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Found 13 papers, 5 papers with code
Multi-View Symbolic Regression
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.
A precise symbolic emulator of the linear matter power spectrum
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.
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
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.
Rank-based Non-dominated Sorting
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.
Cluster Analysis of a Symbolic Regression Search Space
In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space.
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available.
Optimization Networks for Integrated Machine Learning
In this contribution we revisit the core principles of optimization networks and demonstrate their suitability for solving machine learning problems.
On the Effectiveness of Genetic Operations in Symbolic Regression
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.
Contemporary Symbolic Regression Methods and their Relative Performance
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression
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.
Using Shape Constraints for Improving Symbolic Regression Models
We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function.
Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge
Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms.
Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure
Diversity represents an important aspect of genetic programming, being directly correlated with search performance.
