Abstract

Unbiased black-box complexity was introduced as a refined complexity model for random-ized search heuristics (Lehre and Witt (2012) [24]). For several problems, this notion avoids the unrealistically low complexity results given by the classical model of Droste et al. (2006) [10]. We show that for some problems the unbiased black-box complexity remains artificially small. More precisely, for two different formulations of an NP-hard subclass of the well-known Partition problem, we give mutation-only unbiased black-box algorithms having complexity O(nlog n). This indicates that also the unary unbiased black-box complexity does not give a complete picture of the true difficulty of this problem for randomized search heuristics.

Original languageEnglish
Pages (from-to)275-286
Number of pages12
JournalArtificial Intelligence
Volume216
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Evolutionary computation
  • Heuristic search
  • Run time analysis

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