74 citations · 241 across the 31 of their papers we have counts for
33 papers
A Comparative Study of Model Selection Criteria for Symbolic Regression
Ali Soltani, Gabriel Kronberger, Fabricio Olivetti de Franca +2
Effective model selection is critical in symbolic regression (SR) to identify mathematical expressions that balance accuracy and complexity, and have low expected error on unseen d…
Identification of Empirical Constitutive Models for Age-Hardenable Aluminium Alloy and High-Chromium Martensitic Steel Using Symbolic Regression
Evgeniya Kabliman, Gabriel Kronberger
Process-structure-property relationships are fundamental in materials science and engineering and are key to the development of new and improved materials. Symbolic regression serv…
A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming
Yousef A. Radwan, Gabriel Kronberger, Stephan Winkler
Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networ…
The Inefficiency of Genetic Programming for Symbolic Regression
Gabriel Kronberger, Fabricio Olivetti de Franca, Harry Desmond +2
We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This…
A precise symbolic emulator of the linear matter power spectrum
Deaglan J. Bartlett, Lukas Kammerer, Gabriel Kronberger +6
Computing the matter power spectrum, , as a function of cosmological parameters can be prohibitively slow in cosmological analyses, hence emulating this calculation is desira…
Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction
Daniel Parra, David Joedicke, J. Manuel Velasco +2
People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose regulation requires a proper combination of food intake and insulin b…
Steel Phase Kinetics Modeling using Symbolic Regression
David Piringer, Bernhard Bloder, Gabriel Kronberger
We describe an approach for empirical modeling of steel phase kinetics based on symbolic regression and genetic programming. The algorithm takes processed data gathered from dilato…
Identifying Differential Equations to predict Blood Glucose using Sparse Identification of Nonlinear Systems
David Jödicke, Daniel Parra, Gabriel Kronberger +1
Describing dynamic medical systems using machine learning is a challenging topic with a wide range of applications. In this work, the possibility of modeling the blood glucose leve…
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization
Lukas Kammerer, Gabriel Kronberger, Michael Kommenda
Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems. We improve the accuracy of FFX by adding parameters to the arguments of nonlin…
Comparing Shape-Constrained Regression Algorithms for Data Validation
Florian Bachinger, Gabriel Kronberger
Industrial and scientific applications handle large volumes of data that render manual validation by humans infeasible. Therefore, we require automated data validation approaches t…
Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles
Fabricio Olivetti de Franca, Gabriel Kronberger
Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of…
Local Optimization Often is Ill-conditioned in Genetic Programming for Symbolic Regression
Gabriel Kronberger
Gradient-based local optimization has been shown to improve results of genetic programming (GP) for symbolic regression. Several state-of-the-art GP implementations use iterative n…
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
Bogdan Burlacu, Michael Kommenda, Gabriel Kronberger +2
Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle sim…
Cluster Analysis of a Symbolic Regression Search Space
Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu +3
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space. The motivation for this work is to imp…
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu +3
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. Such scenarios often require spe…
Optimization Networks for Integrated Machine Learning
Michael Kommenda, Johannes Karder, Andreas Beham +4
Optimization networks are a new methodology for holistically solving interrelated problems that have been developed with combinatorial optimization problems in mind. In this contri…
Complexity Measures for Multi-objective Symbolic Regression
Michael Kommenda, Andreas Beham, Michael Affenzeller +1
Multi-objective symbolic regression has the advantage that while the accuracy of the learned models is maximized, the complexity is automatically adapted and need not be specified…
Data Aggregation for Reducing Training Data in Symbolic Regression
Lukas Kammerer, Gabriel Kronberger, Michael Kommenda
The growing volume of data makes the use of computationally intense machine learning techniques such as symbolic regression with genetic programming more and more impractical. This…
Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-valued Optimization Problem
Erik Pitzer, Gabriel Kronberger
The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization…
Concept Drift Detection with Variable Interaction Networks
Jan Zenisek, Gabriel Kronberger, Josef Wolfartsberger +2
The current development of today's production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance. By this means, the co…
Extending a Physics-Based Constitutive Model using Genetic Programming
Gabriel Kronberger, Evgeniya Kabliman, Johannes Kronsteiner +1
In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity) and their relations to processing conditions. A major draw…
Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications
Florian Bachinger, Gabriel Kronberger
With the increasing number of created and deployed prediction models and the complexity of machine learning workflows we require so called model management systems to support data…
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression
Bogdan Burlacu, Lukas Kammerer, Michael Affenzeller +1
We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symb…
Using Shape Constraints for Improving Symbolic Regression Models
Christian Haider, Fabricio Olivetti de França, Bogdan Burlacu +1
We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function. This is rele…