An enhanced features extractor for a portfolio of constraint solvers
An enhanced features extractor for a portfolio of constraint solvers
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用于约束求解器组合的增强型特征提取器
DOI：
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发表时间：
2013
期刊：
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通讯作者：
J. Mauro
中科院分区：
文献类型：
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作者：
R. Amadini;M. Gabbrielli;J. Mauro
最近的研究表明，一个任意有效的求解器可以显着优于一个投资组合的可能较慢的平均求解器。求解器的选择通常是通过（非）监督学习技术，利用从问题规范中提取的功能。在本文中，我们提出了一个有用的和灵活的框架，能够提取一个广泛的功能集的约束（满意度/优化）可能不同的建模语言定义的问题：MiniZinc，FlatZinc或XCSP。
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.