“…It is widely believed that (µ/µ, λ)-ES are more parallel (efficient for large population sizes) than (1, λ)-ES, which is shown by rough calculus as in e.g. [1] and by experiments with intermediate values of λ. Experimentally however, (µ/µ, λ)-ES are less efficient than (1, λ)-ES for a (sufficiently) large population size λ, for the usual parametrization of the algorithms, as shown in [6]; in particular, the theoretical speedups of ES for parallel optimization (the best possible speed-ups, for optimal algorithms) are far better than the results of current implementations. In this paper, we show that changing the parametrization of self-adaptive (µ/µ, λ)-ES leads to a huge improvement for large λ, and that, with this improvement we can reach the theoretical bounds shown in [7].…”