Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear Scaling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{chen:2023:GECCO,
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author = "Qi Chen and Bing Xue and Wolfgang Banzhaf and
Mengjie Zhang",
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title = "Relieving Genetic Programming from Coefficient
Learning for Symbolic Regression via Correlation and
Linear Scaling",
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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 = "420--428",
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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, fitness
function, correlation, linear scaling, symbolic
regression",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3595918",
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size = "9 pages",
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abstract = "The difficulty of learning optimal coefficients in
regression models using only genetic operators has long
been a challenge in genetic programming for symbolic
regression. As a simple but effective remedy it has
been proposed to perform linear scaling of model
outputs prior to a fitness evaluation. Recently, the
use of a correlation coefficient-based fitness function
with a post-processing linear scaling step for model
alignment has been shown to outperform error-based
fitness functions in generating symbolic regression
models. In this study, we compare the impact of four
evaluation strategies on relieving genetic programming
(GP) from learning coefficients in symbolic regression
and focusing on learning the more crucial model
structure. The results from 12 datasets, including ten
real-world tasks and two synthetic datasets, confirm
that all these strategies assist GP to varying degrees
in learning coefficients. Among the them, correlation
fitness with one-time linear scaling as
post-processing, due to be the most efficient while
bringing notable benefits to the performance, is the
recommended strategy to relieve GP from learning
coefficients.",
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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
Qi Chen
Bing Xue
Wolfgang Banzhaf
Mengjie Zhang
Citations