19 citations · 53 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…