Michael Kommenda

发表

Using Genetic Programming in Nonlinear Model Identification

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2012 .

Symbolic regression with sampling

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2010 .

Application of Symbolic Regression on Blast Furnace and Temper Mill Datasets

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2011, EUROCAST.

Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset

Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, 2011, EvoApplications.

Macro-economic Time Series Modeling and Interaction Networks

Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, 2011, EvoApplications.

Gaining Deeper Insights in Symbolic Regression

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2013, GPTP.

Data Aggregation for Reducing Training Data in Symbolic Regression

Gabriel Kronberger, Michael Kommenda, Lukas Kammerer, 2019, EUROCAST.

Search Strategies for Grammatical Optimization Problems - Alternatives to Grammar-Guided Genetic Programming

Gabriel Kronberger, Michael Kommenda, 2015, Computational Intelligence and Efficiency in Engineering Systems.

Parameter identification for symbolic regression using nonlinear least squares

Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, 2019, Genetic Programming and Evolvable Machines.

Parsimony measures in multi-objective genetic programming for symbolic regression

Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, 2019, GECCO.

On the use of estimated tumour marker classifications in tumour diagnosis prediction - a case study for breast cancer

Witold Jacak, Stephan M. Winkler, Michael Affenzeller, 2013, Int. J. Simul. Process. Model..

Predicting friction system performance with symbolic regression and genetic programming with factor variables

Gabriel Kronberger, Michael Kommenda, Andreas Promberger, 2018, GECCO.

Analysis of Selected Evolutionary Algorithms in Feature Selection and Parameter Optimization for Data Based Tumor Marker Modeling

Witold Jacak, Stephan M. Winkler, Michael Affenzeller, 2011, EUROCAST.

Cluster Analysis of a Symbolic Regression Search Space

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2018, GPTP.

Sliding Window Symbolic Regression for Detecting Changes of System Dynamics

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2014, GPTP.

Robust Fuzzy Modeling and Symbolic Regression for Establishing Accurate and Interpretable Prediction Models in Supervising Tribological Systems

Edwin Lughofer, Stephan M. Winkler, Michael Affenzeller, 2016, IJCCI.

Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure

Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

Parameter Identification for Simulation Models by Heuristic Optimization

Stephan M. Winkler, Michael Kommenda, M. Kommenda, 2015 .

Dynamic observation of genotypic and phenotypic diversity for different symbolic regression GP variants

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2017, GECCO.

Analysis of uncertainty in engineering design optimization problems

Michael Affenzeller, Michael Kommenda, Thorsten Prante, 2016 .

Enhanced confidence interpretations of GP based ensemble modeling results

Stephan M. Winkler, Stefan Wagner, Gabriel Kronberger, 2012 .

Box-Type Boom Design Using Surrogate Modeling: Introducing an Industrial Optimization Benchmark

Michael Affenzeller, Michael Kommenda, Martin Schwarz, 2018, Computational Methods in Applied Sciences.

Towards the design and implementation of optimization networks in HeuristicLab

Michael Affenzeller, Stefan Wagner, Michael Kommenda, 2017, GECCO.

On the Effectiveness of Genetic Operations in Symbolic Regression

Michael Affenzeller, Michael Kommenda, Bogdan Burlacu, 2015, EUROCAST.

Optimization Strategies for Integrated Knapsack and Traveling Salesman Problems

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2015, EUROCAST.

Feature selection in the analysis of tumor marker data using evolutionary algorithms

Witold Jacak, Stephan M. Winkler, Michael Affenzeller, 2010 .

Genetic programming with data migration for symbolic regression

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2014, GECCO.

Heat treatment process parameter estimation using heuristic optimization algorithms

Michael Affenzeller, Michael Kommenda, Bogdan Burlacu, 2015 .

Visualization of genetic lineages and inheritance information in genetic programming

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2013, GECCO.

System Identification of Blast Furnace Processes with Genetic Programming

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2009, 2009 2nd International Symposium on Logistics and Industrial Informatics.

Identification of Dynamical Systems Using Symbolic Regression

Gabriel Kronberger, Michael Kommenda, Lukas Kammerer, 2019, EUROCAST.

Effects of constant optimization by nonlinear least squares minimization in symbolic regression

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2013, GECCO.

Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2021, GPTP.

On the architecture and implementation of tree-based genetic programming in HeuristicLab

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2012, GECCO '12.

GPDL: a framework-independent problem definition language for grammar-guided genetic programming

Stefan Wagner, Gabriel Kronberger, Michael Kommenda, 2013, GECCO.

Architecture and Design of the HeuristicLab Optimization Environment

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2014 .

Novel robustness measures for engineering design optimisation

Michael Affenzeller, Michael Kommenda, Thorsten Prante, 2018, Int. J. Simul. Process. Model..

Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2016 .

White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems

Stephan M. Winkler, Michael Kommenda, Michael Affenzeller, 2019, EUROCAST.

Preprocessing and Modeling of Radial Fan Data for Health State Prediction

Michael Kommenda, Florian Holzinger, 2019, EUROCAST.

Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset

Gabriel Kronberger, Michael Kommenda, Christoph Feilmayr, 2013 .

Application of Genetic Programming on Temper Mill Datasets

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2009, 2009 2nd International Symposium on Logistics and Industrial Informatics.

Multi-Population Genetic Programming with Data Migration for Symbolic Regression

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2015, Computational Intelligence and Efficiency in Engineering Systems.

Analysis of Schema Frequencies in Genetic Programming

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2017, EUROCAST.

Schema Analysis in Tree-Based Genetic Programming

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2017, GPTP.

Simplifying Problem Definitions in the HeuristicLab Optimization Environment

Michael Affenzeller, Stefan Wagner, Michael Kommenda, 2015, GECCO.

Structural Synthesis of Dispatching Rules for Dynamic Dial-a-Ride Problems

Michael Affenzeller, Michael Kommenda, Andreas Beham, 2013, EUROCAST.

Operon C++: an efficient genetic programming framework for symbolic regression

Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, 2020, GECCO Companion.

Overfitting detection and adaptive covariant parsimony pressure for symbolic regression

Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, 2011, GECCO.

Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2014, IEEE Transactions on Industrial Electronics.

Dynamics of Predictability and Variable Influences Identified in Financial Data Using Sliding Window Machine Learning

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2015, EUROCAST.

Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems

Edwin Lughofer, Stephan M. Winkler, Michael Affenzeller, 2018, Appl. Soft Comput..

Integrated simulation and optimization in HeuristicLab

Stefan Wagner, Gabriel Kronberger, Michael Kommenda, 2014 .

Evolution of Covariance Functions for Gaussian Process Regression Using Genetic Programming pdf

Gabriel Kronberger, Michael Kommenda, G. Kronberger, 2013, EUROCAST.

On the Evolutionary Behavior of Genetic Programming with Constants Optimization

Michael Affenzeller, Michael Kommenda, Bogdan Burlacu, 2013, EUROCAST.

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2016, GPTP.

Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2017, EUROCAST.

Novel robustness measures for engineering design optimisation

Michael Affenzeller, Michael Kommenda, Thorsten Prante, 2018 .

Measures for the Evaluation and Comparison of Graphical Model Structures

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2017, EUROCAST.

Optimization Networks for Integrated Machine Learning

Michael Affenzeller, Stefan Wagner, Gabriel Kronberger, 2017, EUROCAST.

Methods for Genealogy and Building Block Analysis in Genetic Programming

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2015, Computational Intelligence and Efficiency in Engineering Systems.

Evolution tracking in genetic programming

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2012 .

Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis

Michael Kommenda, Thomas Kern, Viktoria Dorfer, 2009, BMC Bioinformatics.

Complexity Measures for Multi-objective Symbolic Regression

Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, 2015, EUROCAST.

Scripting and framework integration in heuristic optimization environments

Stefan Wagner, Gabriel Kronberger, Michael Kommenda, 2014, GECCO.

Using Contextual Information in Sequential Search for Grammatical Optimization Problems

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2015, EUROCAST.

Nonlinear Least Squares Optimization of Constants in Symbolic Regression

Stephan M. Winkler, Michael Affenzeller, Gabriel Kronberger, 2013, EUROCAST.

Improving the Parsimony of Regression Models for an Enhanced Genetic Programming Process

Michael Affenzeller, Gabriel Kronberger, Michael Kommenda, 2011, EUROCAST.

Simulation-Based Optimization with HeuristicLab: Practical Guidelines and Real-World Applications

Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, 2015 .

Knowledge Discovery through Symbolic Regression with HeuristicLab

Michael Affenzeller, Stefan Wagner, Gabriel Kronberger, 2012, ECML/PKDD.

Understanding and Preparing Data of Industrial Processes for Machine Learning Applications

Michael Kommenda, Philipp Fleck, Manfred Kügel, 2019, EUROCAST.

Predicting Calibration Parameter Values for Constitutive Models using Genetic Programming pdf

Gabriel Kronberger, Michael Kommenda, Evgeniya Kabliman, 2021, ArXiv.

Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge

Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, 2021, Evolutionary Computation.

Contemporary Symbolic Regression Methods and their Relative Performance pdf

Michael Kommenda, William La Cava, Marco Virgolin, 2021, NeurIPS Datasets and Benchmarks.

Extending a physics-based constitutive model using genetic programming

Gabriel Kronberger, Michael Kommenda, Evgeniya Kabliman, 2021, Applications in Engineering Science.

Identification of Dynamical Systems using Symbolic Regression

Gabriel Kronberger, Michael Kommenda, Lukas Kammerer, 2021 .

On the Use of Estimated Tumor Marker Classifications in Tumor Diagnosis Prediction - A Case Study for Breast Cancer

Witold Jacak, Stephan M. Winkler, Michael Affenzeller, 2011 .

Building Blocks Identification Based on Subtree Sample Counts for Genetic Programming

Michael Affenzeller, Michael Kommenda, Bogdan Burlacu, 2015, 2015 Asia-Pacific Conference on Computer Aided System Engineering.