Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP

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@Article{Nazari:2013:ESA,
  author =       "Ali Nazari and F. {Pacheco Torgal}",
  title =        "Modeling the compressive strength of geopolymeric
                 binders by gene expression programming-GEP",
  journal =      "Expert Systems with Applications",
  year =         "2013",
  volume =       "40",
  number =       "14",
  pages =        "5427--5438",
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming, Geopolymers, Compressive
                 strength",
  ISSN =         "0957-4174",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417413002510",
  DOI =          "doi:10.1016/j.eswa.2013.04.014",
  size =         "12 pages",
  abstract =     "Abstract GEP has been employed in this work to model
                 the compressive strength of different types of
                 geopolymers through six different schemes. The
                 differences between the models were in their linking
                 functions, number of genes, chromosomes and head sizes.
                 The curing time, Ca(OH)2 content, the amount of
                 superplasticizer, NaOH concentration, mold type,
                 aluminosilicate source and H2O/Na2O molar ratio were
                 the seven input parameters considered in the
                 construction of the models to evaluate the compressive
                 strength of geopolymers. A total number of 399
                 input-target pairs were collected from the literature,
                 randomly divided into 299 and 100 sets and were trained
                 and tested, respectively. The best performance model
                 had 6 genes, 14 head size, 40 chromosomes and
                 multiplication as linking function. This was shown by
                 the absolute fraction of variance, the absolute
                 percentage error and the root mean square error. These
                 were of 0.9556, 2.4601 and 3.4716 for training phase,
                 respectively and 0.9483, 2.8456 and 3.7959 for testing
                 phase, respectively. However, another model with 7
                 genes, 12 head size, 30 chromosomes and addition as
                 linking function showed suitable results with the
                 absolute fraction of variance, the absolute percentage
                 error and the root mean square of 0.9547, 2.5665 and
                 3.4360 for training phase, respectively and 0.9466,
                 2.8020 and 3.8047 for testing phase, respectively.
                 These models showed that gene expression programming
                 has a strong potential for predicting the compressive
                 strength of different types of geopolymers in the
                 considered range.",
}

Genetic Programming entries for Ali Nazari Fernando Pacheco Torgal

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