EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems
Created by W.Langdon from
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- @Article{journals/tetci/HuangLMDW19,
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title = "{EGEP}: An Event Tracker Enhanced Gene Expression
Programming for Data Driven System Engineering
Problems",
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author = "Zhengwen Huang and Maozhen Li and Alireza Mousavi and
Morad Danishvar and Zidong Wang",
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journal = "IEEE Transactions on Emerging Topics in Computational
Intelligence",
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year = "2019",
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number = "2",
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volume = "3",
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pages = "117--126",
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month = apr,
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keywords = "genetic algorithms, genetic programming, gene
expression programming, schema theory,event tracker,
data driven system engineering, Z-fact0r",
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bibdate = "2020-07-14",
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DOI = "doi:10.1109/TETCI.2018.2864724",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/tetci/tetci3.html#HuangLMDW19",
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size = "10 pages",
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abstract = "Gene expression programming (GEP) is a data driven
evolutionary technique that is well suited to
correlation mining of system components. With the rapid
development of industry 4.0, the number of components
in a complex industrial system has increased
significantly with a high complexity of correlations.
As a result, a major challenge in employing GEP to
solve system engineering problems lies in computation
efficiency of the evolution process. To address this
challenge, this paper presents EGEP, an event tracker
enhanced GEP, which filters irrelevant system
components to ensure the evolution process to converge
quickly. Furthermore, we introduce three theorems to
mathematically validate the effectiveness of EGEP based
on a GEP schema theory. Experiment results also confirm
that EGEP outperforms the GEP with a shorter
computation time in an evolution.",
- }
Genetic Programming entries for
Zhengwen Huang
Maozhen Li
Alireza Mousavi
Morad Danishvar
Zidong Wang
Citations