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This paper presents a genetic algorithms approach for the optimization of a fed-batch penicillin fermentation process. A customized float-encoding genetic algorithm is developed and implemented to a benchmark fed-batch penicillin fermentation process. Off-line optimization of the initial conditions and set points are carried out in two stages for a single variable and multiple variables. Further investigations with online optimization have been carried out to demonstrate that the yield can be significantly improved with an optimal feed rate profile. The results have shown that the proposed approaches can be successfully applied to optimization problems of fed-batch fermentation to improve the operation of such processes.