Skip to main content
SpringerLink
Log in

Effect of transformations of numerical parameters in automatic algorithm configuration

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

We study the impact of altering the sampling space of parameters in automatic algorithm configurators. We show that a proper transformation can strongly improve the convergence towards better configurations; at the same time, biases about good parameter values, possibly based on misleading prior knowledge, may lead to wrong choices in the transformations and be detrimental for the configuration process. To emphasize the impact of the transformations, we initially study their effect on configuration tasks with a single parameter in different experimental settings. We also propose a mechanism for how to adapt towards an appropriate transformation and give exemplary experimental results of that scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Note that SMAC offers the possibility of a \(\mathcal {L}og(x)\) transformation but not that of \(\mathcal {RL}og(x)\).

References

  1. Ansótegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent, I.P. (ed.) Principles and Practice of Constraint Programming. CP 2009, volume 5732 of LNCS, pp. 142–157. Springer, Heidelberg (2009)

    Google Scholar 

  2. Çela, E.: The Quadratic Assignment Problem: Theory and Algorithms. Kluwer Academic Publishers, Dordrecht (1998)

    Book  Google Scholar 

  3. Cohn, H., Fielding, M.J.: Simulated annealing: searching for an optimal temperature. SIAM J. Optim. 9(3), 779–802 (1999)

    Article  MathSciNet  Google Scholar 

  4. Eiben, A.E., Smit, S.K.: Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol. Comput. 1(1), 19–31 (2011)

    Article  Google Scholar 

  5. Fielding, M.J.: Simulated annealing with an optimal fixed temperature. SIAM J. Optim. 11(2), 289–307 (2000)

    Article  MathSciNet  Google Scholar 

  6. Girerd, N., Rabilloud, M., Pibarot, P., Mathieu, P., Roy, P.: Quantification of treatment effect modification on both an additive and multiplicative scale. PLoS ONE 11(4), 1–14 (2016)

    Article  Google Scholar 

  7. Hoos, H.H.: Programming by optimization. Commun. ACM 55(2), 70–80 (2012)

    Article  Google Scholar 

  8. Hussin, M.S., Stützle, T.: Tabu search vs. simulated annealing for solving large quadratic assignment instances. Comput. Oper. Res. 43, 286–291 (2014)

    Article  Google Scholar 

  9. Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267–306 (2009)

    Article  Google Scholar 

  10. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello Coello, C.A. (ed.) Learning and Intelligent Optimization, 5th International Conference, LION 5, volume 6683 of LNCS, pp. 507–523. Springer, Heidelberg (2011)

    Google Scholar 

  11. Knol, M.J., VanderWeele, T.J., Groenwold, R.H.H., Klungel, O.H., Rovers, M.M., Grobbee, D.E.: Estimating measures of interaction on an additive scale for preventive exposures. Eur. J. Epidemiol. 26(6), 433–438 (2011)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  13. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953)

    Article  Google Scholar 

  14. Snoek, J., Swersky, K., Zemel, R., Adams, R.P.: Input warping for Bayesian optimization of non-stationary functions. In: Proceedings of the 31th International Conference on Machine Learning, vol. 32, pp. 1674–1682 (2014)

  15. Taillard, É.D.: Robust taboo search for the quadratic assignment problem. Parallel Comput. 17(4–5), 443–455 (1991)

    Article  MathSciNet  Google Scholar 

  16. Yuan, Z., Montes de Oca, M.A., Stützle, T., Birattari, M.: Continuous optimization algorithms for tuning real and integer algorithm parameters of swarm intelligence algorithms. Swarm Intell. 6(1), 49–75 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

This research has received funding from the COMEX Project (P7/36) within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stützle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director.

Author information

Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles (ULB), 50, Av. F. Roosevelt, 1050, Brussels, Belgium

    Alberto Franzin, Leslie Pérez Cáceres & Thomas Stützle

Authors
  1. Alberto Franzin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leslie Pérez Cáceres
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Stützle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto Franzin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Franzin, A., Pérez Cáceres, L. & Stützle, T. Effect of transformations of numerical parameters in automatic algorithm configuration. Optim Lett 12, 1741–1753 (2018). https://doi.org/10.1007/s11590-018-1240-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-018-1240-3

Keywords

Search

Navigation