Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems
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gp-bibliography.bib Revision:1.8098
- @InCollection{Alavi:2013:MWGTE,
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author = "Amir Hossein Alavi and Amir Hossein Gandomi and
Ali Mollahasani and Jafar {Bolouri Bazaz}",
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title = "Linear and Tree-Based Genetic Programming for Solving
Geotechnical Engineering Problems",
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editor = "Xin-She Yang and Amir Hossein Gandomi and
Siamak Talatahari and Amir Hossein Alavi",
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booktitle = "Metaheuristics in Water, Geotechnical and Transport
Engineering",
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publisher = "Elsevier",
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address = "Oxford",
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year = "2013",
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pages = "289--310",
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chapter = "12",
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keywords = "genetic algorithms, genetic programming, Tree-based
genetic programming, linear genetic programming,
geotechnical engineering, prediction",
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isbn13 = "978-0-12-398296-4",
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DOI = "doi:10.1016/B978-0-12-398296-4.00012-X",
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URL = "http://www.sciencedirect.com/science/article/pii/B978012398296400012X",
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abstract = "This chapter presents new approaches for solving
geotechnical engineering problems using classical
tree-based genetic programming (TGP) and linear genetic
programming (LGP). TGP and LGP are symbolic
optimisation techniques that create computer programs
to solve a problem using the principle of Darwinian
natural selection. Generally, they are supervised,
machine-learning techniques that search a program space
instead of a data space. Despite remarkable prediction
capabilities of the TGP and LGP approaches, the
contents of reported applications indicate that the
progress in their development is marginal and not
moving forward. The present study introduces a
state-of-the-art examination of TGP and LGP
applications in solving complex geotechnical
engineering problems that are beyond the computational
capability of traditional methods. In order to justify
the capabilities of these techniques, they are
systematically employed to formulate a typical
geotechnical engineering problem. For this aim,
effective angle of shearing resistance (phi) of soils
is formulated in terms of the physical properties of
soil. The validation of the TGP and LGP models is
verified using several statistical criteria. The
numerical example shows the superb accuracy,
efficiency, and great potential of TGP and LGP. The
models obtained using TGP and LGP can be used
efficiently as quick checks on solutions developed by
more time consuming and in-depth deterministic
analyses. The current research directions and issues
that need further attention in the future are
discussed. Keywords Tree-based genetic programming,
linear genetic programming geotechnical engineering,
prediction",
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notes = "Also known as \cite{Alavi2013289}",
- }
Genetic Programming entries for
A H Alavi
A H Gandomi
Ali Mollahasani
Jafar Bolouri Bazaz
Citations