Search

Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of S. cerevisiae fed-batch cultivation model parameters obtained using multi-population genetic algorithm (MpGA). Performances of the examined algorithm have been tested before and after the application of the procedure for purposeful model parameters genesis for three different values of generation gap which is the most sensitive genetic algorithms parameter toward convergence time. Results obtained after the implementation of intuitionistic fuzzy logic for the algorithm performance assessment have been compared and MpGA with GGAP = 0.1 after the purposeful model parameters genesis procedure application has been distinguished as the fastest and the most reliable one. [ABSTRACT FROM PUBLISHER]

Intuitionistic fuzzy logic has been implemented aiming to derive intuitionistic fuzzy estimations of S. cerevisiae fed-batch cultivation model parameters obtained using standard simple genetic algorithm. Performances of the examined algorithm have been tested before and after the application of the procedure for purposeful model parameters genesis and for three different values of the most sensitive toward convergence time genetic algorithms parameter namely generation gap. Results obtained after the implementation of intuitionistic fuzzy logic for the algorithm performance assessment have been compared and a value of GGAP ensuring the fastest and the most reliable solution has been recommended. [ABSTRACT FROM PUBLISHER]