OneMax in Black-Box Models with Several Restrictions

@article{Doerr2016OneMaxIB,
  title={OneMax in Black-Box Models with Several Restrictions},
  author={Carola Doerr and J. Lengler},
  journal={Algorithmica},
  year={2016},
  volume={78},
  pages={610-640}
}
Black-box complexity studies lower bounds for the efficiency of general-purpose black-box optimization algorithms such as evolutionary algorithms and other search heuristics. Different models exist, each one being designed to analyze a different aspect of typical heuristics such as the memory size or the variation operators in use. While most of the previous works focus on one particular such aspect, we consider in this work how the combination of several algorithmic restrictions influence the… Expand
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References

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OneMax in Black-Box Models with Several Restrictions
TLDR
This work shows that the (μ+λ) elitist memory-restricted ranking-based black-box complexity of OneMax is as small as (an advanced version of) the information-theoretic lower bound, and enlivens the quest for natural evolutionary algorithms optimizing OneMax in o(n log n) iterations. Expand
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A ranking-based black-box algorithm is presented that has a runtime of Θ(n/logn), which shows that the OneMax problem does not become harder with the additional ranking- basedness restriction. Expand
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This work proposes a new elitist black-box model, in which algorithms are required to base all decisions solely on (a fixed number of) the best search points sampled so far, and introduces the concept of $p-Monte Carlo black- box complexity, which measures the time it takes to optimize a problem with failure probability at most p. Expand
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