1997
DOI: 10.1007/bf02510391
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Application of genetic algorithms to parameter estimation of bioprocesses

Abstract: The paper explains the application of a genetic algorithm (GA) to the problem of estimating parameters for a kinetic model of a biologically reacting system. It is demonstrated that the GA is a powerful tool for quantifying the kinetic parameters using kinetic data. As the operation of the GA does not depend on the form of the model equation, it can be applied to the wide spectrum of kinetic modelling problems without any complex formulation procedure.

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Cited by 44 publications
(28 citation statements)
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“…This validation step was necessary due to differences between the current algorithm and that applied previously (Park et al, 1997). A model for denitrification by Pseudomonas denitrificans (ATCC 13867) under carbon-and/or oxidizednitrogen-limited conditions was used for this purpose (Kornaros et al, 1996).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…This validation step was necessary due to differences between the current algorithm and that applied previously (Park et al, 1997). A model for denitrification by Pseudomonas denitrificans (ATCC 13867) under carbon-and/or oxidizednitrogen-limited conditions was used for this purpose (Kornaros et al, 1996).…”
Section: Resultsmentioning
confidence: 99%
“…Unlike other nonlinear regression techniques, the GA does not require information about the gradient of the function, as differentiation is not required (Park et al, 1997). In fact, it is completely indifferent to the model being fit.…”
Section: Introductionmentioning
confidence: 99%
“…Thus the model is monotonic with respect to all of the parameters if the discretization window, [t 0 , t 2 ] lies within one of the intervals [0, 6]. Determining whether a specific discretization time window yields a model that is monotonic with respect to the parameters over that window is simply a function of the parameter ranges and the ranges of the variables over that window.…”
Section: Exploitation Of Monotonicitymentioning
confidence: 99%
“…Sometimes this purpose is also achieved by allowing the updating step to not only go "downhill" but also occasionally explore far-off regions of parameter space. Various strategies used in global minimization may be applied to this problem [6,7], but regardless of the specific form of the above algorithm, the overall computational effort is typically large. The final result of such a procedure is a point estimate in parameter space that (hopefully) is a best fit of the model to these particular data.…”
Section: Introductionmentioning
confidence: 99%
“…Parameter estimation is a recurrent issue in the model building process. It deals with the finding of the numerical values characterizing the mathematical representation of a given system from experimental data [9]. A key feature of the experimental measurements is that they must come from variables representing their main features both at a given particular time as well as along its evolution over time [10].…”
Section: Introductionmentioning
confidence: 99%