InterCriteria Analysis of Multi-population Genetic Algorithms Performance

InterCriteria Analysis approach is here applied for the assessment of promising genetic algorithms optimization techniques. Altogether six multi-population genetic algorithms are here considered, differing in the execution order of main genetic operators selection, crossover and mutation. InterCriteria Analysis approach, based on the apparatuses of index matrices and intuitionistic fuzzy sets, is implemented to assess the performance of multi-population genetic algorithms for the parameter identification of Saccharomyces cerevisiae fed-batch fermentation process. Degrees of "agreement" and "disagreement" between the algorithms outcomes convergence time and model accuracy, from one hand, and model parameters estimations, from the other hand, have been established. Outlined relations are going to lead to an additional exploring of the model, expected to be extraordinary valuable especially in the case of modelling of living systems, such as fermentation processes.