Towards Meta-Algorithm Selection

TL;DR

This paper explores the concept of meta-algorithm selection, where the process of choosing an algorithm is itself selected, and investigates its potential benefits and challenges through empirical analysis.

Contribution

It introduces the idea of meta-algorithm selection, discusses its implications, and empirically evaluates its effectiveness and limitations.

Findings

Meta-algorithm selection can be beneficial in certain cases.

Applying algorithm selection at the meta-level presents specific challenges.

Successful meta-algorithm selection is not universally guaranteed.

Abstract

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.