Genetic Programming in Python, with a scikit-learn inspired API
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Updated
Nov 29, 2023 - Python
Genetic Programming in Python, with a scikit-learn inspired API
Physical Symbolic Optimization
High-Performance Symbolic Regression in Python and Julia
Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.
A framework for gene expression programming (an evolutionary algorithm) in Python
Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"
EC-KitY is a scikit-learn-compatible Python tool kit for doing evolutionary computation.
a python 3 library based on deap providing abstraction layers for symbolic regression problems.
Simple Genetic Programming for Symbolic Regression in Python3
Cartesian genetic programming (CGP) in pure Python.
predicting equations from raw data with deep learning
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
AI Physicist, a paradigm with algorithms for learning theories from data, by Wu and Tegmark (2019)
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.
A baseline implementation of genetic programming (using trees to encode programs) with some examples of usage.
Evolving Symbolic Pruning Metric from scratch
An approach for embedding hierarhical structures into a continuous vector space using variational autoencoders.
a lightweight implementation of Cartesian genetic programming with symbolic regression in mind.
[DMLR] Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
Code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (CEC-2019). Paper Link: https://ieeexplore.ieee.org/document/8790341
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