Sequential sampling for noisy optimisation with CMA-ES

This paper proposes a novel sequential sampling scheme to allocate samples to individuals in order to maximally inform the selection step in Covariance Matrix Adaptation Evolution Strategies (CMA-ES) for noisy function optimisation. More specifically we adopt the well-known Knowledge Gradient (KG) method to minimise the Kullback-Leibler divergence (relative entropy) between the distribution used for generating the next offspring population based...