On Symbolic Regression for Optimizing Thermostable Lipase Production

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Faris2014,
  author =       "Hossam Faris and Alaa Sheta and Rania Hiary",
  title =        "On Symbolic Regression for Optimizing Thermostable
                 Lipase Production",
  journal =      "International Journal of Advanced Science and
                 Technology",
  year =         "2014",
  volume =       "63",
  number =       "11",
  pages =        "23--33",
  note =         "Special Issue on: Computational Optimisation and
                 Engineering Applications",
  keywords =     "genetic algorithms, genetic programming, symbolic
                 regression, lipase production, ANN, heuristiclab",
  ISSN =         "2005-4238",
  publisher =    "Science & Engineering Research Support soCiety, 20
                 Virginia Court, Sandy Bay, Tasmania, Australia.
                 ijast@sersc.org",
  URL =          "http://www.sersc.org/journals/IJAST/vol63/3.pdf",
  broken =       "http://dx.doi.org/10.14257/ijast.2014.63.03",
  size =         "12 pages",
  abstract =     "Theromostable lipases have wide range of
                 biotechnological applications in the industry.
                 Therefore, there is always high interest in
                 investigating their features and operating conditions.
                 However, Lipase production is a challenging and complex
                 process due to its nature which is highly dependent on
                 the conditions of the process such as temperature,
                 initial pH, incubation period, time, inoculum size and
                 agitation rate. Efficient optimisation of the process
                 is a common goal in order to improve the productivity
                 and reduce the costs. In this paper, we apply a
                 Symbolic Regression Genetic Programming (GP) approach
                 in order to develop a mathematical model which can
                 predict the lipase activities in submerged fermentation
                 (SmF) system. The developed GP model is compared with a
                 neural network model proposed in the literature. The
                 reported evaluation results show superiority of GP in
                 modelling and optimising the process.",
  notes =        "http://www.sersc.org/journals/IJAST/",
}

Genetic Programming entries for Hossam Faris Alaa Sheta Rania Hiary

Citations