Natural Selection of Asphalt Mix Stiffness Predictive Models with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Kasthurirangan Gopalakrishnan and Halil Ceylan and 
                 Sunghwan Kim and Siddhartha K. Khaitan",
  title =        "Natural Selection of Asphalt Mix Stiffness Predictive
                 Models with Genetic Programming",
  booktitle =    "ANNIE 2010, Intelligent Engineering Systems through
                 Artificial Neural Networks",
  year =         "2010",
  editor =       "Cihan H. Dagli",
  volume =       "20",
  pages =        "paper 48",
  address =      "St. Louis, Mo, USA",
  month =        nov # " 1-3",
  organisation = "Smart Engineering Systems Laboratory, Systems
                 Engineering Graduate Programs, Missouri University of
                 Science and Technology, 600 W. 14th St., Rolla, MO
                 65409 USA",
  publisher =    "ASME",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9780791859599",
  DOI =          "doi:10.1115/1.859599.paper48",
  abstract =     "Genetic Programming (GP) is a systematic,
                 domain-independent evolutionary computation technique
                 that stochastically evolves populations of computer
                 programs to perform a user-defined task. Similar to
                 Genetic Algorithms (GA) which evolves a population of
                 individuals to better ones, GP iteratively transforms a
                 population of computer programs into a new generation
                 of programs by applying biologically inspired
                 operations such as crossover, mutation, etc. In this
                 paper, a population of Hot-Mix Asphalt (HMA) dynamic
                 modulus stiffness prediction models is genetically
                 evolved to better ones by applying the principles of
                 genetic programming. The HMA dynamic modulus (|E*|),
                 one of the stiffness measures, is the primary HMA
                 material property input in the new Mechanistic
                 Empirical Pavement Design Guide (MEPDG) developed under
                 National Cooperative Highway Research Program (NCHRP)
                 1-37A (2004) for the American State Highway and
                 Transportation Officials (AASHTO). It is shown that the
                 evolved HMA model through GP is reasonably compact and
                 contains both linear terms and low-order non-linear
                 transformations of input variables for
  notes =        "
                 ASME Order Number: 859599",

Genetic Programming entries for Kasthurirangan Gopalakrishnan Halil Ceylan Sung Hwan Kim Siddhartha Kumar Khaitan