Boosting as a Metaphor for Algorithm Design

Kevin Leyton-Brown, Eugene Nudelman, Galen Andrew, Jim McFadden, Yoav Shoham

Abstract: Although some algorithms are better than others on average, there is rarely a best algorithm for a given problem. Instead, different algorithms often perform well on different problem instances. Not surprisingly, this phenomenon is most pronounced among algorithms for solving \(\mathcal{NP}\)-hard problems, when runtimes are highly variable from instance to instance. When algorithms exhibit high runtime variance, one is faced with the problem of deciding which algorithm to use for each particular instance; in 1976 Rice dubbed this the “algorithm selection problem” [8]. More recent work on this problem includes [5,4].