144 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Johannes Kepler University of LinzAT14 papers
- Universidade Federal do ABCBR7 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- University of PortsmouthGB4 papers
- Institut d'Astrophysique de ParisFR3 papers
- Research Center for Non Destructive Testing (Austria)AT3 papers
- Sorbonne UniversitéFR3 papers
- University of OxfordGB3 papers
- Boston Children's HospitalUS2 papers
- Harvard UniversityUS2 papers
- Software Competence Center Hagenberg (Austria)AT2 papers
- Technische Hochschule UlmDE2 papers
10 papers · 1 filter
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…
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…
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…
The State and Future of Genetic Improvement
William B. Langdon, Westley Weimer, Christopher Timperley +9
We report the discussion session at the sixth international Genetic Improvement workshop, GI-2019 @ ICSE, which was held as part of the 41st ACM/IEEE International Conference on So…
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…
On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection
Gabriel Kronberger, Stephan Winkler, Michael Affenzeller +2
Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve among others regression, classification, and time-series…
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…