In this paper, a hybrid algorithm based on the Multiple Offspring Sampling framework is presented and benchmarked on the BBOB-2010 noisy testbed. MOS allows the seamless combination of multiple metaheuristics in a hybrid algorithm capable of dynamically adjusting the participation of each of the composing algorithms. The experimental results show a good performance on functions with moderate noise. However, on functions with severe noise the results deteriorate, which suggests that further research should be conducted to find more adequate control mechanisms for these types of functions. Categories and Subject Descriptors