Towards Meta-Algorithm Selection
This paper addresses the problem of selecting the best algorithm selector for researchers and practitioners, but the benefits are limited and the approach is not broadly effective.
This paper explores the application of algorithm selection (AS) to AS algorithms themselves, termed meta-AS. The goal is to select the best algorithm selector for a given problem instance, which then selects the final algorithm. Empirically, meta-AS can be beneficial in some cases, but successful AS approaches struggle with the meta-level problem.
Instance-specific algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidates most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Over the past years, a plethora of algorithm selectors have been proposed. As an algorithm selector is again an algorithm solving a specific problem, the idea of algorithm selection could also be applied to AS algorithms, leading to a meta-AS approach: Given an instance, the goal is to select an algorithm selector, which is then used to select the actual algorithm for solving the problem instance. We elaborate on consequences of applying AS on a meta-level and identify possible problems. Empirically, we show that meta-algorithm-selection can indeed prove beneficial in some cases. In general, however, successful AS approaches have problems with solving the meta-level problem.