Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{oh:2021:Symmetry,
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author = "Sanghoun Oh and Woong-Hyun Suh and Chang-Wook Ahn",
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title = "{Self-Adaptive} Genetic Programming for Manufacturing
Big Data Analysis",
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journal = "Symmetry",
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year = "2021",
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volume = "13",
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number = "4",
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keywords = "genetic algorithms, genetic programming, manufacturing
big data analysis, self-adaptive genetic programming",
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ISSN = "2073-8994",
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URL = "https://www.mdpi.com/2073-8994/13/4/709",
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DOI = "doi:10.3390/sym13040709",
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abstract = "While black-box-based machine learning algorithms have
high analytical consistency in manufacturing big data
analysis, those algorithms experience difficulties in
interpreting the results based on the manufacturing
process principle. To overcome this limitation, we
present a Self-Adaptive Genetic Programming (SAGP) for
manufacturing big data analysis. In Genetic Programming
(GP), the solution is expressed as a relationship
between variables using mathematical symbols, and the
solution with the highest explanatory power is finally
selected. These advantages enable intuitive
interpretation on manufacturing mechanisms and derive
manufacturing principles based on the variables
represented by formulas. However, GP occasionally has
trouble adjusting the balance between high accuracy and
detailed interpretation due to an incommensurable
symmetry of the solutions. In order to effectively
handle this drawback, we apply the self-adaptive
mechanism into GP for managing crossover and mutation
probabilities regarding the complexity of tree
structure solutions in each generation. Our proposed
algorithm showed equal or superior performance compared
to other machine learning algorithms. We believe our
proposed method can be applied in diverse manufacturing
big data analytics in the future.",
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notes = "also known as \cite{sym13040709}",
- }
Genetic Programming entries for
Sanghoun Oh
Woong-Hyun Suh
Chang Wook Ahn
Citations