A Novel Symbolic Regressor Enhancer Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{chiang:2024:CEC,
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author = "Tu-Chin Chiang and Chi-Hsien Chang and Tian-Li Yu",
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title = "A Novel Symbolic Regressor Enhancer Using Genetic
Programming",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Databases,
Evolutionary computation, Benchmark testing,
Syntactics, Programming, Polynomials, Symbolic
regression",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612124",
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abstract = "This paper proposes a framework combining genetic
programming (GP) with other symbolic regression (SR)
methods, called the symbolic regressor enhancer (SRE).
The basic idea is to use the syntax tree of the
expression obtained from other SR methods to improve
both the efficiency and the quality of the evolutionary
procedure. Specifically, this paper investigates on the
different ways of hybridization, selection, and
crossover to assemble the proposed SRE. The
effectiveness of SRE is demonstrated with the Taylor
polynomial, the fast function extraction, and the
GP-based SR methods, including Operon, the GP variant
of gene-pool optimal mixing evolutionary algorithm, the
epsilon-Iexicase selection, and gplearn. Out of 28
benchmarks from the SR benchmark and the Feynman SR
database, the statistical test indicates that SRE
applied to each selected SR method significantly
outperforms the respective SR method in at least 8 and
at most 24 benchmarks.",
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notes = "also known as \cite{10612124}
WCCI 2024",
- }
Genetic Programming entries for
Tu-Chin Chiang
Chi-Hsien Chang
Tian-Li Yu
Citations