The following commentator writes:
Having studied this extensively back when they were called Genetic Algorithms, I would like to offer a few insights.
One of the biggest reasons they fell out of favor for more "mathematical" approaches was that no one could really explain why exactly they worked. It makes sense on the surface that "survival of the fittest" and doing something akin to multiple stochastic gradient descents would work, but no one has really been able to produce a mathematical proof as to why.
Since other folks are producing good examples of "explainable AI", I don't know how Genetic Algorithms/programming could be made 'explainable' as to why they achieved an optimal solution other than hand-waving to how evolution works in nature.
Steven Wolfram has published the book A New Kind of Science, in which he posits that mathematical proofs aren't relevant to this approach, and the algorithm is itself the science. Many considered this unsatisfactory.
My question is: Are there satisfying explanations for why genetic algorithms work?