WA-ASNG augments ASNG with weight adaptation that maximizes an estimated update signal, showing improved results over PBIL and ASNG on binary problems with population sizes 25-100 and under noise.
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cs.NE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Empirical benchmarking shows tolfunhist and the full portfolio stop CMA-ES closest to the optimal evaluation count on BBOB, while tolfun and tolfunhist often trigger before full stagnation.
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Weight Adaptation for Improving Parallel Performance of Adaptive Stochastic Natural Gradient
WA-ASNG augments ASNG with weight adaptation that maximizes an estimated update signal, showing improved results over PBIL and ASNG on binary problems with population sizes 25-100 and under noise.
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Quantitative Performance Analysis of Stopping Criteria for CMA-ES
Empirical benchmarking shows tolfunhist and the full portfolio stop CMA-ES closest to the optimal evaluation count on BBOB, while tolfun and tolfunhist often trigger before full stagnation.