1 code implementation • 6 Feb 2024 • Etienne Russeil, Fabrício Olivetti de França, Konstantin Malanchev, Bogdan Burlacu, Emille E. O. Ishida, Marion Leroux, Clément Michelin, Guillaume Moinard, Emmanuel Gangler
We demonstrate the effectiveness of MvSR using data generated from known expressions, as well as real-world data from astronomy, chemistry and economy, for which an a priori analytical expression is not available.
1 code implementation • 27 Nov 2023 • Deaglan J. Bartlett, Lukas Kammerer, Gabriel Kronberger, Harry Desmond, Pedro G. Ferreira, Benjamin D. Wandelt, Bogdan Burlacu, David Alonso, Matteo Zennaro
We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0. 2% between $k = 9\times10^{-3} - 9 \, h{\rm \, Mpc^{-1}}$ and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression.
1 code implementation • 13 Jun 2022 • Bogdan Burlacu, Michael Kommenda, Gabriel Kronberger, Stephan Winkler, Michael Affenzeller
This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results.
2 code implementations • 25 Mar 2022 • Bogdan Burlacu
Non-dominated sorting is a computational bottleneck in Pareto-based multi-objective evolutionary algorithms (MOEAs) due to the runtime-intensive comparison operations involved in establishing dominance relationships between solution candidates.
no code implementations • 28 Sep 2021 • Gabriel Kronberger, Lukas Kammerer, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space.
no code implementations • 28 Sep 2021 • Lukas Kammerer, Gabriel Kronberger, Bogdan Burlacu, Stephan M. Winkler, Michael Kommenda, Michael Affenzeller
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available.
no code implementations • 1 Sep 2021 • Michael Kommenda, Johannes Karder, Andreas Beham, Bogdan Burlacu, Gabriel Kronberger, Stefan Wagner, Michael Affenzeller
In this contribution we revisit the core principles of optimization networks and demonstrate their suitability for solving machine learning problems.
no code implementations • 24 Aug 2021 • 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 fitness variation from parent to offspring.
4 code implementations • 29 Jul 2021 • William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabrício Olivetti de França, Marco Virgolin, Ying Jin, Michael Kommenda, Jason H. Moore
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
no code implementations • 22 Jul 2021 • Bogdan Burlacu, Lukas Kammerer, Michael Affenzeller, Gabriel Kronberger
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 symbolic regression: fast, online calculation of population diversity and algebraic simplification of symbolic expression trees.
no code implementations • 20 Jul 2021 • Christian Haider, Fabricio Olivetti de França, Bogdan Burlacu, Gabriel Kronberger
We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function.
no code implementations • 29 Mar 2021 • Gabriel Kronberger, Fabricio Olivetti de França, Bogdan Burlacu, Christian Haider, Michael Kommenda
Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms.
no code implementations • 3 Feb 2019 • Bogdan Burlacu, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda
Diversity represents an important aspect of genetic programming, being directly correlated with search performance.