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 language | English |
|---|---|
| Pages (from-to) | 275-286 |
| Number of pages | 12 |
| Journal | Artificial Intelligence |
| Volume | 216 |
| DOIs | |
| Publication status | Published - 1 Jan 2014 |
Keywords
- Evolutionary computation
- Heuristic search
- Run time analysis