The design of advanced functional devices and systems is often based on technical specifications that are either represented as complicated, let alone, contradicting tradeoff relations or situated at the very limit of the physically possible. In either case classical engineering approaches will render inappropriate and have to be reformulated as an inverse problem ready to be solved using numerical optimization. Hence, the question regarding the feasibility of solving such inverse problems with numerical structural optimization is discussed along various examples in the realm of microoptics and nanophotonics. We focus on population-based design approaches as supported by biological-inspired search heuristics like evolutionary algorithms where a finite population of potential solutions is numerically iterated according to specific genetic reproduction rules,undergoing a kind of artificial evolution. Besides the intended solution, population-based optimization algorithms are apt to deliver structural and temporal information during evolution that can be further exploited in order to provide measures for either refining or accelerating the global search behavior. In an interlude we will further speculate whether physical quantities intrinsic to the device are adequate to be nested into such global search heuristics in order to improve the optimization process.Besides the success assigned to computer guided engineering schemes, there is a hidden epistemological problem  – and thus mostly ignored – regarding the counterintuitive morphology of the best performing outcomes. Here in particular we will address the question whether a formal postprocessing of such findings could provide a measure to reconcile the peculiar outcomes with current engineering expertise.  Jürg Fröhlich and Daniel Erni, "Postprocessing – making technical artifacts more intelligible," submitted to EASST Conference 2010 (EASST 010), ‘Practicing Science and Technology, Performing the Social’, The European Association for the Study of Science and Technology, Sept. 2-4, University of Trento, Track 8: Probing Technoscience, 2010.