Abstract
In this paper a hybrid scheme using GA and SQP method is introduced. In the hybrid GA-SQP the role of the GA is to explore the search place in order to either isolate the most promising region of the search space. The role of the SQP is to exploit the information gathered by the GA. To demonstrate the usefulness of the presented approach, two cases for parameter identification of different complexity are considered. The hybrid scheme is applied for modeling of E. coli MC4110 fed-batch cultivation process. The results show that the GA-SQP takes the advantages of both GA’s global search ability and SQP’s local search ability, hence enhances the overall search ability and computational efficiency.
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Roeva, O. (2012). A Hybrid Genetic Algorithm for Parameter Identification of Bioprocess Models. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2011. Lecture Notes in Computer Science, vol 7116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29843-1_28
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DOI: https://doi.org/10.1007/978-3-642-29843-1_28
Publisher Name: Springer, Berlin, Heidelberg
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