Optimizing Thermostable Enzymes Production Using Multigene Symbolic Regression Genetic Programming
نویسندگان
چکیده
Thermostable enzymes production depends on number of attributes such as temperature, pH, inoculum, time and agitation. Optimizing 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 modeling problem. In this paper, we explore the use of Multigene Symbolic Regression GeneticProgramming to solve the production problem of a solvent, detergent, and thermotolerantlipase using the Newly IsolatedAcinetobacter 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.
منابع مشابه
On Symbolic Regression for Optimizing Thermostable Lipase Production
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 si...
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