Closed-Loop Generative Selection: Convergence, Memory, and Noisy Oracles
A self-contained theory of convergence and expected running time for closed-loop generative selection is developed, and it is shown that elitism makes the search absorbing, and almost-sure convergence is proved together with a runtime bound that decomposes the search into the time spent escaping each fitness level.