The performance of any Evolutionary Algorithm (EA) is closely related to the topographical features of the problem fitness landscape it is applied to. It is therefore of paramount importance to determine a set of features that is useful in order to choose an appropriate algorithm for a given problem. This way, the inefficient trial and error stage that most EA users carry out until they find an EA that satisfies their objectives can be reduced. In fact, as this, usually lengthy, trial and error stage is generally carried out in an ad hoc manner, the information the user gleans from the performance of the algorithms chosen and their particular parameter sets, or lack thereof, can be very misleading or plain useless. Thus, in previous work, we analyze a set of features in synthetic fitness landscapes that can be used in order to characterize problems and relate them to the performance of EAs. The objective is to define a mechanism to reduce the trial and error stage when choosing the correct EA and, at the same time, provide more in depth knowledge on the nature of the problem. Here, in order to highlight the usefulness of the approach, this analysis is extended to real world application landscapes by means of the characterization of a horizontal axis wind turbine (HAWT) design problem, showing the relevance of the pre-processing stage in the selection of the most appropriate EA to solve it.