-
SUNNY-CP and the MiniZinc Challenge
In Constraint Programming (CP) a portfolio solver combines a variety of ...
read it
-
Proceedings of the 2018 XCSP3 Competition
This document represents the proceedings of the 2018 XCSP3 Competition. ...
read it
-
From MiniZinc to Optimization Modulo Theories, and Back (Extended Version)
Optimization Modulo Theories (OMT) is an extension of SMT that allows fo...
read it
-
Combining finite and continuous solvers
Combining efficiency with reliability within CP systems is one of the ma...
read it
-
Structure Based Extended Resolution for Constraint Programming
Nogood learning is a powerful approach to reducing search in Constraint ...
read it
-
claspfolio 2: Advances in Algorithm Selection for Answer Set Programming
To appear in Theory and Practice of Logic Programming (TPLP). Building o...
read it
-
Cable Tree Wiring – Benchmarking Solvers on a Real-World Scheduling Problem with a Variety of Precedence Constraints
Cable trees are used in industrial products to transmit energy and infor...
read it
sunny-as2: Enhancing SUNNY for Algorithm Selection
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.
READ FULL TEXT
Comments
There are no comments yet.