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Conference Papers Year : 2009

Benchmarking the (1+1)-ES with One-Fifth Success rule on the BBOB-2009 Noisy Testbed

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Abstract

The (1+1)-ES with one-fifth success rule is one of the first and simplest stochastic algorithm proposed for optimization on a continuous search space in a black-box scenario. In this paper, we benchmark an independent-restart (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed. The maximum number of function evaluations used equals $10^{6}$ times the dimension of the search space. The algorithm could only solve 3 functions with moderate noise in 5-D and 2 functions in 20-D.
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Dates and versions

inria-00430516 , version 1 (08-11-2009)

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  • HAL Id : inria-00430516 , version 1

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Anne Auger. Benchmarking the (1+1)-ES with One-Fifth Success rule on the BBOB-2009 Noisy Testbed. ACM-GECCO Genetic and Evolutionary Computation Conference, Jul 2009, Montreal, Canada. ⟨inria-00430516⟩
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