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Issue:Knowledge discovery from data: InterCriteria Analysis of mutation rate influence

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Title of paper: Knowledge discovery from data: InterCriteria Analysis of mutation rate influence
Author(s):
Olympia Roeva
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
olympia@biomed.bas.bg
Dafina Zoteva
Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria
dafy.zoteva@gmail.com
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 24 (2018), Number 1, pages 120–130
DOI: https://doi.org/10.7546/nifs.2018.24.1.120-130
Download:  PDF (252 Kb  Kb, File info)
Abstract: In this paper the InterCriteria Analysis (ICrA) approach is applied to find more knowledge from series of identification procedures using 34 differently tuned genetic algorithms (GAs). The influence of the mutation rate pm on the algorithm performance is investigated. An E. coli fed-batch fermentation process model is used as a test problem. Based on the results from parameter identification, namely objective function values, the GAs, with the correspondent pm-value, producing the best results are determined. Frther, ICrA is applied using information from all model parameter estimates, computational time and objective function value. The ICrA confirms the conclusions based only on objective function values and helps to choose what mutation rate pm is more appropriate to use in the considered case study.
Keywords: InterCriteria Analysis, Intuitionistic fuzzy sets, Genetic algorithms, Mutation rate, E. coli.
AMS Classification: 03E72
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