Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
NMA'06 Proceedings of the 6th international conference on Numerical methods and applications
Feed rate profiles synthesis using genetic algorithms
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
A hybrid genetic algorithm for parameter identification of bioprocess models
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
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Mathematical models and their parameters used to describe cell behavior constitute the key problem of bioprocess modelling, in practical, in parameter estimation. The model building leads to an information deficiency and to non unique parameter identification. While searching for new, more adequate modeling concepts, methods which draw their initial inspiration from nature have received the early attention. One of the most common direct methods for global search is genetic algorithm. A system of six ordinary differential equations is proposed to model the variables of the regarded cultivation process. Parameter estimation is carried out using real experimental data set from an E. coli MC4110fed-batch cultivation process. In order to study and evaluate the links and magnitudes existing between the model parameters and variables sensitivity analysis is carried out. A procedure for consecutive estimation of four definite groups of model parameters based on sensitivity analysis is proposed. The application of that procedure and genetic algorithms leads to a successful parameter identification.