Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems

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@InCollection{Alavi:2013:MWGTE,
  author =       "Amir Hossein Alavi and Amir Hossein Gandomi and 
                 Ali Mollahasani and Jafar {Bolouri Bazaz}",
  title =        "Linear and Tree-Based Genetic Programming for Solving
                 Geotechnical Engineering Problems",
  editor =       "Xin-She Yang and Amir Hossein Gandomi and 
                 Siamak Talatahari and Amir Hossein Alavi",
  booktitle =    "Metaheuristics in Water, Geotechnical and Transport
                 Engineering",
  publisher =    "Elsevier",
  address =      "Oxford",
  year =         "2013",
  pages =        "289--310",
  keywords =     "genetic algorithms, genetic programming, Tree-based
                 genetic programming, linear genetic programming,
                 geotechnical engineering, prediction",
  isbn13 =       "978-0-12-398296-4",
  DOI =          "doi:10.1016/B978-0-12-398296-4.00012-X",
  URL =          "http://www.sciencedirect.com/science/article/pii/B978012398296400012X",
  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",
  notes =        "Also known as \cite{Alavi2013289}",
}

Genetic Programming entries for A H Alavi A H Gandomi Ali Mollahasani Jafar Bolouri Bazaz

Citations