Automatic Generation of English-Japanese Translation Pattern Utilizing Genetic Programming Technique

2010 ◽  
Vol 130 (6) ◽  
pp. 1078-1088
Author(s):  
Koki Matsumura ◽  
Yuji Tamekuni ◽  
Shuhei Kimura
Keyword(s):  
Genetic Programming ◽  
Automatic Generation ◽  
Programming Technique ◽  
Japanese Translation

An application of the genetic programming technique to strategy development

2009 ◽  
Vol 36 (3) ◽  
pp. 5157-5161 ◽  
Author(s):  
Koun-Tem Sun ◽  
Yi-Chun Lin ◽  
Cheng-Yen Wu ◽  
Yueh-Min Huang
Keyword(s):  
Genetic Programming ◽  
Strategy Development ◽  
Programming Technique

scholarly journals Automatic Generation of Control Programs for Walking Robots Using Genetic Programming

2002 ◽  
pp. 258-267 ◽  
Author(s):  
Jens Busch ◽  
Jens Ziegler ◽  
Christian Aue ◽  
Andree Ross ◽  
Daniel Sawitzki ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Automatic Generation ◽  
Walking Robots ◽  
Control Programs

An Improved Genetic Programming Technique for the Classification of Raman Spectra

2007 ◽  
pp. 181-192
Author(s):  
Kenneth Hennessy ◽  
Michael G. Madden ◽  
Jennifer Conroy ◽  
Alan G. Ryder
Keyword(s):  
Genetic Programming ◽  
Raman Spectra ◽  
Programming Technique

Using genetic programming to transform from Australian to USDA/FAO soil particle-size classification system

Soil Research ◽  
2012 ◽  
Vol 50 (6) ◽  
pp. 443 ◽  
Author(s):  
José Padarian ◽  
Budiman Minasny ◽  
Alex McBratney
Keyword(s):  
Particle Size ◽  
Genetic Programming ◽  
Classification Systems ◽  
Soil Survey ◽  
Pedotransfer Functions ◽  
Programming Technique ◽  
Size Classification ◽  
The Difference ◽  
Prior Models ◽  
Australian Soil

The difference between the International (adopted by Australia) and the USDA/FAO particle-size classification systems is the limit between silt and sand fractions (20 μm for the International and 50 µm for the USDA/FAO). In order to work with pedotransfer functions generated under the USDA/FAO system with Australian soil survey data, a conversion should be attempted. The aim of this work is to improve prior models using larger datasets and a genetic programming technique, in the form of a symbolic regression. The 2–50 µm fraction was predicted using a USDA dataset which included both particle-size classification systems. The presented model reduced the root mean square error (%) by 14.96 and 23.62% (IGBP-DIS dataset and Australian dataset, respectively), compared with the previous model.

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