![](/images/graphics-bg.png)
Tuning Genetic Algorithm Parameters to Improve Convergence Time
Joint Authors
Pencheva, Tania
Angelova, Maria
Source
International Journal of Chemical Engineering
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-09-06
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Engineering Sciences and Information Technology
Abstract EN
Fermentation processes by nature are complex, time-varying, and highly nonlinear.
As dynamic systems their modeling and further high-quality control are a serious challenge.
The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution.
As an alternative, genetic algorithms as a stochastic global optimization method can be applied.
For the purpose of parameter identification of a fed-batch cultivation of S.
cerevisiae altogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered.
Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation.
The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too.
Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.
American Psychological Association (APA)
Angelova, Maria& Pencheva, Tania. 2011. Tuning Genetic Algorithm Parameters to Improve Convergence Time. International Journal of Chemical Engineering،Vol. 2011, no. 2011, pp.1-7.
https://search.emarefa.net/detail/BIM-487893
Modern Language Association (MLA)
Angelova, Maria& Pencheva, Tania. Tuning Genetic Algorithm Parameters to Improve Convergence Time. International Journal of Chemical Engineering No. 2011 (2011), pp.1-7.
https://search.emarefa.net/detail/BIM-487893
American Medical Association (AMA)
Angelova, Maria& Pencheva, Tania. Tuning Genetic Algorithm Parameters to Improve Convergence Time. International Journal of Chemical Engineering. 2011. Vol. 2011, no. 2011, pp.1-7.
https://search.emarefa.net/detail/BIM-487893
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-487893