Expression Programming Techniques for Formulation of Structural Engineering Systems
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- @InCollection{Gandomi:2013:MASI.18,
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author = "Amir Hossein Gandomi and Amir Hossein Alavi",
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title = "Expression Programming Techniques for Formulation of
Structural Engineering Systems",
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editor = "Amir Hossein Gandomi and Xin-She Yang and
Siamak Talatahari and Amir Hossein Alavi",
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booktitle = "Metaheuristic Applications in Structures and
Infrastructures",
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publisher = "Elsevier",
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address = "Oxford",
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year = "2013",
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chapter = "18",
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pages = "439--455",
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keywords = "genetic algorithms, genetic programming, Gene
expression programming, Data mining, structural
engineering, expression programming, prediction",
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isbn13 = "978-0-12-398364-0",
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DOI = "doi:10.1016/B978-0-12-398364-0.00018-8",
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URL = "http://www.sciencedirect.com/science/article/pii/B9780123983640000188",
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abstract = "Modelling the real behaviour of structural systems is
very difficult because of the multivariable
dependencies of materials and structural responses. To
deal with this complex behavior, simplifying
assumptions are commonly incorporated into the
development of the conventional methods. This may lead
to very large errors. The present study investigates
the simulation capabilities of expression programming
(EP) techniques by applying them to complex structural
engineering problems. Gene expression programming (GEP)
and multiexpression programming (MEP) are the employed
EP systems. Compared with traditional genetic
programming, the EP techniques are more compatible with
computer architectures. This results in a significant
speedup in their execution. GEP and MEP are
substantially useful in deriving empirical models for
characterising the behavior of structural engineering
systems by directly extracting the knowledge contained
in the experimental data. The problems analysed herein
include the following: (i) prediction of shear strength
of reinforced concrete columns and (ii) prediction of
hysteretic energy demand in steel moment resisting
frames. The results obtained by GEP and MEP are
compared with those provided by other equations
presented in the literature and found to be more
accurate. The new approaches of GEP and MEP overcome
the shortcomings of different methods previously
presented in the literature for the analysis of
structural engineering systems. Contrary to artificial
neural networks and many other soft computing tools,
GEP and MEP provide reasonably simplified prediction
equations. The derived equations can be used for
routine design practice. Unlike the conventional
methods, GEP and MEP do not require any simplifying
assumptions in developing the models.",
- }
Genetic Programming entries for
A H Gandomi
A H Alavi
Citations