# OneMax in Black-Box Models with Several Restrictions

@article{Doerr2016OneMaxIB, title={OneMax in Black-Box Models with Several Restrictions}, author={Carola Doerr and J. Lengler}, journal={Algorithmica}, year={2016}, volume={78}, pages={610-640} }

Black-box complexity studies lower bounds for the efficiency of general-purpose black-box optimization algorithms such as evolutionary algorithms and other search heuristics. Different models exist, each one being designed to analyze a different aspect of typical heuristics such as the memory size or the variation operators in use. While most of the previous works focus on one particular such aspect, we consider in this work how the combination of several algorithmic restrictions influence the… Expand

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#### 21 Citations

OneMax in Black-Box Models with Several Restrictions

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- GECCO
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This work shows that the (μ+λ) elitist memory-restricted ranking-based black-box complexity of OneMax is as small as (an advanced version of) the information-theoretic lower bound, and enlivens the quest for natural evolutionary algorithms optimizing OneMax in o(n log n) iterations. Expand

Elitist Black-Box Models: Analyzing the Impact of Elitist Selection on the Performance of Evolutionary Algorithms

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The (1+1) Elitist Black-Box Complexity of LeadingOnes

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The permutation- and bit-invariant version of LeadingOnes is regarded and it is proved that its (1+1) elitist black-box complexity is Ω(n2), a bound that is matched by (1-1)-type evolutionary algorithms. Expand

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The permutation- and bit-invariant version of LeadingOnes is regarded and it is proved that its(1+1) elitist black-box complexity is VarOmega (n^2)Ω(n2), a bound that is matched by(1-1)-type evolutionary algorithms, a bound which shows that for LeadingOns the memory-restriction, together with the selection requirement, has a substantial impact on the best possible performance. Expand

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OneMax in Black-Box Models with Several Restrictions

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This work shows that the (μ+λ) elitist memory-restricted ranking-based black-box complexity of OneMax is as small as (an advanced version of) the information-theoretic lower bound, and enlivens the quest for natural evolutionary algorithms optimizing OneMax in o(n log n) iterations. Expand

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This work proposes a new elitist black-box model, in which algorithms are required to base all decisions solely on (a fixed number of) the best search points sampled so far, and introduces the concept of $p-Monte Carlo black- box complexity, which measures the time it takes to optimize a problem with failure probability at most p. Expand

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