# OneMax in Black-Box Models with Several Restrictions

@article{Doerr2015OneMaxIB,
title={OneMax in Black-Box Models with Several Restrictions},
author={Carola Doerr and Johannes Lengler},
journal={Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation},
year={2015}
}
• Published 11 July 2015
• Computer Science, Mathematics
• Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation
As in classical runtime analysis the OneMax problem is the most prominent test problem also in black-box complexity theory. It is known that the unrestricted, the memory-restricted, and the ranking-based black-box complexities of this problem are all of order n/log n, where n denotes the length of the bit strings. The combined memory-restricted ranking-based black-box complexity of OneMax, however, was not known. We show in this work that it is Θ(n) for the smallest possible size bound, that is…
5 Citations
OneMax in Black-Box Models with Several Restrictions
• Mathematics, Computer Science
Algorithmica
• 2016
This work shows that the (1+1) memory-restricted ranking-based black-box complexity of OneMax is linear, and provides improved lower bounds for the complexity of the OneMax in the regarded models.
On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization
• Computer Science, Mathematics
Algorithmica
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A rigorous runtime analysis concerning the update strength, a vital parameter in PMBGAs such as the step size 1 / K in the so-called compact Genetic Algorithm and the evaporation factor $$\rho$$ρ in ant colony optimizers (ACO).
Theory of estimation-of-distribution algorithms
• Computer Science
GECCO
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An up-to-date overview of the most commonly analyzed EDAs and the most recent theoretical results in this area is provided, with emphasis put on the runtime analysis of simple univariate EDAs.
Theory of estimation-of-distribution algorithms
• C. Witt
• Computer Science
GECCO Companion
• 2020

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This work shows that the (1+1) memory-restricted ranking-based black-box complexity of OneMax is linear, and provides improved lower bounds for the complexity of the OneMax in the regarded models.
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