Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization
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 nonlinear functions. Instead of only optimizing linear parameters, we optimize these additional nonlinear parameters with separable nonlinear least squared optimization using a variable projection algorithm. Both FFX and our new algorithm is applied on the PennML benchmark suite. We show that the proposed extensions of FFX leads to higher accuracy while providing models of similar length and with only a small increase in runtime on the given data. Our results are compared to a large set of regression methods that were already published for the given benchmark suite.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionSymbolic RegressionSimilar Papers 제목 키워드 기반
Elite Bases Regression: A Real-time Algorithm for Symbolic Regression
Symbolic regression is an important but challenging research topic in data mining. It can detect the underlying mathematical models. Genetic programming (GP) is one of the most popular methods for symbolic regression. Ho…
regressionSymbolic Regressionsyren-halofit: A fast, interpretable, high-precision formula for the $Λ$CDM nonlinear matter power spectrum
Rapid and accurate evaluation of the nonlinear matter power spectrum, $P(k)$, as a function of cosmological parameters and redshift is of fundamental importance in cosmology. Analytic approximations provide an interpreta…
regressionSymbolic RegressionLearning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics
In this paper we consider the problem of learning a regression function without assuming its functional form. This problem is referred to as symbolic regression. An expression tree is typically used to represent a soluti…
regressionSymbolic RegressionSymbolic Regression using Mixed-Integer Nonlinear Optimization
The Symbolic Regression (SR) problem, where the goal is to find a regression function that does not have a pre-specified form but is any function that can be composed of a list of operators, is a hard problem in machine …
regressionSymbolic RegressionCombining Neural Networks and Symbolic Regression for Analytical Lyapunov Function Discovery
We propose CoNSAL (Combining Neural networks and Symbolic regression for Analytical Lyapunov function) to construct analytical Lyapunov functions for nonlinear dynamic systems. This framework contains a neural Lyapunov f…
regressionSymbolic Regressionvalid