Efficient expected improvement estimation for continuous multiple ranking and selection

This paper considers the problem of identifying the best of a discrete set of alternatives for each of a set of correlated problem instances. We assume that the instances can be described by a set of continuous features and the performance of a particular alternative on a particular problem instance can only be estimated from noisy samples. A possible application is in manufacturing, where we would like to identify the best dispatching rule to...