Performance prediction and automated tuning of randomized and parametric algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4204 LNCS, Issue , 2006, Pages 213-228

  Hutter, Frank ^a   Hamadi, Youssef ^b   Hoos, Holger H ^a   Leyton Brown, Kevin ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMBINATORIAL MATHEMATICS; MATHEMATICAL MODELS; PARAMETER ESTIMATION;

DISTRIBUTION-SPECIFIC PARAMETER; PARAMETER SETTINGS; RUN-TIME DISTRIBUTIONS; SEARCH ALGORITHMS;

LEARNING SYSTEMS;

EID: 33750380289     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11889205_17     Document Type: Conference Paper

References (25)

1. B. Adenso-Daz and M. Laguna. Fine-tuning of algorithms using fractional experimental design and local search. Operations Research, 54(1), 2006. To appear.
2. R. Battiti and M. Brunato. Reactive search: machine learning for memory-based heuristics. Technical Report DIT-05-058, Università Degli Studi Di Trento, Dept. of information and communication technology, Trento, Italy, September 2005.
3. M. Birattari, T. Stützle, L. Paquete, and K. Varrentrapp. A racing algorithm for configuring metaheuristics. In Proc. of GECCO-02, pages 11-18, 2002.
4. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
5. F. Brglez, X. Y. Li, and M. F. Stallmann. On SAT instance classes and a method for reliable performance experiments with SAT solvers. Annals of Mathematics and Artificial Intelligence, 0:1-34, 2004.
6. T. Carchrae and J. C. Beck. Applying machine learning to low-knowledge control of optimization algorithms. Computational Intelligence, 21(4):372-387, 2005.
7. D. A. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. JAIR, 4:129-145, 1996.
8. C. Gebruers, B. Hnich, D. Bridge, and E. Freuder. Using CBR to select solution strategies in constraint programming. In Proc. of ICCBR-05, pages 222-236, 2005.
9. I. P. Gent, H. H. Hoos, P. Prosser, and T. Walsh. Morphing: Combining structure and randomness. In Proc. of AAAI-99, pages 654-660, Orlando, Florida, 1999.
10. C. P. Gomes and B. Selman. Problem structure in the presence of perturbations. In Proc. of AAAI-97, 1997.
11. C. P. Gomes, B. Selman, N. Crato, and H. Kautz. Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. J. of Automated Reasoning, 24(1), 2000.
12. H. H. Hoos. On the run-time behaviour of stochastic local search algorithms for SAT. In Proc. of AAAI-99, pages 661-666, 1999.
13. H. H. Hoos. An adaptive noise mechanism for WalkSAT. In Proc. of AAAI-02, pages 655-660, 2002.
14. H. H. Hoos and T. Stützle. Stochastic Local Search - Foundations & Applications. Morgan Kaufmann, SF, CA, USA, 2004.
15. E. Horvitz, Y. Ruan, C. P. Gomes, H. Kautz, B. Selman, and D. M. Chickering. A Bayesian approach to tackling hard computational problems. In Proc. of UAI-0I, 2001.
16. F. Hutter and Y. Hamadi. Parameter adjustment based on performance prediction: Towards an instance-aware problem solver. Technical Report MSR-TR-2005-125, Microsoft Research, Cambridge, UK, December 2005.
17. F. Hutter, D. A. D. Tompkins, and H. H. Hoos. Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In Proc. of CP-02, volume 2470, pages 233-248, 2002.
18. M. G. Lagoudakis and M. L. Littman. Learning to select branching rules in the DPLL procedure for satisfiability. In Electronic Notes in Discrete Mathematics (ENDM), 2001.
19. K. Leyton-Brown, E. Nudelman, and Y. Shoham. Learning the empirical hardness of optimization problems: The case of combinatorial auctions. In Proc. of CP-02, 2002.
20. D. McAllester, B. Selman, and H. Kautz. Evidence for invariants in local search. In Proc. of AAAI-97, pages 321-326, 1997.
21. E. Nudelman, K. Leyton-Brown, H. H. Hoos, A. Devkar, and Y. Shoham. Understanding random SAT: Beyond the clauses-to-variables ratio. In Proc. of CP-04, 2004.
22. D. J. Patterson and H. Kautz. Auto-WalkSAT: a self-tuning implementation of WalkSAT. In Electronic Notes in Discrete Mathematics (ENDM), 9, 2001.
23. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006.
24. B. Srivastava and A. Mediratta. Domain-dependent parameter selection of search-based algorithms compatible with user performance criteria. In Proc. of AAAI-05, 2005.
25. J. R. Thornton. Clause weighting local search for SAT. J. of Automated Reasoning, 2005.