High-Performance Symbolic Regression in Python and Julia
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Updated
Jun 22, 2024 - Python
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.
A framework for gene expression programming (an evolutionary algorithm) in Python
a python 3 library based on deap providing abstraction layers for symbolic regression problems.
EC-KitY is 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"
predicting equations from raw data with deep learning
Evolving Symbolic Pruning Metric from scratch
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. In this work, we present SymbolicGPT, a novel transformer-based language model for symbolic regression.
[ICLR 2024 Spotlight] This is the official code for the paper "SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training"
AI Physicist, a paradigm with algorithms for learning theories from data, by Wu and Tegmark (2019)
Cartesian genetic programming (CGP) in pure Python.
A baseline implementation of genetic programming (using trees to encode programs) with some examples of usage.
Simple Genetic Programming for Symbolic Regression in Python3
a lightweight implementation of Cartesian genetic programming with symbolic regression in mind.
An approach for embedding hierarhical structures into a continuous vector space using variational autoencoders.
[DMLR] Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
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