  Natural evolution strategies are a class of approximate-gradient black-box
optimizers that have been successfully used for continuous parameter spaces. In
this paper, we derive NES algorithms for discrete parameter spaces and
demonstrate their effectiveness in tasks involving discrete parameters.
