- author = "Alaa Sheta and Rania Hiary and Hossam Faris and Nazeeh Ghatasheh",
- title = "Optimizing Thermostable Enzymes Production Using Multigene Symbolic Regression Genetic Programming",
- journal = "World Applied Sciences Journal",
- year = "2013",
- volume = "22",
- number = "4",
- pages = "485--493",
- keywords = "genetic algorithms, genetic programming, Multigene Genetic Programming, Symbolic regression, Optimisation, Thermostable Enzymes",
- ISSN = "1818-4952",
- publisher = "IDOSI",
-
URL = "
http://www.idosi.org/wasj/wasj22(4)13/6.pdf",
-
URL = "
http://www.idosi.org/wasj/wasj22(4)2013.htm",
- size = "9 pages",
- abstract = "Thermostable enzymes production depends on number of attributes such as temperature, pH, inoculum, time and agitation. Optimising the relationship between these attributes has been a challenge in biochemical research field. Machine learning techniques such as Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Genetic Algorithms (GAs) were used to solve the lipase activity modelling problem. In this paper, we explore the use of Multigene Symbolic Regression Genetic Programming to solve the production problem of a solvent, detergent, and thermo-tolerant lipase using the Newly Isolated Acinetobacter sp. in submerged and solid-state fermentation. Five attributes will be used to develop a mathematical model for the lipase activities. They are temperature, pH, inoculum, time and agitation. Genetic Programming shows promising results compared to reported results in the literature.",
-
notes = "GPTIPS Matlab
toolbox
http://www.idosi.org/wasj/wasj.htm",
