Proves sharp threshold on mutation parameter χ for (1+1)-EA on Dynamic Binary Value and Uniform weight dynamic linear problems, yielding O(n log n) runtime below threshold and 2^Ω(n) above, plus a second stagnation-distance threshold for the former.
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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.
Self-adjusting mutation rates let the (1+1) EA optimize the top k bits of BinVal in O(k^{1+ε}) time independent of n for all k in o(n) simultaneously.
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The $(1 + 1)$-EA in Dynamic Environments
Proves sharp threshold on mutation parameter χ for (1+1)-EA on Dynamic Binary Value and Uniform weight dynamic linear problems, yielding O(n log n) runtime below threshold and 2^Ω(n) above, plus a second stagnation-distance threshold for the former.
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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.
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Anytime Analysis on BinVal: Adaptive Parameters Help
Self-adjusting mutation rates let the (1+1) EA optimize the top k bits of BinVal in O(k^{1+ε}) time independent of n for all k in o(n) simultaneously.