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- W4297798135 abstract "Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new emph{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. Our model combines features of the ranking-based and the memory-restricted black-box models with elitist selection. We provide several examples for which the elitist black-box complexity is exponentially larger than that the respective complexities in all previous black-box models, thus showing that the elitist black-box complexity can be much closer to the runtime of typical evolutionary algorithms. We also introduce the concept of $p$-Monte Carlo black-box complexity, which measures the time it takes to optimize a problem with failure probability at most $p$. Even for small~$p$, the $p$-Monte Carlo black-box complexity of a function class $mathcal F$ can be smaller by an exponential factor than its typically regarded Las Vegas complexity (which measures the emph{expected} time it takes to optimize $mathcal F$)." @default.
- W4297798135 created "2022-10-01" @default.
- W4297798135 creator A5040561209 @default.
- W4297798135 creator A5084778631 @default.
- W4297798135 date "2015-08-27" @default.
- W4297798135 modified "2023-10-14" @default.
- W4297798135 title "Introducing Elitist Black-Box Models: When Does Elitist Selection Weaken the Performance of Evolutionary Algorithms?" @default.
- W4297798135 doi "https://doi.org/10.48550/arxiv.1508.06802" @default.
- W4297798135 hasPublicationYear "2015" @default.
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