Adaptive optimization of fed-batch culture of yeast by using genetic algorithms

Abstract

A simulation and experimental study has been carried out on the adaptive optimization of fed-batch culture of yeast. In the simulation study, three genetic algorithms based on different optimization strategies were developed. The performance of those three algorithms were compared with one another and with that of a variational calculus approach. The one that showed the best performance was selected to be used in the subsequent experimental study. To confer an adaptability, an online adaptation (or model update) algorithm was developed and incorporated into the selected optimization algorithm. The resulting adaptive algorithm was experimentally applied to fed-batch cultures of a recombinant yeast producing salmon calcitonin, to maximize the cell mass production. It followed the actual process quite well and gave a much higher value of performance index than the simple genetic algorithm with no adaptability.