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

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

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Anne Auger, Nikolaus Hansen
• Keywords: Adaptive Stochastic Algorithm, Continuous Search Space, GECCO 2009, Optimization, Search Space