A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{chen:2024:CEC,
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author = "Xinan Chen and Wenjie Yi and Ruibin Bai and
Rong Qu and Yaochu Jin",
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title = "A Hierarchical Cooperative Genetic Programming for
Complex Piecewise Symbolic Regression",
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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, Adaptation
models, Computational modeling, Sociology, Evolutionary
computation, Data models, Regression analysis, symbolic
regression, hierarchical structure, evolutionary
algorithm",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611754",
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abstract = "In regression analysis, methodologies range from
black-box approaches like artificial neural networks to
white-box techniques like symbolic regression. Renowned
for its trans-parency and interpretability, symbolic
regression has become increasingly prominent in
elucidating complex data relationships. Nevertheless,
its effectiveness in managing complex piecewise
symbolic regression tasks poses significant challenges.
This paper introduces a novel Hierarchical Cooperative
Genetic Program-ming (HCGP) framework to address this
issue. The HCGP model uses a unique hierarchical
structure, incorporating dual cooperative genetic
programming (GP) populations. This innovative design
significantly enhances the capability to solve complex
piecewise symbolic regression problems. Implementing a
scenario-based GP is central to the HCGP framework,
which strategically selects the appropriate underlying
calculation GP. This feature enables the system to
autonomously learn and adapt to complex scenarios,
selecting the most suitable calculation GPs for each
case. Our HCGP approach distinguishes itself from
traditional and state-of-the-art methods. It
demonstrates particular proficiency in modelling
piecewise expressions within complex scenarios. The
empirical evaluation of our model, conducted using
benchmark datasets, has exhibited its superior accuracy
and computational efficiency. This progress emphasizes
the potential of HCGP in sophisticated data modelling
and marks a substantial advancement in a hierarchical
structure in complex piecewise symbolic regression.",
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notes = "also known as \cite{10611754}
WCCI 2024",
- }
Genetic Programming entries for
Xinan Chen
Wenjie Yi
Ruibin Bai
Rong Qu
Yaochu Jin
Citations