High-Performance Symbolic Regression in Python and Julia

Physical Symbolic Optimization

Genetic Programming in Python, with a scikit-learn inspired API

Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.

Distributed High-Performance Symbolic Regression in Julia

A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.

A framework for gene expression programming (an evolutionary algorithm) in Python

A GPU-accelerated library for Tree-based Genetic Programming, leveraging PyTorch and custom CUDA kernels for high-performance evolutionary computation. It supports symbolic regression, classification, and policy optimization with advanced features like multi-output trees and benchmark tools.

[ICLR 2025 Oral] This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with Large Language Models

C++ Large Scale Genetic Programming

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

Ridiculously fast symbolic expressions

Symbolic regression solver, based on genetic programming methodology.

EC-KitY: A scikit-learn-compatible Python tool kit for doing evolutionary computation.

[ICML24] Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs

a python 3 library based on deap providing abstraction layers for symbolic regression problems.

[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"

Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"

Official PyTorch implementation of PSE/PSRN: Fast and efficient symbolic expression discovery through parallelized symbolic enumeration. Evaluates millions of expressions simultaneously on GPU with automated subtree reuse.

Automatic equation building and curve fitting. Runs on Tensorflow. Built for academia and research.