sunny-as2: Enhancing SUNNY for Algorithm Selection

TL;DR

This paper presents sunny-as2, an improved algorithm selection method based on SUNNY, tailored for diverse ASlib scenarios, demonstrating enhanced performance through advanced feature selection and validation techniques.

Contribution

The paper introduces technical advancements to sunny-as2, including wrapper-based feature selection, combined training approaches, and nested cross-validation, improving its effectiveness across various scenarios.

Findings

sunny-as2 outperforms previous versions in runtime minimization

performance varies with different AS scenarios

demonstrates strengths and weaknesses in diverse settings

Abstract

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems, and its parallel version won many gold medals in the Open category of the MiniZinc Challenge -- the yearly international competition for CP solvers. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as2, an algorithm selector based on SUNNY for ASlib scenarios. A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work, we present the technical advancements of sunny-as2, including: (i) wrapper-based feature selection; (ii) a training approach combining feature selection and neighbourhood size configuration; (iii) the application of nested cross-validation. We show how sunny-as2 performance varies depending on the considered AS scenarios, and we discuss its strengths and weaknesses. Finally, we also show how sunny-as2 improves on its preliminary version submitted to OASC.