Instance generation via generator instances
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Access to good benchmark instances is always desirable when developing new algorithms, new constraint models, or when comparing existing ones. Hand-written instances are of limited utility and are time-consuming to produce. A common method for generating instances is constructing special purpose programs for each class of problems. This can be better than manually producing instances, but developing such instance generators also has drawbacks. In this paper, we present a method for generating graded instances completely automatically starting from a class-level problem specification. A graded instance in our present setting is one which is neither too easy nor too difficult for a given solver. We start from an abstract problem specification written in the Essence language and provide a system to transform the problem specification, via automated type-specific rewriting rules, into a new abstract specification which we call a generator specification. The generator specification is itself parameterised by a number of integer parameters; these are used to characterise a certain region of the parameter space. The solutions of each such generator instance form valid problem instances. We use the parameter tuner irace to explore the space of possible generator parameters, aiming to find parameter values that yield graded instances. We perform an empirical evaluation of our system for five problem classes from CSPlib, demonstrating promising results.
Akgun , O , Dang , N , Miguel , I J , Salamon , A Z & Stone , C L 2019 , Instance generation via generator instances . in T Schiex & S de Givry (eds) , Principles and Practice of Constraint Programming : 25th International Conference, CP 2019, Stamford, CT, USA, September 30 – October 4, 2019, Proceedings . Lecture Notes in Computer Science (Programming and Software Engineering) , vol. 11802 , Springer , Cham , pp. 3-19 , 25th International Conference on Principles and Practice of Constraint Programming (CP 2019) , Stamford , United States , 30/09/19 . https://doi.org/10.1007/978-3-030-30048-7_1conference
Principles and Practice of Constraint Programming
Copyright © 2019 Springer Nature Switzerland AG. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-3-030-30048-7_1
DescriptionFunding: UK EPSRC grant EP/P015638/1.
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