High-Performance Symbolic Regression in Python and Julia
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Updated
Sep 3, 2024 - Python
High-Performance Symbolic Regression in Python and Julia
Physical Symbolic Optimization
Genetic Programming in Python, with a scikit-learn inspired API
Distributed High-Performance Symbolic Regression in Julia
Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.
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
C++ Large Scale Genetic Programming
Symbolic regression solver, based on genetic programming methodology.
SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
Ridiculously fast symbolic expressions
a python 3 library based on deap providing abstraction layers for symbolic regression problems.
EC-KitY: A scikit-learn-compatible Python tool kit for doing evolutionary computation.
Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs
Automatic equation building and curve fitting. Runs on Tensorflow. Built for academia and research.
🔮 Symbolic regression library
predicting equations from raw data with deep learning
Genetic Programming version of GOMEA. Also includes standard tree-based GP, and Semantic Backpropagation-based GP
Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.
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