Symbolic regression: Difference between revisions

Revision as of 19:34, 31 July 2022

Revision as of 11:44, 5 August 2022

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== Benchmarking ==

In 2022 the results of a large benchmarking competition known as [[SRBench]] were announced at the [[Genetic and Evolutionary Computation Conference|GECCO conference]] in Boston, MA. The competition pitted nine leading symbolic regression algorithms against each other on a large set of data problems and evaluation criteria.<ref name="srbench2022" />. The competition was organised in two tracks, a synthetic track and a real-world data track.

=== Synthetic Track ===

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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.<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|date=June 22, 2022}}</ref>

* [[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>

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== References ==

{{reflist|refs=.<ref name="bayesian">{{cite arXiv

<ref name="bayesian">{{cite arXiv

| title = Bayesian Symbolic Regression

| author1 = Ying Jin

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| class = stat.ME

| eprint = 1910.08892

}}</ref><ref name="schmidt2009distilling">{{cite journal

}}</ref>

<ref name="schmidt2009distilling">{{cite journal

| title = Distilling free-form natural laws from experimental data

| author1 = Michael Schmidt

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| citeseerx = 10.1.1.308.2245

| s2cid = 7366016

}}</ref><ref name="bayesianaifeynman">{{cite arXivjournal

}}</ref>

<ref name="aifeynman">{{cite journal

| title = AI Feynman: A physics-inspired method for symbolic regression

| author1 = Silviu-Marian Udrescu

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| pmc = 7159912

| bibcode = 2020SciA....6.2631U

}}</ref><ref name="ufo">{{cite journal

<ref name="ufo">{{cite journal

| title = Universal Functions Originator

| author1 = Ali R. Al-Roomi

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| doi = 10.1016/j.asoc.2020.106417

| s2cid = 219743405

}}</ref><ref name="aifeynmancomplexity">{{cite journal

}}</ref>

<ref name="complexity">{{cite journal

| title = Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming

| author1 = Ekaterina J. Vladislavleva

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| doi=10.1109/tevc.2008.926486

| s2cid = 12072764

}}</ref><ref name="ufoexactlearning">{{cite journal

}}</ref>

<ref name="exactlearning">{{cite journal

| title = Exact Learning

| author = Benedict W. J. Irwin

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| doi = 10.21203/rs.3.rs-149856/v1

| s2cid = 234014141

}}</ref><ref name="srbench2022">{{cite web

<ref name="srbench2022">{{cite web

|title = SRBench Competition 2022: Interpretable Symbolic Regression for Data Science

|author1 = Michael Kommenda

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|author5 = Marco Virgolin

|url = https://cavalab.org/srbench/competition-2022/

}}</ref><ref name="srfeyn">{{cite arXiv

<ref name="srfeyn">{{cite arXiv

| author1 = Kevin René Broløs

| author2 = Meera Vieira Machado

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| class = cs.LG

| eprint = 2104.05417

}}</ref>.

}}

== Further reading ==