Assessment of artificial neural network and genetic programming as predictive tools

Gandomi, AH; Roke, DA

Assessment of artificial neural network and genetic programming as predictive tools

Gandomi, AH Roke, DA

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Advances in Engineering Software, 2015, 88, pp. 63-72
Issue Date:: 2015-06-22

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Field	Value	Language
dc.contributor.author	Gandomi, AH
dc.contributor.author	Roke, DA
dc.date.accessioned	2023-03-28T20:56:13Z
dc.date.available	2023-03-28T20:56:13Z
dc.date.issued	2015-06-22
dc.identifier.citation	Advances in Engineering Software, 2015, 88, pp. 63-72
dc.identifier.issn	0965-9978
dc.identifier.issn	1873-5339
dc.identifier.uri	http://hdl.handle.net/10453/168744
dc.description.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 modeled 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.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Advances in Engineering Software
dc.relation.isbasedon	10.1016/j.advengsoft.2015.05.007
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Applied Mathematics
dc.title	Assessment of artificial neural network and genetic programming as predictive tools
dc.type	Journal Article
utslib.citation.volume	88
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-28T20:56:11Z
pubs.publication-status	Published
pubs.volume	88

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 modeled 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.

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http://hdl.handle.net/10453/168744