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GECCO
2009
Springer

Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed

10 years 9 months ago
Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed
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 106 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. Categories and Subject Descriptors
Anne Auger
Added 24 Jul 2010
Updated 24 Jul 2010
Type Conference
Year 2009
Where GECCO
Authors Anne Auger
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