Earticle

초록

영어

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 optimization 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 modeling and optimizing the process.

목차

Abstract
 1. Introduction
 2. Symbolic Regression via Genetic Programming
 3. Model Evaluation
 4. Materials and Methods
 5. Data collection
 6. Experiments and Results
 7. Conclusion
 References

키워드

저자

• Hossam Faris [ King Abdulla II School for Information Technology, The University of Jordan, Amman, Jordan ]
• Alaa Sheta [ Computers and Systems Department, Electronics Research Institute (ERI), Cairo, Egypt ]
• Rania Hiary [ Information and Communication Technology in Education Program, Al-Albait University, Mafrag, Jordan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605