Knowledge discovery from data: InterCriteria Analysis of mutation rate influence
Olympia Nikolaeva Roeva, Dafina Zoteva · Notes on Intuitionistic Fuzzy Sets · 2018
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 p m 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 p m -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 p m is more appropriate to use in the considered case study.