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Yep, the interesting thing is genetic algorithms previously were mostly good at course search and less good at fine search.

They also often converge to a local minima, and are costly.

It’ll be interesting to see if LLMs change that, or whether we are just approximating something a gradient could do better

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Hmm, it's more that LLMs can be used for population -> candidate solution generation, e.g. operators in a GA.

The innovation is novelty search, e.g. divergent rather than convergent optimization, paired with the capability that LLM genetic operators and representations unlock.

At least, that's my high-level take. google's alphaevolve is the same kind of thing but focused on convergent ga's (i think).

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