Prediction of compressive and tensile strength of limestone via genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Adil Baykasoglu and Hamza Gullu and Hanifi Canakci and 
                 Lale Ozbakir",
  title =        "Prediction of compressive and tensile strength of
                 limestone via genetic programming",
  journal =      "Expert Systems with Applications",
  volume =       "35",
  number =       "1-2",
  pages =        "111--123",
  year =         "2008",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2007.06.006",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, multi
                 expression programming, gene expression programming,
                 Prediction, Limestone, Strength of materials",
  abstract =     "Accurate determination of compressive and tensile
                 strength of limestone is an important subject for the
                 design of geotechnical structures. Although there are
                 several classical approaches in the literature for
                 strength prediction their predictive accuracy is
                 generally not satisfactory. The trend in the literature
                 is to apply artificial intelligence based soft
                 computing techniques for complex prediction problems.
                 Artificial neural networks which are a member of soft
                 computing techniques were applied to strength
                 prediction of several types of rocks in the literature
                 with considerable success. Although artificial neural
                 networks are successful in prediction, their inability
                 to explicitly produce prediction equations can create
                 difficulty in practical circumstances. Another member
                 of soft computing family which is known as genetic
                 programming can be a very useful candidate to overcome
                 this problem. Genetic programming based approaches are
                 not yet applied to the strength prediction of
                 limestone. This paper makes an attempt to apply a
                 promising set of genetic programming techniques which
                 are known as multi expression programming (MEP), gene
                 expression programming (GEP) and linear genetic
                 programming (LGP) to the uniaxial compressive strength
                 (UCS) and tensile strength prediction of chalky and
                 clayey soft limestone. The data for strength prediction
                 were generated experimentally in the University of
                 Gaziantep civil engineering laboratories by using
                 limestone samples collected from Gaziantep region of

Genetic Programming entries for Adil Baykasoglu Hamza Gullu Hanifi Canakci Lale Ozbakir