Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies
Noisy evolution strategies under fixed evaluation budgets face a depth-fidelity trade-off: spending evaluations to denoise intra-generation rankings reduces the number of distribution updates the optimizer can execute. We argue for depth over fidelity and propose probabilistic elite membership (PEM), which replaces hard rank-based weights in evolution strategies with conditional expected rank weights that integrate over ranking uncertainty. PEM preserves the conditional mean update while reducing conditional update dispersion, a Rao-Blackwellization of the noisy rank-based step. We instantiate PEM via residual bootstrapping (RB-PEM) with capped per-generation overhead, complemented by an ...
| Authors | Sichen Wang, Zhipeng Lu |
|---|---|
| Primary category | cs.NE |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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