Study of Applying Macroevolutionary Genetic Programming to Concrete Strength Estimation

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{chen:290,
  author =       "Li Chen",
  title =        "Study of Applying Macroevolutionary Genetic
                 Programming to Concrete Strength Estimation",
  publisher =    "ASCE",
  year =         "2003",
  journal =      "Journal of Computing in Civil Engineering",
  volume =       "17",
  number =       "4",
  pages =        "290--294",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, civil
                 engineering computing, compressive strength, mixtures,
                 concrete",
  URL =          "http://link.aip.org/link/?QCP/17/290/1",
  DOI =          "doi:10.1061/(ASCE)0887-3801(2003)17:4(290)",
  abstract =     "This technical note is aimed at demonstrating a
                 mixture-proportioning problem, which uses the
                 macroevolutionary algorithm (MA) combined with genetic
                 programming (GP) to estimate the compressive strength
                 of high-performance concrete (HPC). GP provides system
                 identification in a transparent and structured way; a
                 fittest function type of experimental results will be
                 obtained automatically from this method. MA is a new
                 concept of species evolution at the higher level. It
                 could improve the capability of searching global optima
                 and avoid premature convergence during the selection
                 process of GP. In the study, two appropriate functions
                 have been found to represent the relationships between
                 different ingredients and the compressive strength. The
                 results show that this new model, MAGP, is better than
                 the traditional proportional selection GP for HPC
                 strength estimation.",
  notes =        "Dept. of Civil Engineering, Chung Hua Univ., Hsin Chu,
                 Taiwan 30067, Republic of China.",
}

Genetic Programming entries for Li Chen

Citations