HS-Model: a hierarchical statistical subtree-generating model for genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{WenLZ:2009:GEC,
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author = "Lingyun Wen and Guiquan Liu and Yinghai Zhao",
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title = "HS-Model: a hierarchical statistical
subtree-generating model for genetic programming",
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booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
Genetic and Evolutionary Computation",
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year = "2009",
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editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and
Darrell Whitley and Yongsheng Ding",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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pages = "1005--1008",
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address = "Shanghai, China",
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organisation = "SigEvo",
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DOI = "doi:10.1145/1543834.1543994",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = jun # " 12-14",
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isbn13 = "978-1-60558-326-6",
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keywords = "genetic algorithms, genetic programming, Poster",
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abstract = "In genetic programming with subtrees, two issues are
crucial: how to acquire promising subtrees efficiently
and how to keep these subtrees to be used repeatedly in
the evolutional process. In this paper, we propose a
hierarchical statistical model for program trees, named
HS-Model, to deal with both the above issues. The
HS-Model conducts statistic analysis of the current
population and generates superior subtrees
automatically with efficiency. The HS-Model leaves out
the tedious operations to keep the promising subtrees
for reusing and also omits updating the subtree library
according to certain criterion. Experimental results on
solving the classical artificial ant problem proved the
effectiveness and the efficiency of our proposed
method.",
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notes = "Also known as \cite{DBLP:conf/gecco/WenLZ09} part of
\cite{DBLP:conf/gec/2009}",
- }
Genetic Programming entries for
Lingyun Wen
Guiquan Liu
Yinghai Zhao
Citations