On Symbolic Regression for Optimizing Thermostable Lipase Production
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- @Article{Faris2014,
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author = "Hossam Faris and Alaa Sheta and Rania Hiary",
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title = "On Symbolic Regression for Optimizing Thermostable
Lipase Production",
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journal = "International Journal of Advanced Science and
Technology",
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year = "2014",
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volume = "63",
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number = "11",
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pages = "23--33",
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note = "Special Issue on: Computational Optimisation and
Engineering Applications",
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keywords = "genetic algorithms, genetic programming, symbolic
regression, lipase production, ANN, heuristiclab",
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ISSN = "2005-4238",
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publisher = "Science & Engineering Research Support soCiety, 20
Virginia Court, Sandy Bay, Tasmania, Australia.
ijast@sersc.org",
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URL = "http://www.sersc.org/journals/IJAST/vol63/3.pdf",
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broken = "http://dx.doi.org/10.14257/ijast.2014.63.03",
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size = "12 pages",
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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.",
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notes = "http://www.sersc.org/journals/IJAST/",
- }
Genetic Programming entries for
Hossam Faris
Alaa Sheta
Rania Hiary
Citations