The aircraft industry has been at the forefront in developing design optimization strategies ever since the advent of high performance computing. Thanks to the large computational resources now available, many new as well as more mature optimization methods have become well established. However, the same cannot be said for other stages along the optimization process - chiefly, and this is where the present thesis seeks to make its first main contribution, at the geometry parameterization stage. The first major part of the thesis is dedicated to the goal of reducing the size of the search space by reducing the dimensionality of existing parameterization schemes, thus improving the effectiveness of search strategies based upon them. Specifically, a refinement to the Kulfan parameterization method is presented, based on using Genetic Programming and a local search within a Baldwinian learning strategy to evolve a set of analytical expressions to replace the standard 'class function' at the basis of the Kulfan method. The method is shown to significantly reduce the number of parameters and improves optimization performance - this is demonstrated using a simple aerodynamic design case study. The second part describes an industrial level case study, combining sophisticated, high fidelity, as well as fast, low fidelity numerical analysis with a complex physical experiment. The objective is the analysis of a topical design question relating to reducing the environmental impact of aviation: what is the optimum layout of an over-the-wing turbofan engine installation designed to enable the airframe to shield near-airport communities on the ground from fan noise. An experiment in an anechoic chamber reveals that a simple half-barrier noise model can be used as a first order approximation to the change of inlet broadband noise shielding by the airframe with engine position, which can be used within design activities. Moreover, the experimental results are condensed into an acoustic shielding performance metric to be used in a Multidisciplinary Design Optimization study, together with drag and engine performance values acquired through CFD. By using surrogate models of these three performance metrics we are able to find a set of non-dominated engine positions comprising a Pareto Front of these objectives. This may give designers of future aircraft an insight into an appropriate engine position above a wing, as well as a template for blending multiple levels of computational analysis with physical experiments into a multidisciplinary design optimization framework.