Motivations for the Development of a Multi-objective Algorithm Configurator

Nguyen Thi Thanh Dang, Patrick De Causmaecker

2014

Abstract

In the single-objective automated algorithm configuration problem, given an algorithm with a set of parameters that need to be configured and a distribution of problem instances, the automated algorithm configurator will try to search for a good parameter configuration based on a pre-defined performance measure. In this paper, we point out two motivations for the development of a multi-objective algorithm configurator, in which more than one performance measure are considered at the same time. The first motivation is a parameter configuration case study for a deterministic single machine scheduling algorithm with two performance measures: minimization of the average running time and maximization of the total number of optimal solutions. The second one is the configuration problem for non-exact multi-objective optimization algorithms. In addition, a discussion of solving approach for the first motivating problem is also presented.

References

  1. Ansótegui, C., Sellmann, M., & Tierney, K. (2009). A gender-based genetic algorithm for the automatic configuration of algorithms. In Principles and Practice of Constraint Programming-CP 2009 (pp. 142-157). Springer Berlin Heidelberg.
  2. Basseur, M., Zeng, R. Q., & Hao, J. K. (2012). Hypervolume-based multi-objective local search. Neural Computing and Applications, 21(8), 1917- 1929.
  3. Brockhoff, D., Wagner, T., & Trautmann, H. (2012). On the Properties of the R2 Indicator. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference (pp. 465-472). ACM.
  4. Dubois-Lacoste, J., López-Ibáñez, M., & Stützle, T. (2011). Improving the anytime behavior of two-phase local search. Annals of mathematics and artificial intelligence, 61(2), 125-154.
  5. Hoos, H. H. (2012). Automated algorithm configuration and parameter tuning. In Autonomous Search (pp. 37- 71). Springer Berlin Heidelberg.
  6. Hutter, F., Hoos, H. H., Leyton-Brown, K., & Stützle, T. (2009). ParamILS: an automatic algorithm configuration framework. Journal of Artificial Intelligence Research, 36(1), 267-306.
  7. Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2010). Automated configuration of mixed integer programming solvers. In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (pp. 186-202). Springer Berlin Heidelberg.
  8. Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011). Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization (pp. 507-523). Springer Berlin Heidelberg.
  9. Jaeggi, D. M., Parks, G. T., Kipouros, T., & Clarkson, P. J. (2008). The development of a multi-objective tabu search algorithm for continuous optimisation problems. European Journal of Operational Research, 185(3), 1192-121
  10. Knowles, J., Thiele, L., & Zitzler, E. (2006). A tutorial on the performance assessment of stochastic multiobjective optimizers. Tik report, 214, 327-332.
  11. López-Ibánez, M., Dubois-Lacoste, J., Stützle, T., & Birattari, M. (2011). The irace package, iterated race for automatic algorithm configuration. IRIDIA, Université Libre de Bruxelles, Belgium, Tech. Rep. TR/IRIDIA/2011-004.
  12. Smit, S. K., & Eiben, A. E. (2011). Multi-problem parameter tuning using BONESA. In Artificial Evolution (pp. 222-233).
  13. Stützle, T., & López-Ibáñez, M. (2013, July). Automatic (offline) configuration of algorithms. In Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion (pp. 893-918). ACM.
  14. Tanaka, S., & Fujikuma, S. (2012). A dynamicprogramming-based exact algorithm for general single-machine scheduling with machine idle time. Journal of Scheduling, 15(3), 347-361.
  15. Zitzler, E., Knowles, J., & Thiele, L. (2008). Quality assessment of pareto set approximations. In Multiobjective Optimization (pp. 373-404). Springer Berlin Heidelberg.
Download


Paper Citation


in Harvard Style

Dang N. and De Causmaecker P. (2014). Motivations for the Development of a Multi-objective Algorithm Configurator . In Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-017-8, pages 328-333. DOI: 10.5220/0004925203280333


in Bibtex Style

@conference{icores14,
author={Nguyen Thi Thanh Dang and Patrick De Causmaecker},
title={Motivations for the Development of a Multi-objective Algorithm Configurator},
booktitle={Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2014},
pages={328-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004925203280333},
isbn={978-989-758-017-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Motivations for the Development of a Multi-objective Algorithm Configurator
SN - 978-989-758-017-8
AU - Dang N.
AU - De Causmaecker P.
PY - 2014
SP - 328
EP - 333
DO - 10.5220/0004925203280333