Improved Approach of Genetic Programming and Applications for Data Mining
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{conf/icnc/ZhangC06,
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title = "Improved Approach of Genetic Programming and
Applications for Data Mining",
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author = "Yongqiang Zhang and Huashan Chen",
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booktitle = "Advances in Natural Computation, Second International
Conference, {ICNC} 2006, Proceedings, Part {I}",
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publisher = "Springer",
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year = "2006",
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volume = "4221",
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editor = "Licheng Jiao and Lipo Wang and Xinbo Gao and
Jing Liu and Feng Wu",
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pages = "816--819",
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series = "Lecture Notes in Computer Science",
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address = "Xi'an, China",
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month = sep # " 24-28",
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keywords = "genetic algorithms, genetic programming, dynamic tree
depth, ordinary differential equation, data mining",
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bibdate = "2006-11-29",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icnc/icnc2006-1.html#ZhangC06",
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ISBN = "3-540-45901-4",
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DOI = "doi:10.1007/11881070_108",
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abstract = "Genetic Programming (GP for short) is applied to a
benchmark of the data fitting and forecasting problems.
However, the increasing size of the trees may block the
speed of problems reaching best solution and affect the
fitness of best solutions. In this view, this paper
adopts the dynamic maximum tree depth to constraining
the complexity of programs, which can be useful to
avoid the typical undesirable growth of program size.
For more precise data fitting and forecasting, the
arithmetic operator of ordinary differential equations
has been made use of. To testify what and how they
work, an example of service life data series about
electron parts is taken. The results indicate the
feasibility and availability of improved GP, which can
be applied successfully for data fitting and
forecasting problems to some extent.",
- }
Genetic Programming entries for
Yongqiang Zhang
Huashan Chen
Citations