# An Enhanced Features Extractor for a Portfolio of Constraint Solvers

arXiv: https://arxiv.org/abs/1308.0227 · HTML: https://mlsanity.com/paper/1308.0227/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2013.

## Built on by (most cited first, 5 of 5)

- [A Framework for Generating Informative Benchmark Instances](https://mlsanity.com/paper/2205.14753/) (2022)
- [Learning to Select SAT Encodings for Pseudo-Boolean and Linear Integer Constraints](https://mlsanity.com/paper/2307.09342/) (2023)
- [Predictive Machine Learning of Objective Boundaries for Solving COPs](https://mlsanity.com/paper/2111.03160/) (2021)
- [Automatic Feature Learning for Essence: a Case Study on Car Sequencing](https://mlsanity.com/paper/2409.15158/) (2024)
- [Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection](https://mlsanity.com/paper/2608.17170/) (2026)

## Abstract (arXiv)

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.