The (μ+1) EA optimizes BinVal in O(μ log μ · n log n) evaluations for μ = o(n/log n), improving the prior O(μ^5 n log(n/μ^4)) bound.
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RESSAP creates a model-agnostic ensemble of classifiers using resilience-guided feature selection, random subset inference, and noise augmentation to boost robustness to evasion attacks while preserving clean accuracy.
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Improved Runtime Bound for the $(\mu + 1)$ EA on BinVal
The (μ+1) EA optimizes BinVal in O(μ log μ · n log n) evaluations for μ = o(n/log n), improving the prior O(μ^5 n log(n/μ^4)) bound.