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Algorithmica 64(4), 673–697 (Dec 2012)

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

citation-role summary

method 1

citation-polarity summary

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cs.NE 4

years

2026 4

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UNVERDICTED 4

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method 1

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use method 1

representative citing papers

The $(1 + 1)$-EA in Dynamic Environments

cs.NE · 2026-06-11 · unverdicted · novelty 7.0

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.

Analysis of Search Heuristics in the Multi-Armed Bandit Setting

cs.NE · 2026-04-09 · unverdicted · novelty 6.0

In the dueling bandit setting, the (1+1) EA selects the Condorcet winner with only constant probability when its advantage is Ω(1/n), while a Max-Min Ant System EDA selects it with probability 1-Θ(p), and repeated duels improve the EA's performance.

citing papers explorer

Showing 4 of 4 citing papers.

  • The $(1 + 1)$-EA in Dynamic Environments cs.NE · 2026-06-11 · unverdicted · none · ref 9

    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.

  • Improved Runtime Bound for the $(\mu + 1)$ EA on BinVal cs.NE · 2026-06-11 · unverdicted · none · ref 7

    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.

  • Analysis of Search Heuristics in the Multi-Armed Bandit Setting cs.NE · 2026-04-09 · unverdicted · none · ref 9

    In the dueling bandit setting, the (1+1) EA selects the Condorcet winner with only constant probability when its advantage is Ω(1/n), while a Max-Min Ant System EDA selects it with probability 1-Θ(p), and repeated duels improve the EA's performance.

  • Anytime Analysis on BinVal: Adaptive Parameters Help cs.NE · 2026-04-08 · unverdicted · none · ref 9

    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.