Identifying Stochastic Nonlinear Dynamic Systems Using Multi-objective Hierarchical Fair Competition Parallel Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{DBLP:journals/mvl/YuanB10,
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author = "Xiao-lei Yuan and Yan Bai",
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title = "Identifying Stochastic Nonlinear Dynamic Systems Using
Multi-objective Hierarchical Fair Competition Parallel
Genetic Programming",
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journal = "Multiple-Valued Logic and Soft Computing",
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year = "2010",
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volume = "16",
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number = "6",
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pages = "643--660",
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note = "Special Issue: New Trends on Swarm Intelligent
Systems",
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keywords = "genetic algorithms, genetic programming, Nonlinear
dynamic system identification, Stochastic system
identification, NARX, NARMAX, HFC-GP, multi-objective
evolution",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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ISSN = "1542-3980",
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URL = "http://www.oldcitypublishing.com/journals/mvlsc-home/mvlsc-issue-contents/mvlsc-volume-16-number-6-2010/mvlsc-16-6-p-643-660/",
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broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC16.6abstracts/MVLSCv16n6p643-660Yuan.html",
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broken = "http://www.oldcitypublishing.com/MVLSC/MVLSCcontents/MVLSCv16n6contents.html",
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abstract = "A parallel evolutionary algorithm named hierarchical
fair competition genetic programming (HFC-GP) was
employed to identify stochastic nonlinear dynamic
systems. Nonlinear autoregressive with exogenous inputs
(NARX) and nonlinear autoregressive moving average with
exogenous inputs (NARMAX) polynomial models were used
to represent object systems. Multi-objective fitness
was used to restrict individual structure sizes during
the run. HFC-GP outperformed single-population GP and
traditional multi-population GP in combating premature
convergence. For all examples, good results were
achieved with simultaneous and accurate identification
of both structures and parameters. It can be concluded
that HFC-GP is very effective in combating premature
convergence and is superior to other exiting
identification methods.",
- }
Genetic Programming entries for
Xiao-Lei Yuan
Yan Bai
Citations