TY  - JOUR
A1  - Doerr, Benjamin
A1  - Krejca, Martin Stefan
T1  - A simplified run time analysis of the univariate marginal distribution algorithm on LeadingOnes
JF  - Theoretical computer science
N2  - With elementary means, we prove a stronger run time guarantee for the univariate marginal distribution algorithm (UMDA) optimizing the LEADINGONES benchmark function in the desirable regime with low genetic drift. If the population size is at least quasilinear, then, with high probability, the UMDA samples the optimum in a number of iterations that is linear in the problem size divided by the logarithm of the UMDA's selection rate. This improves over the previous guarantee, obtained by Dang and Lehre (2015) via the deep level-based population method, both in terms of the run time and by demonstrating further run time gains from small selection rates. Under similar assumptions, we prove a lower bound that matches our upper bound up to constant factors.
KW  - Theory
KW  - Estimation-of-distribution algorithm
KW  - Run time analysis
Y1  - 2021
U6  - https://doi.org/10.1016/j.tcs.2020.11.028
SN  - 0304-3975
SN  - 1879-2294
VL  - 851
SP  - 121
EP  - 128
PB  - Elsevier
CY  - Amsterdam
ER  -