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Found 8 papers, 1 papers with code
The Inefficiency of Genetic Programming for Symbolic Regression -- Extended Version
This enables us to quantify the success probability of finding the best possible expressions, and to compare the search efficiency of genetic programming to random search in the space of semantically unique expressions.
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 with Fast Function Extraction and Nonlinear Least Squares Optimization
Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems.
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.
Data Aggregation for Reducing Training Data in Symbolic Regression
The performance of genetic programming is compared with random forests and linear regression.
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.
Identification of Dynamical Systems using Symbolic Regression
We describe a method for the identification of models for dynamical systems from observational data.
