| On 2019-10-16 at 14:30:00 (Brussels Time) |
Abstract
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 work, 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 constraint modelling language Essence 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 p arameterised 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 a number of problem classes from CSPlib, demonstrating promising results.
Keywords
Automated modelling, Instance generation, Parameter tuning
