Assessment of artificial neural network and genetic programming as predictive tools
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- @Article{Gandomi:2015:AES,
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author = "Amir H. Gandomi and David A. Roke",
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title = "Assessment of artificial neural network and genetic
programming as predictive tools",
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journal = "Advances in Engineering Software",
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year = "2015",
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volume = "88",
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pages = "63--72",
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month = oct,
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keywords = "genetic algorithms, genetic programming, gene
expression programming, Artificial neural networks,
Over-fitting, Explicit formulation, Punching shear, RC
slabs, Parametric study",
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ISSN = "0965-9978",
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URL = "http://www.sciencedirect.com/science/article/pii/S0965997815000861",
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DOI = "doi:10.1016/j.advengsoft.2015.05.007",
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abstract = "Soft computing techniques have been widely used during
the last two decades for nonlinear system modeling,
specifically as predictive tools. In this study, the
performances of two well-known soft computing
predictive techniques, artificial neural network (ANN)
and genetic programming (GP), are evaluated based on
several criteria, including over-fitting potential. A
case study in punching shear prediction of RC slabs is
modelled here using a hybrid ANN (which includes
simulated annealing and multi-layer perception) and an
established GP variant called gene expression
programming. The ANN and GP results are compared to
values determined from several design codes. For more
verification, external validation and parametric
studies were also conducted. The results of this study
indicate that model acceptance criteria should include
engineering analysis from parametric studies.",
- }
Genetic Programming entries for
A H Gandomi
David Roke
Citations