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

Authors:
Anne Auger;Nikolaus Hansen
Affiliations:
INRIA Saclay Ile-de-France, Orsay, France;Microsoft Research-INRIA Joint Centre, Orsay Cedex, France
Venue:
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Year:
2009

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Abstract

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.