Convergency of Genetic Regression in Data Mining based on Gene Expression Programming and Optimized Solution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Yuan:2006:IJCA,
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author = "Chang an Yuan and Chang jie Tang and Y. Wen and
Jie Zuo and Jing Peng and Jian jun Hu",
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title = "Convergency of Genetic Regression in Data Mining based
on Gene Expression Programming and Optimized Solution",
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journal = "International Journal of Computers and Applications",
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year = "2006",
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volume = "28",
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number = "4",
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, Data mining, genetic
regression, convergency in probability, minimised
residual sum of square genetic algorithm",
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DOI = "doi:10.2316/Journal.202.2006.4.202-1831",
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abstract = "This paper investigates the convergency of the
probability of genetic regression in data mining based
on Gene Expression Programming (GEP) and the proposed
optimised algorithm based on GEP Minimised Residual Sum
of Square Genetic Algorithm (MRSSGA). By extensive
experiments on Genetic Programming (GP), GEP and MRSSGA
show: (1) that all algorithms could find the target
function from the data with low noise; (2) by comparing
the convergency speeds, new algorithms in GEP are 20
times faster than GP and MRSSGA and 60 times faster
than GP for simple data; (3) for very complex data with
an unknown function type, GEP and MRSSGA are
respectively 900 and 1800 times faster than GP at
finding ideal functions; and (4) aimed at the actual
data, the precision of models created by using genetic
regression methods is much more exact than traditional
methods.",
- }
Genetic Programming entries for
Chang-an Yuan
Changjie Tang
Y Wen
Jie Zuo
Jing Peng
Jianjun Hu
Citations