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We present methods to automatically identify and optimize controllers for large-scale complex dynamic systems; in particular, aircraft gas turbine engines. We show how the optimization of different elements within the overall controller can be addressed in an efficient fashion. These elements include local actuator gains, control modifiers, and control schedules. An evolutionary algorithm (EA) is utilized to realize multiobjective optimization on a local as well as a global level, depending on the optimization task at hand. The fitness function comprises performance metrics that incorporate stall margins, exhaust gas temperature, fan-speed tracking error, and local tracking errors. Less attention has been given in the literature to the application of optimization techniques to aircraft engine control systems design, where the controls design and optimization is performed using a full-order engine model and full control systems structures that do not oversimplify the inherent complexities in these highly complex nonlinear dynamic systems. This paper attempts to close that gap.