Parallel Adaptive Survivor Selection

Abstract

Ranking and selection (R&S) procedures in simulation optimization simulate every feasible solution to provide global statistical error control, often selecting a single solution in finite time that is optimal or near-optimal with high probability. By exploiting parallel computing advancements, large-scale problems with hundreds of thousands and even millions of feasible solutions are suitable for R&S. Naively parallelizing existing R&S methods originally designed for a serial computing setting is generally ineffective, however, as many of these conventional methods uphold family-wise error guarantees that suffer from multiplicity and require pairwise comparisons that present a computational bottleneck. Parallel adaptive survivor selection (PASS) is a new framework specifically designed for large-scale parallel R&S. By comparing systems to an adaptive “standard” that is learned as the algorithm progresses, PASS eliminates inferior solutions with false elimination rate control and with computationally efficient aggregate comparisons rather than pairwise comparisons. PASS satisfies desirable theoretical properties and performs effectively on realistic problems.