Where's the Interest in Mapping Fitness Functions of Real Problems?

It seems to be generally assumed that one primary difficulty in applying evolutionary methods is defining a smooth fitness function that avoids local minima as possible. Even the best methods, like NEAT, stagnate as some point. I'm interested in looking at these places in the fitness landscape-- places where evolution isn't able to make further progress. Perhaps even mapping them out. Maybe even generating or co-evolving fitness functions to help find easily understood evolutionary traps. It would probably yield nothing intelligible, but possibly trying to characterize fitness functions by looking at where they trap different representations.

Here's an example looking at CTRNNs and XOR. I find it most striking that there isn't anything familiar-looking in his graphs. Wouldn't you think XOR would be simple? And since it really isn't, what is a simple (but at least slightly non-trival) problem for evolved NN's or CTRNN's?