Intuitionistic Fuzzy Logic as a Tool for Quality Assessment of Genetic Algorithms Performances

Intuitionistic fuzzy logic (IFL) has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of S. cerevisiae fed-batch cultivation model parameters obtained using standard simple (SGA) and multi-population (MpGA) genetic algorithms. Performances of MpGA have been tested before and after the application of the procedure for purposeful model parameters genesis at three different values of generation gap, proven as the most sensitive genetic algorithms parameter toward convergence time. Results obtained after the implementation of intuitionistic fuzzy logic for MpGA performances assessment have been compared and MpGA at GGAP = 0.1 after the purposeful model parameters genesis procedure application has been distinguished as the fastest and the most reliable one. Further, the prominent MpGA at GGAP = 0.1 has been compared to SGA at GGAP = 0.1. Obtained results have been assessed applying IFL and the most reliable algorithm has been distinguished.