As a scientist (and a computer scientist at that) my view is that if we cannot simulate a process we have not understood it properly. I have been following the interesting field of Artificial Life for quite some time and the results are sobering - let me just quote two paragraphs from current overview articles:
One thing that always seems to happen with such projects is that, after they achieve their intended aim, if the ‘evolutionary’ program is allowed to run further it produces no further improvements. This is exactly what would happen if all the knowledge in the successful robot had actually come from the programmer [...]
That is why I doubt that any ‘artificial evolution’ has ever created knowledge. I have the same view, for the same reasons, about the slightly different kind of ‘artificial evolution’ that tries to evolve simulated organisms in a virtual environment, and the kind that pits different virtual species against each other.
Source: David Deutsch (2011): The Beginning of Infinity
One of the earliest networked artificial life experiments was based on the well-known A-Life system, Tierra. This was created in the early 1990s by the ecologist Tom Ray to simulate in silico the basic processes of evolutionary and ecological dynamics. After Ray began his work, he soon recognized the potential of the Web to create a large complex environment in which digital organisms could freely evolve. So he set up a project called Network Tierra to exploit this potential
The results of this experiment were mixed. One goal of Network Tierra was to reproduce the Cambrian explosion in which single-celled organisms on Earth evolved rapidly into multicellular ones and then into more complex animals.
The in silico experiment began with a human-designed multicellular organism consisting of two different cell types. This survived under natural selection, a significant success in itself, but the number of cell types never increased beyond two.
Source: MIT Technology Review (2014): The Curious Evolution of Artificial Life
The point is that I have myself successfully worked a lot with genetic algorithms and genetic programming (I am also teaching this stuff) but what bothers me is that we are still not able to create some abstract form of (co-)evolution inside a computer where some real dynamics take place to produce ever and ever more sophisticated "species".
Are there hints from the biological sciences what this mysterious ingredient could be which we still seem to be missing? Is it physics? Is it chemistry? Is it something else?
Obviously the question is not clear as it stands, so I try a clarification: I refer to complexity of the resulting "species" in artificial life simulations. For example their behavioural or structural complexity. Why do these simulations always get stuck at some very low level (e.g. following food) and never ever even create something as complex as a bacterium? The computing power should be more than sufficient by now - and still, nothing... It seems that only what has been put into the simulation comes out but real evolution produces something really new (this is what the renowned scientist and polymath David Deutsch (University of Oxford) means by "I doubt that any 'artificial evolution' has ever created knowledge.")
Nathaniel gave me a decisive hint in the comments that this problem is called "open-ended evolution (OEE)" in the Alife community and it is one of the biggest research challenges there - unsolved yet! As a starting point see here: https://www.google.de/search?q=%22open-ended+evolution%22&artificial&life
Very interesting that it doesn't seem to bother the biological community and is met even with hostility here (some even lecturing me that the evidence for evolution is overwhelming and thereby implying that I might be some kind of crackpot creationist - unbelievable...)
...and no, the answer is not a matter of opinion (why this question was closed) but a valid research question (hopefully with some good answers someday)!
Last year there was even a big conference on this topic with many interesting results (although the problem itself is still unsolved):
See also my follow-up question here:
If evolution is not about increased complexity, why does so much complexity evolve?