InterCriteria Analysis of crossover and mutation rates relations in simple genetic algorithm

In this investigation recently developed InterCriteria Analysis (ICA) is applied to examine the influences of two main genetic algorithms parameters - crossover and mutation rates during the model parameter identification of S. cerevisiae and E. coli fermentation processes. The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between investigated genetic algorithms parameters, from one hand, and fermentation process model parameters, from the other hand. The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about derived interactions are reported.