Genetic/memetic algorithms are just a rough approximation of a certain selection mechanism we see in the nature, like what Deep Learning is to brain. Making a conclusion that some trivial algorithm with even complicated custom cross-over function can be translated to real-world is as much fantasy as thinking one could upload consciousness to TensorFlow. 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...
> Genetic/memetic algorithms are just a rough approximation of a certain selection mechanism we see in the nature
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.
