144 citations · 336 across the 21 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…