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 • GA has the benefit of being able to handle a full variety of variability in a design. For example, in the preliminary design of an aircraft, the motor number and position must not be defined either in the wing configuration (i.e., monoplane, medium, mid-fuselage, and high), such that a selection algorithm can be used for the best combination.

 • While GA tries to find the whole design space to find a global optimum, no guarantee exists that an algorithm finds this point and there are no certain parameters that show that the global optimum is achieved if fulfilled. Although the multi-minimum problems are similarly insufficient to deal with all alternate algorithms, the GA does not suffer. On the opposite, GA’s potential not just to create an optimal design but an enhanced and feasible design population plays a major role for engineers since it allows them to make final design decisions by judgmental requirements that are outside of the formal framework of optimization. When a GA is regarded in an MDO procedure, the size of the design issue is significant. GA is suitable for parallel processing machines since the number of processors that allow for the calculation of multiple analyses can be simultaneously analyzed by all the people.

 • A downside to these codes is that, after new structural modifications have been required, the engineer cannot provide any detail that can be used by the engineer. Taking account of these reasons, we propose that in the early phases of an MDO application in which the issue is relatively limited but the uncertainty is comparatively high, the GA has a valuable function to play.