Algorithm survival analysis

Abstract

Algorithm selection is typically based on models of algorithm performance,learned during a separate offline training sequence, which can be prohibitively expensive. In recent work, we adopted an online approach, in which models of the runtime distributions of the available algorithms are iteratively updated and used to guide the allocation of computational resources, while solving a sequence of problem instances. The models are estimated using survival analysis techniques, which allow us to reduce computation time, censoring the runtimes of the slower algorithms. Here, we review the statistical aspects of our online selection method, discussing the bias induced in the runtime distributions (RTD) models by the competition of different algorithms on the same problem instances. © 2010 Springer-Verlag Berlin Heidelberg.