Genetic programming approach to predict a model acidolysis system

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@Article{Ciftci:2009:EAEI,
  author =       "Ozan Nazim Ciftci and Sibel Fadiloglu and 
                 Fahrettin Gogus and Aytac Guven",
  title =        "Genetic programming approach to predict a model
                 acidolysis system",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2009",
  volume =       "22",
  pages =        "759--766",
  number =       "4-5",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Acidolysis",
  DOI =          "doi:10.1016/j.engappai.2009.01.010",
  ISSN =         "0952-1976",
  URL =          "http://www.sciencedirect.com/science/article/B6V2M-4VTVJNC-2/2/5894a9c11ade2e94a1ff09a18b63a062",
  abstract =     "This paper models acidolysis of triolein and palmitic
                 acid under the catalysis of immobilized sn-1,3 specific
                 lipase. A gene-expression programming (GEP), which is
                 an extension to genetic programming (GP)-based model
                 was developed for the prediction of the concentration
                 of major reaction products of this reaction
                 (1-palmitoyl-2,3-oleoyl-glycerol (POO),
                 1,3-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein
                 (OOO). Substrate ratio (SR), reaction temperature (T)
                 and reaction time (t) were used as input parameters.
                 The predicted models were able to predict the progress
                 of the reactions with a mean standard error (MSE) of
                 less than 1.0 and R of 0.978. Explicit formulation of
                 proposed GEP models was also presented. Considerable
                 good performance was achieved in modeling acidolysis
                 reaction by using GEP. The predictions of proposed GEP
                 models were compared to those of neural network (NN)
                 modeling, and strictly good agreement was observed
                 between the two predictions. Statistics and scatter
                 plots indicate that the new GEP formulations can be an
                 alternative to experimental models.",
}

Genetic Programming entries for Ozan Nazim Ciftci Sibel Fadiloglu Fahrettin Gogus Aytac Guven

Citations