Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed

The (1+1)-ES with one-fifth success rule is one of the first and simplest stochastic algorithm proposed for optimization on a continuous search space in a black-box scenario. In this paper, we benchmark an independent-restart (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed. The maximum number of function evaluations used equals $10^{6}$ times the dimension of the search space. The algorithm could only solve 3 functions with moderate noise in 5-D and 2 functions in 20-D.