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I think you've confused genetic algorithms with genetic programming. They're not the same thing.

> 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.

This is quite confused. You're comparing different levels of the systems. In neural networks, we would like to know why a numerical model (which has been optimised by gradient descent) gives the outputs it gives. In GAs, (1) the objects being created usually aren't numerical models -- think instead of solutions to TSPs; (2) the reason the object is good is rather easy to see -- one just has to look at the objective function and verify that that the object has the desired properties; (3) we don't really care what other objects were considered during the search process.




> (2) the reason the object is good is rather easy to see -- one just has to look at the objective function and verify that that the object has the desired properties;

Minor point: whether the result of evolution is easy to understand or not depends on the representation (encoding). Even GA results can be difficult to understand if they describe complex objects. In the case of GP, bloat (rapid increase in average program size in your population) can make results very difficult to interpret.




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