Those are some good links, thanks. Are there clear distinctions between these four areas? It seems like the separation is a historical artifact, when they're all basically doing the same thing.

I mostly looked at GAs and GPs. Evo strategy has some interesting mathematical stuff.

Basically GA and GP are the dominant strands. And there's a key difference.

Genetic Algorithms are historically about finding paramaters into a function. You have some function f(X1 ... Xn), and you are trying to find the best set of parameters X1...Xn. So a common representation is a string of floats or integers or whatever, and then you do the crossovers and mutations based on positions in the string.

Genetic Programming is about creating new functions. So instead of a string of parameters, the genome is (usually) a tree of instructions to be interpreted. You do mutations by swapping node types, crossovers by chopping subtrees and moving them etc.

One artefact of the historical "parallel evolution" (teehee) of these fields is that GP practitioners use crossovers much less than GA practitioners.

To a large degree you are correct, the different algorithms are often interchangeable. Sometimes this is not the case: e.g. Genetic Programming is used to search for algorithms that are not easily represented and searched for with other evolutionary algorithms.