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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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",
• chapter = "12",
• 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