3 ms·

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

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

jasonb05 1y ago
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).