As an evolutionary search method, we use Covariance Matrix Adaptation Evolution Strategy (CMA-ES) [ , ], a state-of-the-art, stochastic optimizer for rugged, non-convex, black-box objective functions. In CMA-ES, new COFs are stochastically selected from the search space by sampling from a multivariate Gaussian distribution over the feature space. The mean and covariance matrix of the distribution are updated over the search process, as COFs are acquired and evaluated in batches (generations). To update the distribution, with the aim of increasing the likelihood of acquiring and taking search steps towards high-performing COFs, (i) the mean is updated using a weighted average of the most high-performing COFs in the generation (a selection mechanism) and (ii) the covariance matrix is updated using a weighted average of the best search steps (from the mean) towards the high-performing COFs [ ].