An Enhanced Features Extractor for a Portfolio of Constraint Solvers

TL;DR

This paper introduces a flexible feature extraction framework for constraint problem portfolios, demonstrating that these features enable effective solver selection across different modeling languages, improving performance over existing methods.

Contribution

The paper presents a novel, adaptable feature extractor for constraint problems in multiple languages, enhancing solver portfolio performance and competitiveness.

Findings

Features improve solver selection accuracy

Framework is effective across different modeling languages

Performance is competitive with state-of-the-art CSP portfolios

Abstract

Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specification. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP. We also report some empirical results showing that the performances that can be obtained using these features are effective and competitive with state of the art CSP portfolio techniques.