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Algorithm selection (AS) deals with the automatic selection of an algorithmfrom a fixed set of candidate algorithms most suitable for a specific instanceof an algorithmic problem class, where "suitability" often refers to analgorithm's runtime. Due to possibly extremely long runtimes of candidatealgorithms, training data for algorithm selection models is usually generatedunder time constraints in the sense that not all algorithms are run tocompletion on all instances. Thus, training data usually comprises censoredinformation, as the true runtime of algorithms timed out remains unknown.However, many standard AS approaches are not able to handle such information ina proper way. On the other side, survival analysis (SA) naturally supportscensored data and offers appropriate ways to use such data for learningdistributional models of algorithm runtime, as we demonstrate in this work. Weleverage such models as a basis of a sophisticated decision-theoretic approachto algorithm selection, which we dub Run2Survive. Moreover, taking advantage ofa framework of this kind, we advocate a risk-averse approach to algorithmselection, in which the avoidance of a timeout is given high priority. In anextensive experimental study with the standard benchmark ASlib, our approach isshown to be highly competitive and in many cases even superior tostate-of-the-art AS approaches.

Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis