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Instance Generation via Generator Instances

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Principles and Practice of Constraint Programming (CP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11802))


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.

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  1. Akgün, Ö.: Extensible automated constraint modelling via refinement of abstract problem specifications. Ph.D. thesis, University of St Andrews (2014)

    Google Scholar 

  2. Akgun, O., et al.: Automated symmetry breaking and model selection in Conjure. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 107–116. Springer, Heidelberg (2013).

    Chapter  Google Scholar 

  3. Akgün, Ö., Miguel, I., Jefferson, C., Frisch, A.M., Hnich, B.: Extensible automated constraint modelling. In: AAAI 2011: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pp. 4–11. AAAI Press (2011).

  4. Barták, R.: On generators of random quasigroup problems. In: Hnich, B., Carlsson, M., Fages, F., Rossi, F. (eds.) CSCLP 2005. LNCS (LNAI), vol. 3978, pp. 164–178. Springer, Heidelberg (2006).

    Chapter  Google Scholar 

  5. Bezerra, L.C.T., López-Ibáñez, M., Stützle, T.: Automatic component-wise design of multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 20(3), 403–417 (2016).

    Article  Google Scholar 

  6. Di Gaspero, L., Rendl, A., Urli, T.: Balancing bike sharing systems with constraint programming. Constraints 21(2), 318–348 (2016).

    Article  MathSciNet  MATH  Google Scholar 

  7. Frisch, A.M., Harvey, W., Jefferson, C., Martínez-Hernández, B., Miguel, I.: Essence: a constraint language for specifying combinatorial problems. Constraints 13(3), 268–306 (2008).

    Article  MathSciNet  MATH  Google Scholar 

  8. Gent, I.P., et al.: Discriminating instance generation for automated constraint model selection. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 356–365. Springer, Cham (2014).

    Chapter  Google Scholar 

  9. Gent, I.P., Jefferson, C., Miguel, I.: Minion: a fast scalable constraint solver. In: Proceedings of ECAI 2006, pp. 98–102. IOS Press (2006).

  10. Gent, I.P., Walsh, T.: CSPlib: a benchmark library for constraints. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 480–481. Springer, Heidelberg (1999).

    Chapter  Google Scholar 

  11. Gorcitz, R., Kofman, E., Carle, T., Potop-Butucaru, D., de Simone, R.: On the scalability of constraint solving for static/off-line real-time scheduling. In: Sankaranarayanan, S., Vicario, E. (eds.) FORMATS 2015. LNCS, vol. 9268, pp. 108–123. Springer, Cham (2015).

    Chapter  MATH  Google Scholar 

  12. Hoos, H.H.: Automated algorithm configuration and parameter tuning. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 37–71. Springer, Heidelberg (2011).

    Chapter  Google Scholar 

  13. Julstrom, B.A.: Evolving heuristically difficult instances of combinatorial problems. In: GECCO 2009: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 279–286. ACM (2009).

  14. Lang, M., Kotthaus, H., Marwedel, P., Weihs, C., Rahnenführer, J., Bischl, B.: Automatic model selection for high-dimensional survival analysis. J. Stat. Comput. Simul. 85(1), 62–76 (2015).

    Article  MathSciNet  Google Scholar 

  15. López-Ibáñez, M., Dubois-Lacoste, J., Cáceres, L.P., Birattari, M., Stützle, T.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Persp. 3, 43–58 (2016).,

    Article  MathSciNet  Google Scholar 

  16. Marriott, K., Nethercote, N., Rafeh, R., Stuckey, P.J., Garcia Banda, M., Wallace, M.: The design of the Zinc modelling language. Constraints 13(3), 229–267 (2008).

    Article  MathSciNet  MATH  Google Scholar 

  17. Monette, J.N., Schaus, P., Zampelli, S., Deville, Y., Dupont, P.: A CP approach to the balanced academic curriculum problem. In: Seventh International Workshop on Symmetry and Constraint Satisfaction Problems, vol. 7 (2007).

  18. Moreno-Scott, J.H., Ortiz-Bayliss, J.C., Terashima-Marín, H., Conant-Pablos, S.E.: Challenging heuristics: evolving binary constraint satisfaction problems. In: GECCO 2012: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, ACM (2012).

  19. Muñoz, M.A., Villanova, L., Baatar, D., Smith-Miles, K.: Instance spaces for machine learning classification. Mach. Learn. 107(1), 109–147 (2018).

    Article  MathSciNet  MATH  Google Scholar 

  20. Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007).

    Chapter  Google Scholar 

  21. Nightingale, P., Akgün, Ö., Gent, I.P., Jefferson, C., Miguel, I., Spracklen, P.: Automatically improving constraint models in Savile Row. Artif. Intell. 251, 35–61 (2017).

    Article  MathSciNet  MATH  Google Scholar 

  22. Nightingale, P., Rendl, A.: Essence’ description 1.6.4 (2016).

  23. Smith-Miles, K., van Hemert, J.: Discovering the suitability of optimisation algorithms by learning from evolved instances. Ann. Math. Artif. Intell. 61(2), 87–104 (2011).

    Article  MathSciNet  MATH  Google Scholar 

  24. Ullrich, M., Weise, T., Awasthi, A., Lässig, J.: A generic problem instance generator for discrete optimization problems. In: GECCO 2018: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1761–1768. ACM (2018).

  25. Van Hentenryck, P., Michel, L., Perron, L., Régin, J.-C.: Constraint programming in OPL. In: Nadathur, G. (ed.) PPDP 1999. LNCS, vol. 1702, pp. 98–116. Springer, Heidelberg (1999).

    Chapter  Google Scholar 

  26. Zampelli, S., Deville, Y., Solnon, C.: Solving subgraph isomorphism problems with constraint programming. Constraints 15(3), 327–353 (2010).

    Article  MathSciNet  MATH  Google Scholar 

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

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Correspondence to Nguyen Dang .

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Akgün, Ö., Dang, N., Miguel, I., Salamon, A.Z., Stone, C. (2019). Instance Generation via Generator Instances. In: Schiex, T., de Givry, S. (eds) Principles and Practice of Constraint Programming. CP 2019. Lecture Notes in Computer Science(), vol 11802. Springer, Cham.

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  • Print ISBN: 978-3-030-30047-0

  • Online ISBN: 978-3-030-30048-7

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