What do you mean? You trivially simulated the process of figuring out that an animal needs to grow a new leg/organ in response to some environmental situation or to gain "an upper leg" against its adversaries? Are you serious?
Separately, as an algorithm, evolution also has easily characterisable failure modes (local minima), and we do observe those failure modes in organic examples.
But, to emphasise, the fact artificial evolution requires us to write our own fitness functions is merely an implementation detail; these algorithms work because evolution works. They produce novel solutions without the authors of the programs creating the solutions.
This is irrelevant to my argument. If the simplified version is capable of doing what you have said is unexplained, it is unreasonable to assume that the more complicated version will fail to be capable of the same.
> I never understood how anyone calling themselves a scientist could make such a leap, but obviously it's popular and people made a "scientific" career doing just that...
Occam’s Razor. Start with the simplest possible model, make a prediction, look carefully at reality to see if you were wrong (an act which is easier the simpler the model), and only update the model when reality disagrees with it.
Me coding a genetic algorithm can easily reproduce the entirety of any specific human’s genome if I pick the correct fitness function. Pretty pointless to do so beyond proof of concept, but proof of concept is enough to make the point in this case.
Fortunately my point has no dependency on any specific fitness function, natural or programmed — my argument has been that the original claim “We can observe that cells/DNA are programmed to adapt to local changes; we have no idea how they make sudden jumps for a more efficient design;” totally misrepresented evolution, that in fact evolution totally can make sudden-looking jumps (a point which in retrospect I don’t think I properly justified, even if my list of examples was intended that way), and that it’s trivial to demonstrate this by writing a genetic algorithm.