An improved Gene Expression Programming approach for symbolic regression problems
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- @Article{journals/ijon/PengYQHS14,
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author = "YuZhong Peng and ChangAn Yuan and Xiao Qin and
JiangTao Huang and YaBing Shi",
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title = "An improved Gene Expression Programming approach for
symbolic regression problems",
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journal = "Neurocomputing",
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year = "2014",
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volume = "137",
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pages = "293--301",
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, Evolutionary algorithm,
Symbolic regression, Data modeling",
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bibdate = "2014-05-20",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/ijon/ijon137.html#PengYQHS14",
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URL = "
http://dx.doi.org/10.1016/j.neucom.2013.05.062",
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abstract = "Gene Expression Programming (GEP) is a powerful
evolutionary method for knowledge discovery and model
learning. Based on the basic GEP algorithm, this paper
proposes an improved algorithm named S_GEP, which is
especially suitable for dealing with symbolic
regression problems. The major advantages for this
S_GEP method include: (1) A new method for evaluating
individual without expression tree; (2) a corresponding
expression tree construction schema for the new
evaluating individual method if required by some
special complex problems; and (3) a new approach for
manipulating numeric constants so as to improve the
convergence. A thorough comparative study between our
proposed S_GEP method with the primitive GEP, as well
as other methods are included in this paper. The
comparative results show that the proposed S_GEP method
can significantly improve the GEP performance. Several
well-studied benchmark test cases and real-world test
cases demonstrate the efficiency and capability of our
proposed S_GEP for symbolic regression problems.",
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notes = "Key Lab of Scientific Computing and Intelligent
Information Processing in Universities of Guangxi,
Guangxi Teachers Education University, Nanning 530001,
China",
- }
Genetic Programming entries for
Yu-zhong Peng
Chang-an Yuan
Xiao Qin
Jiangtao Huang
YaBing Shi
Citations