144 citations · 336 across the 21 of their papers we have counts for
21 papers
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
F. O. de Franca, M. Virgolin, M. Kommenda +21
Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model tha…
Vectorial Genetic Programming -- Optimizing Segments for Feature Extraction
Philipp Fleck, Stephan Winkler, Michael Kommenda +1
Vectorial Genetic Programming (Vec-GP) extends GP by allowing vectors as input features along regular, scalar features, using them by applying arithmetic operations component-wise…
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…
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…
Understanding and Preparing Data of Industrial Processes for Machine Learning Applications
Philipp Fleck, Manfred Kügel, Michael Kommenda
Industrial applications of machine learning face unique challenges due to the nature of raw industry data. Preprocessing and preparing raw industrial data for machine learning appl…
Preprocessing and Modeling of Radial Fan Data for Health State Prediction
Florian Holzinger, Michael Kommenda
Monitoring critical components of systems is a crucial step towards failure safety. Affordable sensors are available and the industry is in the process of introducing and extending…
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…
On the Effectiveness of Genetic Operations in Symbolic Regression
Bogdan Burlacu, Michael Affenzeller, Michael Kommenda
This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the…
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…
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…
Contemporary Symbolic Regression Methods and their Relative Performance
William La Cava, Patryk Orzechowski, Bogdan Burlacu +5
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent…
Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables
Gabriel Kronberger, Michael Kommenda, Andreas Promberger +1
Friction systems are mechanical systems wherein friction is used for force transmission (e.g. mechanical braking systems or automatic gearboxes). For finding optimal and safe desig…
Identification of Dynamical Systems using Symbolic Regression
Gabriel Kronberger, Lukas Kammerer, Michael Kommenda
We describe a method for the identification of models for dynamical systems from observational data. The method is based on the concept of symbolic regression and uses genetic prog…
Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge
Gabriel Kronberger, Fabricio Olivetti de França, Bogdan Burlacu +2
We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-c…
Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure
Bogdan Burlacu, Michael Affenzeller, Gabriel Kronberger +1
Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires…
Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset
Michael Kommenda, Gabriel Kronberger, Christoph Feilmayr +1
In this paper a data mining approach for variable selection and knowledge extraction from datasets is presented. The approach is based on unguided symbolic regression (every variab…
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming
Gabriel Kronberger, Michael Kommenda
In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and…
Macro-Economic Time Series Modeling and Interaction Networks
Gabriel Kronberger, Stefan Fink, Michael Kommenda +1
Macro-economic models describe the dynamics of economic quantities. The estimations and forecasts produced by such models play a substantial role for financial and political decisi…