  We present GLASSES: Global optimisation with Look-Ahead through Stochastic
Simulation and Expected-loss Search. The majority of global optimisation
approaches in use are myopic, in only considering the impact of the next
function value; the non-myopic approaches that do exist are able to consider
only a handful of future evaluations. Our novel algorithm, GLASSES, permits the
consideration of dozens of evaluations into the future. This is done by
approximating the ideal look-ahead loss function, which is expensive to
evaluate, by a cheaper alternative in which the future steps of the algorithm
are simulated beforehand. An Expectation Propagation algorithm is used to
compute the expected value of the loss.We show that the far-horizon planning
thus enabled leads to substantive performance gains in empirical tests.
