A machine learning approach to algorithm selection for NP-hard optimization problems: A case study on the MPE problem

Annals of Operations Research

Volumn 156, Issue 1, 2007, Pages 61-82

  Guo, Haipeng ^a   Hsu, William H ^b  

^a BNU HKBU UNITED INTERNATIONAL COLLEGE   (China)

^b Sunflower Networking Group   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34548737646     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-007-0229-6     Document Type: Article

References (52)

1. Abdelbar, A. M., & Hedetniemi, S. M. (1998). Approximating MAPs for belief networks is NP-hard and other theorems. Artificial Intelligence, 102, 21-38.
2. Breese, J. S., & Horvitz, E. (1990). Ideal reformulation of belief networks. In UAI90 (pp. 129-144).
3. Cooper, G., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309-347.
4. Fink, E. (1998). How to solve it automatically: selection among problem-solving methods. In Proceedings of the fourth international conference on artificial intelligence planning systems (pp. 128-136).
5. Fung, R., & Chang, K. C. (1989). Weighting and integrating evidence for stochastic simulation in Bayesian networks. Uncertainty in Artificial Intelligence, 5, 209-219.
6. Gent, I. P., & Walsh, T. (1993). An empirical analysis of search in GSAT. Journal of Artificial Intelligence Research, 1, 47-59.
7. Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer Academic.
8. Gomes, C. P., & Selman, B. (1997). Algorithm portfolio design: theory vs. practice. In UAI97 (pp. 190-197).
9. Guo, H. (2003). Algorithm selection for sorting and probabilistic inference: a machine learning-based approach. PhD thesis, Kansas State University.
10. Guo, H., Boddhireddy, P., & Hsu, W. (2004). Using Ant algorithm to solve MPE. In The 17th Australian joint conference on artificial intelligence, Dec. 2004, Cairns, Australia.
11. Hooker, J. (1994). Needed: an empirical science of algorithms. Operations Research, 42, 201-212.
12. Hoos, H., & Stutzle, T. (1998). Evaluating Las Vegas algorithms-pitfalls and remedies. In UAI98.
13. Hoos, H., & Stutzle, T. (2000). Local search algorithms for SAT: an empirical evaluation. Journal of Automated Reasoning, 24(4), 421-481.
14. Horvitz, E. (1990). Computation and action under bounded resources. PhD thesis, Stanford University.
15. Horvitz, E., Ruan, Y., Kautz, H., Selman, B., & Chickering, D. M. (2001). A Bayesian approach to tackling hard computational problems. In UAI01 (pp. 235-244).
16. Houstis, E. N., Catlin, A. C., Rice, J. R., Verykios, V. S., Ramakrishnan, N., & Houstis, C. (2000). PYTHIA-II: a knowledge/database system for managing performance data and recommending scientific software. ACM Transactions on Mathematical Software, 26(2), 227-253.
17. Hutter, F. (2005). Stochastic local search for solving the most probable explanation problem in Bayesian networks. M.S. thesis, Intellectics Group, Darmstadt University of Technology.
18. Ide, J. S., & Cozman, F. G. (2002). Random generation of Bayesian networks. In Brazilian symposium on artificial intelligence, Pernambuco, Brazil.
19. Jensen, F. V., Olesen, K. G., & Anderson, K. (1990). An algebra of Bayesian belief universes for knowledge-based systems. Networks, 20, 637-659.
20. Jitnah, N., & Nicholson, A. E. (1998). Belief network algorithms: a study of performance based on domain characterization. In Learning and reasoning with complex representations (Vol. 1359, pp. 169-188). New York: Springer.
21. Johnson, D. S. (2002). A theoretician's guide to the experimental analysis of algorithms. In M. H. Goldwasser, D. S. Johnson & C. C. McGeoch (Eds.), Data structures, near neighbor searches, and methodology: fifth and sixth DIMACS implementation challenges (pp. 215-250).
22. Kask, K., & Dechter, R. (1999). Stochastic local search for Bayesian networks. In Workshop on AI and statistics 99 (pp. 113-122).
23. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
24. Lagoudakis, M., & Littman, M. (2001). Learning to select branching rules in the DPLL procedure for satisfiability. Electronic notes in discrete mathematics (ENDM): Vol. 9. LICS 2001 workshop on theory and applications of satisfiability testing (SAT 2001), Boston, MA, June 2001.
25. Lagoudakis, M., Littman, M., & Parr, R. (2001). Selection the right algorithm. In Proceedings of the 2001 AAAI fall symposium series: using uncertainty within computation, Cape Cod, MA, November 2001.
26. Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems (with discussion). Journal of the Royal Statistical Society Series B, 50, 157-224.
27. Leyton-Brown, K., Nudelman, E., & Shoham, Y. (2002). Learning the empirical hardness of optimization problems: the case of combinatorial auctions. In Constraint programming 2002 (CP-02).
28. Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., & Shoham, Y. (2003a). A portfolio approach to algorithm selection. In IJCAI.
29. Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., & Shoham, Y. (2003b). Boosting as a metaphor for algorithm design. Preprint.
30. Littman, M. (1999). Initial experiments in stochastic search for Bayesian networks. In Proceedings of the sixteenth national conference on artificial intelligence (pp. 667-672).
31. Lobjois, L., & Lema, M. (1998). Branch and bound algorithm selection by performance prediction. In Proceedings of the fifteenth national/tenth conference on AI/innovative applications of AI (pp. 353-358).
32. Lucks, M., & Gladwell, I. (1992). Automated selection of mathematical software. ACM Transactions on Mathematical Software, 18(1), 11-34.
33. Mannila, H. (1985). Instance complexity for sorting and NP-complete problems. PhD thesis, Department of Computer Science, University of Helsinki.
34. McGeoch, C. C. (1986). Experimental analysis of algorithms. PhD thesis, Carnegie-Mellon University.
35. Mengshoel, O. J. (1999). Efficient Bayesian network inference: genetic algorithms, stochastic local search, and abstraction. Computer Science Department, University of Illinois at Urbana-Champaign.
36. Moret, B. M. E. (2002). Towards a discipline of experimental algorithmics. In Data structures, near neighbor searches, and methodology: fifth and sixth DIMACS implementation challenges. DIMACS monographs (Vol. 59, pp. 197-213).
37. Orponen, P., Ko, K., Schoning, U., & Watanabe, O. (1994). Instance complexity. Journal of the ACM, 41(1), 96-121.
38. Park, J. D. (2002). Using weighted MAX-SAT engines to solve MPE. In Proceedings of the 18th national conference on artificial intelligence (AAAI) (pp. 682-687).
39. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. San Mateo: Morgan Kaufmann.
40. Ramakrishnan, N., & Valdes-perez, R. E. (2000). Note on generalization in experimental algorithmics. ACM Transactions on Mathematical Software, 26(4), 568-580.
41. Rardin, R. L., & Uzsoy, R. (2001). Experimental evaluation of heuristic optimization algorithms: a tutorial. Journal of Heuristics, 7(3), 261-304.
42. Rice, J. R. (1976). The algorithm selection problem. In M. V. Zelkowitz (Ed.), Advances in computers (Vol. 15, pp. 65-118).
43. Ruan, Y., Kautz, H., & Horvitz, E. (2004). The backdoor key: a path to understanding problem hardness. In Nineteenth national conference on artificial intelligence, San Jose, CA, 2004.
44. Russell, S., & Norvig, P. (2003). Artificial intelligence: a modern approach. Englewood Cliffs: Prentice-Hall.
45. Sanders, P. (2002). Presenting data from experiments in algorithmics. In Experimental algorithmics: from algorithm design to robust and efficient software (pp. 181-196). New York: Springer.
46. Santos, E. (1991). On the generation of alternative explanations with implications for belief revision. In UAI 91 (pp. 339-347).
47. Santos, E., Shimony, S. E., & Williams, E. (1995). On a distributed anytime architecture for probabilistic reasoning (Technique report AFIT/EN/TR94-06). Department of Electrical and Computer Engineering, Air Force Institute of Technology.
48. Shafer, G., & Shenoy, P. (1990). Probability propagation. Annals of Mathematics and Artificial Intelligence, 2, 327-352.
49. Shimony, S. E., & Charniak, E. (1999). A new algorithm for finding MAP assignments to belief network. In UAI 99 (pp. 185-193).
50. Shimony, S. E., & Domshlak, C. (2003). Complexity of probabilistic reasoning in directed-path singly connected Bayes networks. Artificial Intelligence, 151, 213-225.
51. Witten, I. H., & Frank, E. (1999). Data mining: practical machine learning tools and techniques with Java implementations. Los Altos: Morgan Kaufmann.
52. Zilberstein, S. (1993). Operational rationality through compilation of anytime algorithms. PhD thesis, University of California at Berkeley.