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9

GECCO
2009
Springer

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

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

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We benchmark an independent-restart-(1+1)-CMA-ES on the BBOB-2009 noisy 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. The maximum number of function evaluations used here equals 104 times the dimension of the search space. The algorithm could only solve 4 functions with moderate noise in 5-D and 2 functions in 20-D. Categories and Subject Descriptors

Anne Auger, Nikolaus Hansen

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Adaptive Stochastic Algorithm | Continuous Search Space | GECCO 2009 | Optimization | Search Space |

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Added	24 Jul 2010
Updated	24 Jul 2010
Type	Conference
Year	2009
Where	GECCO
Authors	Anne Auger, Nikolaus Hansen

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