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Instance Generation via Generator Instances

  • Özgür Akgün
  • Nguyen DangEmail author
  • Ian Miguel
  • András Z. Salamon
  • Christopher Stone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11802)

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 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.

Keywords

Automated modelling Instance generation Parameter tuning 

Notes

Acknowledgements

This work is supported by EPSRC grant EP/P015638/1 and used the Cirrus UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Özgür Akgün
    • 1
  • Nguyen Dang
    • 1
    Email author
  • Ian Miguel
    • 1
  • András Z. Salamon
    • 1
  • Christopher Stone
    • 1
  1. 1.School of Computer ScienceUniversity of St AndrewsSt AndrewsUK

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