (Computational Intelligence and Efficiency in Engineering Systems, 2015)

Similarity-Based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression. - Stephan M. Winkler; Michael Affenzeller; Bogdan Burlacu; Gabriel Kronberger; Michael Kommenda; Philipp Fleck (GPTP, 2016)

Robust Fuzzy Modeling and Symbolic Regression for Establishing Accurate and Interpretable Prediction Models in Supervising Tribological Systems. - Edwin Lughofer; Gabriel Kronberger; Michael Kommenda; Susanne Saminger-Platz; Andreas Promberger; Falk Nickel; Stephan M. Winkler; Michael Affenzeller (IJCCI (FCTA), 2016)

Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals. - Michael Affenzeller; Bogdan Burlacu; Stephan M. Winkler; Michael Kommenda; Gabriel Kronberger; Stefan Wagner 002 (EUROCAST (1), 2017)

Analysis of Schema Frequencies in Genetic Programming. - Bogdan Burlacu; Michael Affenzeller; Michael Kommenda; Gabriel Kronberger; Stephan M. Winkler (EUROCAST (1), 2017)

Optimization Networks for Integrated Machine Learning. - Michael Kommenda; Johannes Karder; Andreas Beham; Bogdan Burlacu; Gabriel Kronberger; Stefan Wagner 002; Michael Affenzeller (EUROCAST (1), 2017)

Measures for the Evaluation and Comparison of Graphical Model Structures. - Gabriel Kronberger; Bogdan Burlacu; Michael Kommenda; Stephan M. Winkler; Michael Affenzeller (EUROCAST (1), 2017)

Dynamic observation of genotypic and phenotypic diversity for different symbolic regression GP variants. - Michael Affenzeller; Stephan M. Winkler; Bogdan Burlacu; Gabriel Kronberger; Michael Kommenda; Stefan Wagner 002 (GECCO (Companion), 2017)

Towards the design and implementation of optimization networks in HeuristicLab. - Johannes Karder; Stefan Wagner 002; Andreas Beham; Michael Kommenda; Michael Affenzeller (GECCO (Companion), 2017)

Schema Analysis in Tree-Based Genetic Programming. - Bogdan Burlacu; Michael Affenzeller; Michael Kommenda; Gabriel Kronberger; Stephan M. Winkler (GPTP, 2017)

Predicting friction system performance with symbolic regression and genetic programming with factor variables. - Gabriel Kronberger; Michael Kommenda; Andreas Promberger; Falk Nickel (GECCO, 2018)

Cluster Analysis of a Symbolic Regression Search Space. - Gabriel Kronberger; Lukas Kammerer; Bogdan Burlacu; Stephan M. Winkler; Michael Kommenda; Michael Affenzeller (GPTP, 2018)

Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems. - Gabriel Kronberger; Michael Kommenda; Edwin Lughofer; Susanne Saminger-Platz; Andreas Promberger; Falk Nickel; Stephan M. Winkler; Michael Affenzeller (Appl. Soft Comput., 2018)

Novel robustness measures for engineering design optimisation. - Philipp Fleck; Michael Kommenda; Thorsten Prante; Michael Affenzeller (Int. J. Simul. Process. Model., 2018)

Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure. - Bogdan Burlacu; Michael Affenzeller; Gabriel Kronberger; Michael Kommenda (CEC, 2019)

White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems. - Michael Affenzeller; Bogdan Burlacu; Viktoria Dorfer; Sebastian Dorl; Gerhard Halmerbauer; Tilman Königswieser; Michael Kommenda; Julia Vetter; Stephan M. Winkler (EUROCAST (1), 2019)

Understanding and Preparing Data of Industrial Processes for Machine Learning Applications. - Philipp Fleck; Manfred Kügel; Michael Kommenda (EUROCAST (1), 2019)

Preprocessing and Modeling of Radial Fan Data for Health State Prediction. - Florian Holzinger; Michael Kommenda (EUROCAST (1), 2019)

Data Aggregation for Reducing Training Data in Symbolic Regression. - Lukas Kammerer; Gabriel Kronberger; Michael Kommenda (EUROCAST (1), 2019)

Identification of Dynamical Systems Using Symbolic Regression. - Gabriel Kronberger; Lukas Kammerer; Michael Kommenda (EUROCAST (1), 2019)

Parsimony measures in multi-objective genetic programming for symbolic regression. - Bogdan Burlacu; Gabriel Kronberger; Michael Kommenda; Michael Affenzeller (GECCO (Companion), 2019)

Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication. - Lukas Kammerer; Gabriel Kronberger; Bogdan Burlacu; Stephan M. Winkler; Michael Kommenda; Michael Affenzeller (GPTP, 2019)

Operon C++: an efficient genetic programming framework for symbolic regression. - Bogdan Burlacu; Gabriel Kronberger; Michael Kommenda (GECCO Companion, 2020)

Parameter identification for symbolic regression using nonlinear least squares. - Michael Kommenda; Bogdan Burlacu; Gabriel Kronberger; Michael Affenzeller (Genet. Program. Evolvable Mach., 2020)

Grammar-Based Vectorial Genetic Programming for Symbolic Regression. - Philipp Fleck; Stephan M. Winkler; Michael Kommenda; Michael Affenzeller (GPTP, 2021)

Contemporary Symbolic Regression Methods and their Relative Performance. - William G. La Cava; Patryk Orzechowski; Bogdan Burlacu; Fabrício Olivetti de França; Marco Virgolin; Ying Jin; Michael Kommenda; Jason H. Moore (NeurIPS Datasets and Benchmarks, 2021)

Predicting Calibration Parameter Values for Constitutive Models using Genetic Programming. - Gabriel Kronberger; Evgeniya Kabliman; Johannes Kronsteiner; Michael Kommenda (CoRR, 2021)

Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization. - Lukas Kammerer; Gabriel Kronberger; Michael Kommenda (EUROCAST, 2022)

Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data. - Bogdan Burlacu; Michael Kommenda; Gabriel Kronberger; Stephan M. Winkler; Michael Affenzeller (GPTP, 2022)

Application of Symbolic Regression in Polymer Processing. - Wolfgang Roland; Michael Kommenda; Gerald R. Berger-Weber (SYNASC, 2022)

Shape-Constrained Symbolic Regression - Improving Extrapolation with Prior Knowledge. - Gabriel Kronberger; Fabrício Olivetti de França; Bogdan Burlacu; Christian Haider; Michael Kommenda (Evol. Comput., 2022)

Evolutionary Algorithms for Segment Optimization in Vectorial GP. - Philipp Fleck; Stephan M. Winkler; Michael Kommenda; Sara Silva; Leonardo Vanneschi; Michael Affenzeller (GECCO Companion, 2023)

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. - 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; et al. (CoRR, 2023)

Vectorial Genetic Programming - Optimizing Segments for Feature Extraction. - Philipp Fleck; Stephan Winkler 001; Michael Kommenda; Michael Affenzeller (EUROCAST (1), 2024)

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. - 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; et al. (IEEE Trans. Evol. Comput., 2025)