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 10 4 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.
benchmarking evolutionary computation cma-es black-box optimization