Paper Info
Title
Lower bounds on the run time of the Univariate Marginal Distribution Algorithm on OneMax.
Abstract
The Univariate Marginal Distribution Algorithm (UMDA) – a popular estimation-of-distribution algorithm – is studied from a run time perspective. On the classical OneMax benchmark function on bit strings of length n, a lower bound of Ω(λ+μn+nlog⁡n), where μ and λ are algorithm-specific parameters, on its expected run time is proved. This is the first direct lower bound on the run time of UMDA. It is stronger than the bounds that follow from general black-box complexity theory and is matched by the run time of many evolutionary algorithms. The results are obtained through advanced analyses of the stochastic change of the frequencies of bit values maintained by the algorithm, including carefully designed potential functions. These techniques may prove useful in advancing the field of run time analysis for estimation-of-distribution algorithms in general.
Year
DOI
Venue
2020
10.1016/j.tcs.2018.06.004
Theoretical Computer Science
Keywords
DocType
Volume
Estimation-of-distribution algorithm,Run time analysis,Lower bound
Journal
832
ISSN
Citations 
PageRank 
0304-3975
1
0.36
References 
Authors
0
2
Name
Order
Citations
PageRank
Martin Krejca1829.47
Carsten Witt298759.83