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Genetic Algorithms Quality Assessment Implementing Intuitionistic Fuzzy Logic
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Author(s): Tania Pencheva (Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Bulgaria), Maria Angelova (Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Bulgaria)and Krassimir Atanassov (Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Bulgaria)
Copyright: 2014
Pages: 28
Source title:
Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
Source Author(s)/Editor(s): Pandian M. Vasant (Petronas University of Technology, Malaysia)
DOI: 10.4018/978-1-4666-4450-2.ch011
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Abstract
Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Considered here are standard simple and multi-population genetic algorithms as well as their modifications differ from each other in execution order of main genetic operators (selection, crossover, and mutation). All are applied for the purpose of parameter identification of S. cerevisiae fed-batch cultivation. Performances of the examined algorithms have been assessed before and after the application of a procedure for narrowing the range of model parameters variation. Behavior of standard simple genetic algorithm has been also examined for different values of proof as the most sensitive genetic algorithms parameter toward convergence time, namely, generation gap (GGAP). Results obtained after the intuitionistic fuzzy logic implementation for assessment of genetic algorithms performance have been compared. As a result, the most reliable algorithm/value of GGAP ensuring the fastest and the most valuable solution is distinguished.
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