View source for Issue:InterCriteria analysis of a cultivation process model based on the genetic algorithm population size influence

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{{issue/title
 | title           = InterCriteria analysis of a cultivation process model based on the genetic algorithm population size influence
 | shortcut        = nifs/21/4/90-103
}}
{{issue/author
 | author          = Maria Angelova
 | institution     = Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences
 | address         = 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
 | email-before-at = maria.angelova
 | email-after-at  = biomed.bas.bg
}}
{{issue/author
 | author          = Olympia Roeva
 | institution     = Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences
 | address         = 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
 | email-before-at = olympia
 | email-after-at  = biomed.bas.bg
}}
{{issue/author
 | author          = Tania Pencheva
 | institution     = Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences
 | address         = 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
 | email-before-at = tania.pencheva
 | email-after-at  = biomed.bas.bg
}}
{{issue/data
 | conference      = 
 | issue           = [[Notes on Intuitionistic Fuzzy Sets/21/4|"Notes on IFS", Volume 21, 2015, Number 4]], pages 90–103
 | file            = NIFS-21-4-090-103.pdf
 | format          = PDF
 | size            = 653
 | abstract        = InterCriteria Analysis (ICA), based on the apparatus of the Index Matrices and the Intuitionistic Fuzzy Sets, is here applied. The main idea is ICA to be applied in order the relations of defined parameters in a non-linear model of a Saccharomyces cerevisiae fed-batch cultivation to be evaluated. Using genetic algorithms, series of identification procedures of considered model are performed. The influence of the genetic algorithm population size towards optimization technique outcomes, such as convergence time and objective function value, is investigated. Implementing ICA, relations between model parameters themselves, on the one hand, and genetic algorithms outcomes, on the other hand, are obtained. The thorough discussion of derived results are worth for, firstly, deeper cultivation process model understanding, and, further, a better performance of genetic algorithms to be achieved.
 | keywords        = InterCriteria analysis, Genetic algorithms, Population size, Cultivation process, '' Saccharomyces cerevisiae''.
 | ams             = 03E72.
 | references      = 
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