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Genetic Algorithms for Parameter Estimation in Mathematical Modeling of Glucose Metabolism by Umberto Morbiducci; Andrea Tura; Mauro Grigioni is a Medicine article available to read on EtoBox.

What is Genetic Algorithms for Parameter Estimation in Mathematical Modeling of Glucose Metabolism about?

Direct measurement of hormones secretion and kinetics in glucose metabolism is not feasible in the clinical practice, being highly invasive. As their knowledge is important in the diagnosis of metabolic disorders, thanks to mathematical models based on non-invasive tests, estimation of hormones behaviour is obtained. Unfortunately, traditional model estimation can suffer for convergence problems, and it can be strongly dependent on the parameters initial value. To overcome these limitations, Genetic algorithms (GAs) were tested on a group of 49 subjects. The stochastic nature of GAs allowed overcoming the initialization problem. Moreover, GAs significantly improved the accuracy of fit.

Who reads Genetic Algorithms for Parameter Estimation in Mathematical Modeling of Glucose Metabolism?

Author

Umberto Morbiducci; Andrea Tura; Mauro Grigioni

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0010-4825)

Published

2005

Language

EN

Field

Medicine (Physical Sciences)