In the recent years, Evolutionary Algorithms (EAs) have been the topic of many researches through optimizations.Differential
Evolution (DE) is one of the most popular optimization methods for real-valuedproblems and a large number of its variants have been
proposed so far. However, bringingtogether different ideas that already led to successful DE versions is rare in the literature In this
paper we propose a novel DE based MemeticAlgorithm(DEBMA) which hybridizes the differential evolution algorithm with a Local
Search (LS)method to control the convergence rate of the population. In the proposed algorithm, some individuals are chosen for local
refinement using a LS method, which leads to a smoother variation and a longer memory effect. The LS demonstrates a potential for
interpreting evolution of the algorithm and to control its convergence. In this paper we describe an application of EAs to the design of
fractional order proportional-integral-derivative (FOPID) controllers which involve a fractional order integral and a fractional order
derivative. Fractional order controllers are more complex to design due to five design parameters. Here we use EAs to design an
optimal FOPID controller to control a Bioreactor plant.To show the performance of both the FOPID and the proposed algorithm, a
comparison between the designed controller using MA, simple DE and the conventional PID controller is presented.