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2009
Springer

Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbed

10 years 7 months ago
Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbed
The (1+1)-CMA-ES is an adaptive stochastic algorithm for the optimization of objective functions defined on a continuous search space in a black-box scenario. In this paper, an independent restart version of the (1+1)-CMA-ES is implemented and benchmarked on the BBOB-2009 noise-free testbed. The maximum number of function evaluations per run is set to 104 times the search space dimension. The algorithm solves 23, 13 and 12 of 24 functions in dimension 2, 10 and 40, respectively. Categories and Subject Descriptors
Anne Auger, Nikolaus Hansen
Added 24 Jul 2010
Updated 24 Jul 2010
Type Conference
Year 2009
Where GECCO
Authors Anne Auger, Nikolaus Hansen
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