Reducing Overparameterization of Symbolic Regression Models with Equality Saturation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{de-franca:2023:GECCO,
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author = "Fabricio {Olivetti de Franca} and Gabriel Kronberger",
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title = "Reducing Overparameterization of Symbolic Regression
Models with Equality Saturation",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "1064--1072",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming,
simplification, symbolic regression, equality
saturation",
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isbn13 = "9798400701191",
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URL = "https://pure.fh-ooe.at/en/publications/reducing-overparameterization-of-symbolic-regression-models-with-",
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URL = "https://dl.acm.org/doi/abs/10.1145/3583131.3590346",
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DOI = "doi:10.1145/3583131.3590346",
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size = "9 pages",
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abstract = "Overparameterized models in regression analysis are
often harder to interpret and can be harder to fit
because of ill-conditioning. Genetic programming is
prone to overparameterized models as it evolves the
structure of the model without taking the location of
parameters into account. One way to alleviate this is
rewriting the expression and merging the redundant
fitting parameters. In this paper we propose the use of
equality saturation to alleviate overparameterization.
We first notice that all the tested GP implementations
suffer from overparameterization to different extents
and then show that equality saturation together with a
small set of rewriting rules is capable of reducing the
number of fitting parameters to a minimum with a high
probability. Compared to one of the few available
alternatives, Sympy, it produces much better and
consistent results. These results lead to different
possible future investigations such as the
simplification of expressions during the evolutionary
process, and improvement of the interpretability of
symbolic models.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Fabricio Olivetti de Franca
Gabriel Kronberger
Citations