Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Improvement of genetic algorithm performance for identification of cultivation process models
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
Parameter Estimation of a Monod-Type Model Based on Genetic Algorithms and Sensitivity Analysis
Large-Scale Scientific Computing
Engineering Applications of Artificial Intelligence
Infeasibility Detection and SQP Methods for Nonlinear Optimization
SIAM Journal on Optimization
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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.