Abstract
This paper presents an improved Differential Evolution algorithm (IDE). It is aimed at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Nonetheless, due to the noisy data and difficulty of the system in estimating myriad of parameters, many computation algorithms face obstacles and require longer computational time to estimate the relevant parameters. The IDE proposed in this paper is a hybrid of a Differential Evolution algorithm (DE) and a Kalman Filter (KF). The outcome of IDE is proven to be superior than a Genetic Algorithm (GA) and DE. The results of IDE from this experiment show estimated optimal kinetic parameters values, shorter computation time and better accuracy of simulated results compared to the other estimation algorithms.
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Chong, C.K. et al. (2012). Using an Improved Differential Evolution Algorithm for Parameter Estimation to Simulate Glycolysis Pathway. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_85
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DOI: https://doi.org/10.1007/978-3-642-28765-7_85
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