Semantic Cluster Operator for Symbolic Regression and Its Applications
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{JEONG:2022:advengsoft,
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author = "Hoseong Jeong and Jae Hyun Kim and Seung-Ho Choi and
Seokin Lee and Inwook Heo and Kang Su Kim",
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title = "Semantic Cluster Operator for Symbolic Regression and
Its Applications",
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journal = "Advances in Engineering Software",
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volume = "172",
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pages = "103174",
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year = "2022",
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ISSN = "0965-9978",
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DOI = "doi:10.1016/j.advengsoft.2022.103174",
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URL = "https://www.sciencedirect.com/science/article/pii/S0965997822000850",
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keywords = "genetic algorithms, genetic programming, Automatic
code derivation, Semantic, Clustering, Iterated local
search, Symbolic regression",
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abstract = "a novel operator, semantic cluster operator, was
developed to overcome the low convergence performance
of genetic programming in symbolic regression. The main
strategy for steep convergence was to narrow search
space and scrutinize the narrowed search space using a
semantic cluster library. To demonstrate the success of
this idea, the computation time and offspring fitness
of the operator developed in this paper were compared
with those of exhaustive search. The computation time
of the operator was approximately 6percent of that of
the exhaustive search, and its offspring fitness was in
the top 0.5percent among all offspring derived from the
exhaustive search. In two application problems, derived
models from an algorithm using the operator showed high
prediction accuracy comparable to an artificial neural
network, random forest, and support vector machine
despite its simplicity.",
- }
Genetic Programming entries for
Hoseong Jeong
Jae-Hyun Kim
Seung-Ho Choi
Seokin Lee
Inwook Heo
Kang Su Kim
Citations