Parallel linear genetic programming for multi-class classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{Downey:2012:GPEM,
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author = "Carlton Downey and Mengjie Zhang and Jing Liu",
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title = "Parallel linear genetic programming for multi-class
classification",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2012",
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volume = "13",
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number = "3",
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pages = "275--304",
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month = sep,
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note = "Special issue on selected papers from the 2011
European conference on genetic programming",
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keywords = "genetic algorithms, genetic programming, Linear
genetic programming, Classification, Parallel
structure, Caching",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-012-9162-9",
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size = "30 pages",
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abstract = "Motivated by biological inspiration and the issue of
instruction disruption, we develop a new form of Linear
Genetic Programming (LGP) called Parallel LGP (PLGP)
for classification problems. PLGP programs consist of
multiple lists of instructions. These lists are
executed in parallel after which the resulting vectors
are combined to produce the classification result. PLGP
limits the disruptive effects of crossover and
mutation, which allows PLGP to significantly outperform
regular LGP. Furthermore, PLGP programs are naturally
suited to caching due to their parallel architecture.
Although caching techniques have been used in tree
based GP, to our knowledge, there are no caching
techniques specifically developed for LGP. Thus, a
novel caching technique is also developed with the
intrinsic properties of PLGP in mind, which can
decrease fitness evaluation time by almost an order of
magnitude for the classification problems.",
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notes = "Jing Liu = http://see.xidian.edu.cn/faculty/liujing/
EuroGP 2011 \cite{Silva:2011:GP}",
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affiliation = "School of Engineering and Computer Science, Victoria
University of Wellington, Wellington, New Zealand",
- }
Genetic Programming entries for
Carlton Downey
Mengjie Zhang
Jing Liu
Citations