Complexity Theory for Discrete Black-Box Optimization Heuristics

@article{Doerr2018ComplexityTF,
  title={Complexity Theory for Discrete Black-Box Optimization Heuristics},
  author={Carola Doerr},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.02037}
}
A predominant topic in the theory of evolutionary algorithms and, more generally, theory of randomized black-box optimization techniques is running-time analysis. Running-time analysis is aimed at understanding the performance of a given heuristic on a given problem by bounding the number of function evaluations that are needed by the heuristic to identify a solution of a desired quality. As in general algorithms theory, this running-time perspective is most useful when it is complemented by a… Expand
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Optimal parameter choices via precise black-box analysis
TLDR
This work proves that the unary unbiased black-box complexity of the OneMax benchmark function class is n ln ⁡ ( n ) − c n ± o ( n) for a constant c which is between 0.2539 and 0.2665. Expand
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