20 citations · 69 across the 9 of their papers we have counts for
10 papers
Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression
Lukas Kammerer, Gabriel Kronberger, Deaglan J. Bartlett +3
We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three we…
syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
Lukas Kammerer, Deaglan J. Bartlett, Gabriel Kronberger +2
Baryonic physics has a considerable impact on the distribution of matter in our Universe on scales probed by current and future cosmological surveys, acting as a key systematic in…
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…
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…
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…
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…
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…
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…