Symbolic regression: Difference between revisions

Revision as of 01:50, 10 October 2022

Revision as of 09:07, 10 October 2022

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In the synthetic track, methods were compared according to five properties: re-discovery of exact expressions; feature selection; resistance to local optima; extrapolation; and sensitivity to noise. Rankings of the methods were:

# [[QLattice]]

# [[PySR]]

# [https://github.com/MilesCranmer/PySR PySR]

# [https://github.com/brendenpetersen/deep-symbolic-optimization uDSR (Deep Symbolic Optimization)]

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# [https://github.com/brendenpetersen/deep-symbolic-optimization uDSR (Deep Symbolic Optimization)]

# [[QLattice]]

# [https://github.com/alcides/GeneticEngine [geneticengine]]

== Non-Standard Methods ==

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=== End-user software ===

* [[QLattice]] is a quantum-inspired simulation and machine learning technology that helps you search through an infinite list of potential mathematical models to solve your problem (commercial).<ref>{{Cite web|url=https://docs.abzu.ai|title=Feyn is a Python module for running the QLattice|date=June 22, 2022}}</ref><ref name="srfeyn" />

* [[Deep Symbolic Optimization]] is a deep learning framework for symbolic optimization tasks<ref>{{Cite web|url=https://github.com/brendenpetersen/deep-symbolic-optimization|title=Deep symbolic optimization|website=[[GitHub]] |date=June 22, 2022}}</ref>

* [https://github.com/darioizzo/dcgp [dCGP]], differentiable Cartesian Genetic Programming in python (free, open source) <ref>{{Cite web|url=https://darioizzo.github.io/dcgp/|title=Differentiable Cartesian Genetic Programming, v1.6 Documentation|date=June 10, 2022}}</ref><ref>{{Cite journal|title=Differentiable genetic programming|first1=Dario|last1=Izzo|first2=Francesco|last2=Biscani|first3=Alessio|last3=Mereta|journal=Proceedings of the European Conference on Genetic Programming|year=2016 |arxiv=1611.04766 }}</ref>

* [[HeuristicLab]], a software environment for heuristic and evolutionary algorithms, including symbolic regression (free, open source)

* [[Gene expression programming#Software|GeneXProTools]], - an implementation of [[Gene expression programming]] technique for various problems including symbolic regression (commercial)

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* [[Eureqa]], evolutionary symbolic regression software (commercial), and [[software library]]

* [[TuringBot]], symbolic regression software based on simulated annealing (commercial)

* [https://github.com/MilesCranmer/PySR [PySR]],<ref>{{cite web |title=High-Performance Symbolic Regression in Python |website=[[GitHub]] |date=18 August 2022 |url=https://github.com/MilesCranmer/PySR}}</ref> symbolic regression environment written in [[Python (programming language)|Python]] and [[Julia (programming language)|Julia]], using regularized evolution, [[simulated annealing]], and [[gradient]]-free optimization (free, open source)<ref>{{Cite web|url=https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/|title='Machine Scientists' Distill the Laws of Physics From Raw Data|date=May 10, 2022|website=[[Quanta Magazine]]}}</ref>

* [https://github.com/marcovirgolin/GP-GOMEA GP-GOMEA], fast ([[C++]] back-end) [[genetic programming|evolutionary]] symbolic regression with [[Python (programming language)|Python]] [[scikit-learn]]-compatible interface, achieved one of the best trade-offs between accuracy and simplicity of discovered models on [https://cavalab.org/srbench/ SRBench] in 2021 (free, open source)