A knowledge discovery approach to understanding relationships between scheduling problem structure and heuristic performance

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

Volumn 5851 LNCS, Issue , 2009, Pages 89-103

  Smith Miles, Kate A ^a   James, Ross J W ^b   Giffin, John W ^b   Tu, Yiqing ^a  

^a MONASH UNIVERSITY   (Australia)

^b UNIVERSITY OF CANTERBURY   (New Zealand)

Author keywords

Algorithm selection; Heuristics; Knowledge discovery; Performance prediction; Scheduling; Self organizing map

Indexed keywords

ALGORITHM DESIGN; ALGORITHM SELECTION; EARLY/TARDY SCHEDULING PROBLEM; FEATURE SPACE; KNOWLEDGE DISCOVERY; OPTIMIZATION PROBLEMS; PERFORMANCE PREDICTION; PROBLEM STRUCTURE; SCHEDULING HEURISTICS; SCHEDULING PROBLEM; SINGLE MACHINES;

ALGORITHMS; CONFORMAL MAPPING; DECISION TREES; OPTIMIZATION;

HEURISTIC METHODS;

EID: 72449184183     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-11169-3_7     Document Type: Conference Paper

References (35)

1. Rice, J.R.: The Algorithm Selection Problem. Adv. Comp. 15, 65-118 (1976)
2. Watson, J.P., Barbulescu, L., Howe, A.E., Whitley, L.D.: Algorithm Performance and Problem Structure for Flow-shop Scheduling. In: Proc. AAAI Conf. on Artificial Intelligence, pp. 688-694 (1999)
3. Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE T. Evolut. Comput. 1, 67 (1997)
4. Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: Satzilla-07: The Design and Analysis of An Algorithm Portfolio For SAT. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 712-727. Springer, Heidelberg (2007)
5. Leyton-Brown, K., Nudelman, E., Shoham, Y.: Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 556-569. Springer, Heidelberg (2002)
6. Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A Portfolio Approach to Algorithm Selection. In: Proc. IJCAI, pp. 1542-1543 (2003)
7. Nudelman, E., Leyton-Brown, K., Hoos, H., Devkar, A., Shoham, Y.: Understanding Random SAT: Beyond the Clauses-To-Variables Ratio. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 438-452. Springer, Heidelberg (2004)
8. Horvitz, E., Ruan, Y., Gomes, C., Kautz, H., Selman, B., Chickering, M.: A Bayesian Approach to Tackling Hard Computational Problems. In: Proc. 17th Conf. on Uncertainty in Artificial Intelligence, pp. 235-244. Morgan Kaufmann, San Francisco (2001)
9. Samulowitz, H., Memisevic, R.: Learning to solve QBF. In: Proc. 22nd AAAI Conf. on Artificial Intelligence, pp. 255-260 (2007)
10. Streeter, M., Golovin, D., Smith, S.F.: Combining multiple heuristics online. In: Proc. 22nd AAAI Conf. on Artificial Intelligence, pp. 1197-1203 (2007)
11. Vilalta, R., Drissi, Y.: A Perspective View and Survey of Meta-Learning. Artif. Intell. Rev. 18, 77-95 (2002)
12. Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York (1994)
13. Brazdil, P., Soares, C., Costa, J.: Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Mach. Learn. 50, 251-277 (2003)
14. Ali, S., Smith, K.: On Learning Algorithm Selection for Classification. Appl. Soft Comp. 6, 119-138 (2006)
15. Stützle, T., Fernandes, S.: New Benchmark Instances for the QAP and the Experimental Analysis of Algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 199-209. Springer, Heidelberg (2004)
16. Carchrae, T., Beck, J.C.: Applying Machine Learning to Low Knowledge Control of Optimization Algorithms. Comput. Intell. 21, 373-387 (2005)
17. Shaw, M.J., Park, S., Raman, N.: Intelligent Scheduling With Machine Learning Capabilities: The Induction of Scheduling Knowledge. IIE Trans. 24, 156-168 (1992)
18. Knowles, J.D., Corne, D.W.: Towards Landscape Analysis to Inform the Design of a Hybrid Local Search for the Multiobjective Quadratic Assignment Problem. In: Abraham, A., Ruiz-Del-Solar, J., Koppen, M. (eds.) Soft Computing Systems: Design, Management and Applications, pp. 271-279. IOS Press, Amsterdam (2002)
19. Merz, P.: Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms. Evol. Comp. 2, 303-325 (2004)
20. Watson, J., Beck, J.C., Howe, A.E., Whitley, L.D.: Problem Difficulty for Tabu Search in Job-Shop Scheduling. Artif. Intell. 143, 189-217 (2003)
21. Smith-Miles, K.A.: Cross-Disciplinary Perspectives on Meta-Learning For Algorithm Selection. ACM Computing Surveys (in press, 2009)
22. Baker, K.R., Scudder, G.D.: Sequencing With Earliness and Tardiness Penalties: A Review. Ops. Res. 38, 22-36 (1990)
23. James, R.J.W., Buchanan, J.T.: A Neighbourhood Scheme with a Compressed Solution Space for The Early/Tardy Scheduling Problem. Eur. J. Oper. Res. 102, 513-527 (1997)
24. Fry, T.D., Armstrong, R.D., Blackstone, J.H.: Minimizing Weighted Absolute Deviation in Single Machine Scheduling. IIE Transactions 19, 445-450 (1987)
25. Vollmann, T.E., Berry, W.L., Whybark, D.C., Jacobs, F.R.: Manufacturing Planning and Control for Supply Chain Management, 5th edn. McGraw Hill, New York (2005)
26. Krajewski, L.J., Ritzman, L.P.: Operations Management: Processes and Value Chains, 7th edn. Pearson Prentice Hall, New Jersey (2005)
27. Schiavinotto, T., Stützle, T.: A review of metrics on permutations for search landscape analysis. Comput. Oper. Res. 34, 3143-3153 (2007)
28. Pfahringer, B., Bensusan, H., Giraud-Carrier, C.G.: Meta-Learning by Landmarking Various Learning Algorithms. In: Proc. ICML, pp. 743-750 (2000)
29. Baker, K.B., Martin, J.B.: An Experimental Comparison of Solution Algorithms for the Single Machine Tardiness Problem. Nav. Res. Log. 21, 187-199 (1974)
30. Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An Emerging Direction in Modern Search Technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Meta-heuristics, pp. 457-474. Kluwer, Norwell (2002)
31. Smith, K.A.: Neural Networks for Prediction and Classification. In: Wang, J. (ed.) Encyclopaedia of Data Warehousing and Mining, vol. 2, pp. 865-869. Information Science Publishing, Hershey (2006)
32. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
33. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
34. Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biol. Cyber. 43, 59-69 (1982)
35. Achlioptas, D., Naor, A., Peres, Y.: Rigorous Location of Phase Transitions in Hard Optimization Problems. Nature 435, 759-764 (2005)