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EvE (Evolutionary Explorer), a steady-state, population-of-four differential evolution (DE) optimizer with a targeted Adam fallback: each iteration proposes one candidate via DE, running a short burst of gradient descent only if the DE step fails to improve on the incumbent.
This work studies how to make the diverse trade-off architectures to possess a regularity, so they can be better understood, maintained, and deployed with confidence, and shows that this regularity-driven search can produce families of architectures that remain competitive in performance while being structurally simple...
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