Issue:InterCriteria analysis of genetic algorithm parameters in parameter identification

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Title of paper: InterCriteria analysis of genetic algorithm parameters in parameter identification
Author(s):
Tania Pencheva
Bioinformatics and Mathematical Modelling Department, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
tania.penchevaAt sign.pngbiomed.bas.bg
Maria Angelova
Bioinformatics and Mathematical Modelling Department, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
maria.angelovaAt sign.pngbiomed.bas.bg
Vassia Atanassova
Bioinformatics and Mathematical Modelling Department, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
vassia.atanassovaAt sign.pnggmail.com
Olympia Roeva
Bioinformatics and Mathematical Modelling Department, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
olympiaAt sign.pngbiomed.bas.bg
Presented at: 19th International Conference on Intuitionistic Fuzzy Sets, 4–6 June 2015, Burgas, Bulgaria
Published in: "Notes on IFS", Volume 21, 2015, Number 2, pages 99—110
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Abstract: An application of InterCriteria Analysis (ICA) – recently proposed approach for multicriteria decision support – is here presented. The apparata of Index Matrices and Intuitionistic Fuzzy Sets are in the grounds of ICA. In this investigation, ICA is applied to examine the influences of genetic algorithms parameters during the model parameter identification of E. coli MC4110 and S. cerevisiae fermentation processes. The impact of two of the main genetic algorithms parameters, namely number of individuals and number of generations, is here studied. The obtained results after ICA application are discussed towards convergence time and model accuracy. Some conclusions about existing relations and dependencies between genetic algorithms parameters, from one side, and fermentation process model parameters from the other side, are derived.
Keywords: InterCriteria analysis, Intuitionistic fuzzy sets, Genetic algorithms, Fermentation process, E. coli, S. cerevisiae.
AMS Classification: 03E72.
References:
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