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.

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.

Sep 7, 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.

1 day ago · Operon is an efficient state-of-the-art software implementation of genetic programming for symbolic regression. We use it here as a representative for symbolic regression systems based on genetic programming. Operon is implemented in modern C++ and relies heavily on thread-based parallelism to speed-up GP on multi-core machines.

1 day ago · In our work, we propose a hybrid cooperative genetic programming approach for the symbolic regression problem. The proposed algorithm is a hybridization of a new MOEA/D and an NSGA-II. The objective is that the two algorithms support each other cooperatively by communicating the most promising solutions to create a smoother and well-distributed ...

1 day ago · 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 ...

Apr 18, 2025 · Explore symbolic regression using genetic programming in Python, a key aspect of AI as a Novel Programming Paradigm. Symbolic regression is a powerful technique that leverages genetic programming to discover mathematical expressions that best fit a given dataset.

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.

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.