Algorithm Selection on a Meta Level

TL;DR

This paper introduces meta algorithm selection, a method to combine existing algorithm selectors using ensemble techniques, significantly improving performance over individual selectors in algorithm selection tasks.

Contribution

It presents a general framework for meta algorithm selection and demonstrates that ensembles of selectors outperform single methods through extensive experiments.

Findings

Ensembles of algorithm selectors outperform individual selectors.

Meta algorithm selection can significantly improve algorithm selection accuracy.

The proposed methods have the potential to establish new state-of-the-art results.

Abstract

The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.