[joe_the_user]

In wikipedia and numerous references, I get the impression that EAs, evolutionary algorithms, are a huge and fuzzily defined field.

With Genetic algorithms, just one kind of evolutionary algorithm, the fitness, the mutation and the crossover function seem to require the implementor to look at the problem domain and "come up with something" whereas once you have a goal, gradient descent requires lots of tuning but is more or less defined, you can track how well you're doing and so-forth.

Perhaps there's something I'm missing. Pointers would be welcome.

Looked at: https://en.wikipedia.org/wiki/Evolutionary_algorithm https://en.wikipedia.org/wiki/Genetic_algorithm

[tlb]

Salimans et al evolved weights in convolutional neural nets -- the same structure of network that works with deep reinforcement learning -- to play Atari games from pixels. https://blog.openai.com/evolution-strategies/

So you can make it work with generic models. Although as you say, many of the big successes like the Backgammon players and Kosa's work on electronic circuits used domain-specific models.