A Genetic Programming-Based Approach for the Performance Characteristics Assessment of Stabilized Soil

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InCollection{books/sp/chiong2012/AlaviGM12,
  author =       "Amir Hossein Alavi and Amir Hossein Gandomi and 
                 Ali Mollahasani",
  title =        "A Genetic Programming-Based Approach for the
                 Performance Characteristics Assessment of Stabilized
                 Soil",
  booktitle =    "Variants of Evolutionary Algorithms for Real-World
                 Applications",
  publisher =    "Springer",
  year =         "2012",
  editor =       "Raymond Chiong and Thomas Weise and 
                 Zbigniew Michalewicz",
  chapter =      "9",
  pages =        "343--376",
  keywords =     "genetic algorithms, genetic programming, Chemical
                 stabilisation, Simulated annealing, Nonlinear
                 modelling",
  isbn13 =       "978-3-642-23423-1",
  DOI =          "doi:10.1007/978-3-642-23424-8_11",
  abstract =     "This chapter presents a variant of genetic
                 programming, namely linear genetic programming (LGP),
                 and a hybrid search algorithm coupling LGP and
                 simulated annealing (SA), called LGP/SA, to predict the
                 performance characteristics of stabilised soil. LGP and
                 LGP/SA relate the unconfined compressive strength
                 (UCS), maximum dry density (MDD), and optimum moisture
                 content (OMC) metrics of stabilised soil to the
                 properties of the natural soil as well as the types and
                 quantities of stabilizing additives. Different sets of
                 LGP and LGP/SA-based prediction models have been
                 separately developed. The contributions of the
                 parameters affecting UCS, MDD, and OMC are evaluated
                 through a sensitivity analysis. A subsequent parametric
                 analysis is carried out and the trends of the results
                 are compared with previous studies. A comprehensive set
                 of data obtained from the literature has been used for
                 developing the models. Experimental results confirm
                 that the accuracy of the proposed models is
                 satisfactory. In particular, the LGP-based models are
                 found to be more accurate than the LGP/SA-based
                 models.",
  affiliation =  "School of Civil Engineering, Iran University of
                 Science and Technology, Tehran, Iran",
  bibdate =      "2011-11-16",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/books/collections/Chiong2012.html#AlaviGM12",
}

Genetic Programming entries for A H Alavi A H Gandomi Ali Mollahasani

Citations