Estimating Strength of Concrete Using a Grammatical Evolution

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

@InProceedings{conf/icnc/HsuCKWC09,
  title =        "Estimating Strength of Concrete Using a Grammatical
                 Evolution",
  author =       "Hsun-Hsin Hsu and Li Chen and Chang-Huan Kou and 
                 Tai-Sheng Wang and Sing-Han Chen",
  booktitle =    "Fifth International Conference on Natural Computation,
                 2009. ICNC '09",
  year =         "2009",
  editor =       "Haiying Wang and Kay Soon Low and Kexin Wei and 
                 Junqing Sun",
  month =        "14-16 " # aug,
  address =      "Tianjian, China",
  publisher =    "IEEE Computer Society",
  isbn13 =       "978-0-7695-3736-8",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  bibdate =      "2010-01-21",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icnc/icnc2009-3.html#RaoWY09",
  pages =        "134--138",
  DOI =          "doi:10.1109/ICNC.2009.492",
  abstract =     "The main purpose of this paper is to propose an
                 incorporating a grammatical evolution (GE) into the
                 genetic algorithm (GA), called GEGA, and apply it to
                 estimate the compressive strength of high-performance
                 concrete (HPC). The GE, an evolutionary programming
                 type system, automatically discovers complex
                 relationships between significant factors and the
                 strength of HPC in a more transparent way to enhance
                 our understanding of the mechanisms. A GA was used
                 afterward with GE to optimize the appropriate function
                 type and associated coefficients using over 1,000
                 examples for which experimental data were available.
                 The results show that this novel model, GEGA, can
                 obtain a highly nonlinear mathematical equation which
                 outperforms than the traditional multiple regression
                 analysis (RA) with lower estimating errors for
                 predicting the compressive strength of HPC.",
}

Genetic Programming entries for Hsun-Hsin Hsu Li Chen Chang-Huan Kou Tai-Sheng Wang Sing-Han Chen

Citations