English

Reducing the Arity in Unbiased Black-Box Complexity

Neural and Evolutionary Computing 2015-03-20 v1

Abstract

We show that for all 1<klogn1<k \leq \log n the kk-ary unbiased black-box complexity of the nn-dimensional \onemax\onemax function class is O(n/k)O(n/k). This indicates that the power of higher arity operators is much stronger than what the previous O(n/logk)O(n/\log k) bound by Doerr et al. (Faster black-box algorithms through higher arity operators, Proc. of FOGA 2011, pp. 163--172, ACM, 2011) suggests. The key to this result is an encoding strategy, which might be of independent interest. We show that, using kk-ary unbiased variation operators only, we may simulate an unrestricted memory of size O(2k)O(2^k) bits.

Cite

@article{arxiv.1203.4111,
  title  = {Reducing the Arity in Unbiased Black-Box Complexity},
  author = {Benjamin Doerr and Carola Winzen},
  journal= {arXiv preprint arXiv:1203.4111},
  year   = {2015}
}

Comments

An extended abstract of this paper has been accepted for inclusion in the proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012)

R2 v1 2026-06-21T20:36:13.987Z