Differential evolution (DE) is one of the novel evolutionary optimization methods used for solving the problems that consist of nondifferentiable, nonlinear and multi-objective functions. In this presented work. the classical DE technique and its various versions, such as opposition based on differential evolution (ODE), adaptive differential evolution (ADE), adaptive opposition based on differential evolution (AODE) which is an advanced version of ODE, are presented to determine the optimal feeding flow profile of an industrial scale fed-batch baker's yeast fermentation process. The main objective in any fed-batch fermentation process optimization is both to maximize the amount of the biomass at the end of the process and to minimize the ethanol formation during the process. Four different cases regarding the initial condition of the fermentation process were considered so as to evaluate the performances of proposed algorithms. Besides, two strategies of mutation and crossover operators, which are the most popular in DE's applications, were utilized for performance comparison tests. The influence of initial seed value, initial condition of the process, and both of the mutation and crossover strategies have been investigated for all the different classic, opposition-based, self-adaptive and adaptive opposition-based mechanisms. To demonstrate the performance comparison of the of DE's techniques, the experimental data collected from the fermentor with volume of 100 m(3) are presented with the optimization results obtained by using all the interested DE techniques for the same initial conditions. (C) 2009 ISA. Published by Elsevier Ltd. All rights reserved.