A Unified Framework for Deep Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{DBLP:conf/nips/LandajuelaLYGSA22,
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author = "Mikel Landajuela and Chak Shing Lee and
Jiachen Yang and Ruben Glatt and Claudio P. Santiago and
Ignacio Aravena and Terrell Nathan Mundhenk and
Garrett Mulcahy and Brenden K. Petersen",
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title = "A Unified Framework for Deep Symbolic Regression",
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booktitle = "Advances in Neural Information Processing Systems 35:
Annual Conference on Neural Information Processing
Systems, NeurIPS 2022",
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year = "2022",
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editor = "Sanmi Koyejo and S. Mohamed and A. Agarwal and
Danielle Belgrave and K. Cho and A. Oh",
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pages = "33985--33998",
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address = "New Orleans, USA",
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month = nov # " 28 - " # dec # " 9",
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publisher = "Curran Associates, Inc.",
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keywords = "genetic algorithms, genetic programming",
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timestamp = "Mon, 08 Jan 2024 16:31:35 +0100",
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biburl = "https://dblp.org/rec/conf/nips/LandajuelaLYGSA22.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "http://papers.nips.cc/paper_files/paper/2022/hash/dbca58f35bddc6e4003b2dd80e42f838-Abstract-Conference.html",
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URL = "https://proceedings.neurips.cc/paper_files/paper/2022/hash/dbca58f35bddc6e4003b2dd80e42f838-Abstract-Conference.html",
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URL = "https://proceedings.neurips.cc/paper_files/paper/2022/file/dbca58f35bddc6e4003b2dd80e42f838-Paper-Conference.pdf",
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code_url = "https://github.com/cavalab/srbench",
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size = "14 pages",
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abstract = "The last few years have witnessed a surge in methods
for symbolic regression, from advances in traditional
evolutionary approaches to novel deep learning-based
systems. Individual works typically focus on advancing
the state-of-the-art for one particular class of
solution strategies, and there have been few attempts
to investigate the benefits of hybridizing or
integrating multiple strategies. In this work, we
identify five classes of symbolic regression solution
strategies, recursive problem simplification,
neural-guided search, large-scale pre-training, genetic
programming, and linear models, and propose a strategy
to hybridise them into a single modular, unified
symbolic regression framework. Based on empirical
evaluation using SRBench, a new community tool for
benchmarking symbolic regression methods, our unified
framework achieves state-of-the-art performance in its
ability to (1) symbolically recover analytical
expressions, (2) fit datasets with high accuracy, and
(3) balance accuracy-complexity trade-offs, across 252
ground-truth and black-box benchmark problems, in both
noiseless settings and across various noise levels.
Finally, we provide practical use case-based guidance
for constructing hybrid symbolic regression algorithms,
supported by extensive, combinatorial ablation
studies.",
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notes = "also known as \cite{NEURIPS2022_dbca58f3}",
- }
Genetic Programming entries for
Mikel Landajuela
Chak Shing Lee
Jiachen Yang
Ruben Glatt
Claudio P Santiago
Ignacio Andres Aravena Solis
T Nathan Mundhenk
Garrett Mulcahy
Brenden Kyle Petersen
Citations