Abstract: Presents an implementation of symbolic regression which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones.

Jan 11, 2021 · Symbolic Regression is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity.

Sep 14, 2024 · Establishing the NP-hard nature of the SR problem, this study introduces a novel approach named Symbol Graph Genetic Programming (SGGP) (Code is available at https://github.com/SymbolGraph/sggp). SGGP begins by constructing a symbol graph to represent the mathematical expression space effectively.

Oct 29, 2021 · In this work, we introduce a hybrid neural-guided/genetic programming approach to symbolic regression and other combinatorial optimization problems. We propose a neural-guided component used to seed the starting population of a random restart genetic programming component, gradually learning better starting populations.

This project is my first attempt at implementing an evolutionary algorithm using standard genetic programming techniques. The algorithm is designed to solve a symbolic regression problem by evolving mathematical expressions over generations.

Apr 24, 2025 · Symbolic Regression (SR) is a powerful technique for uncovering hidden mathematical expressions from observed data and has broad applications in scientific discovery and automatic programming. Genetic Programming (GP) has traditionally been the dominant technique for solving the SR, benefiting from a robust global search capability that enables the discovery of solutions with high fitting ...

Nov 13, 2024 · Symbolic regression is one of the best known problems in GP (see Reference). It is commonly used as a tuning problem for new algorithms, but is also widely used with real-life distributions, where other regression methods may not work. It is conceptually a simple problem, and therefore makes a good introductory example for the GP framework in DEAP.

6 days ago · Symbolic regression is a powerful technique that leverages genetic programming to discover mathematical expressions that best fit a given dataset. This approach is particularly useful in scenarios where the underlying relationship between variables is unknown or complex.

2 days ago · This paper introduces a symbolic regression based filter transform for convolutional neural network using CGP (Cartesian Genetic Programming). Symbolic regression is a powerful technique to discover analytic equations that describe data, which can ...

Apr 24, 2025 · Symbolic regression is a technique that can automatically derive analytic models from data. Traditionally, symbolic regression has been implemented primarily through genetic programming that evolves populations of candidate solutions sampled by genetic operators, crossover and mutation. More recently, neural networks have been employed to learn the entire analytical model, i.e., its structure ...