Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{journals/sp/XuHSL17,
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author = "Lixiong Xu and Yuan Huang and Xiaodong Shen and
Yang Liu2",
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title = "Parallelizing Gene Expression Programming Algorithm in
Enabling Large-Scale Classification",
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journal = "Scientific Programming",
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year = "2017",
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volume = "2017",
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pages = "5081526:1--5081526:10",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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bibdate = "2017-05-28",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/sp/sp2017.html#XuHSL17",
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URL = "http://downloads.hindawi.com/journals/sp/2017/5081526.pdf",
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DOI = "doi:10.1155/2017/5081526",
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abstract = "As one of the most effective function mining
algorithms, Gene Expression Programming (GEP) algorithm
has been widely used in classification, pattern
recognition, prediction, and other research fields.
Based on the self-evolution, GEP is able to mine an
optimal function for dealing with further complicated
tasks. However, in big data researches, GEP encounters
low efficiency issue due to its long time mining
processes. To improve the efficiency of GEP in big data
researches especially for processing large-scale
classification tasks, this paper presents a
parallelized GEP algorithm using MapReduce computing
model. The experimental results show that the presented
algorithm is scalable and efficient for processing
large-scale classification tasks.",
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notes = "The Iris Dataset, The Wine Dataset",
- }
Genetic Programming entries for
Lixiong Xu
Yuan Huang
Xiaodong Shen
Yang Liu2
Citations