Local Search and Backtracking vs Non-Systematic Backtracking

AAAI Fall Symposium - Technical Report

Volumn FS-01-04, Issue , 2001, Pages 109-115

  Prestwich, Steven ^a  

^a UNIVERSITY COLLEGE CORK   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LOCAL SEARCH (OPTIMIZATION);

BACKTRACKING ALGORITHM; COLORING ALGORITHMS; GRAPH COLORINGS; LOCAL SEARCH; MODIFIED ALGORITHMS; N-QUEEN; SATISFIABILITY; SEAL-ABILITY; STOCHASTIC LOCAL SEARCHES; VARIABLE ASSIGNMENT;

STOCHASTIC SYSTEMS;

EID: 84880829196     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (41)

1. Baker, A. B. 1994. The Hazardsof FancyBacktracking. In Proceedingsof the Twelfth National Conferenceon Artificial Intelligence, 1:288-293. Seattle, WA: AAAI Press.
2. Bayardo, R. J. Jr, and Schrag, R. 1997. Using CSPLookBack Techniques to Solve Real WorldSATInstances. In Proceedingsof the Fourteenth National Conferenceon Artificial Intelligence, 203-208.Providence,RI: AAAI Press.
3. Br61az, D. 1979. NewMethodsto Color the Vertices of a Graph. Communicationsof the A CM22(4):251-256.
4. Crawford.J. M. 1993. Solving Satisfiability ProblemsUsing a Combinationof Systematic and Local Search. In Second DIMACS Challenge: Cliques, Coloring, and Satisfiability, RutgersUniversity,NJ.
5. Crawford, J. M., and Auton, L. D. 1996. ExperimentalResults on the Crossover Point in Random3SAT.Artificial Intelligence 81 (1-2):31-57.
6. Culberson, J. C.; BeachamA.; and Papp, D. 1995. Hiding Our Colors. In Proceedings of the CP’95Workshop on Studying and Solving Really HardProblems, Cassis, France.
7. Davis, M.; Logemann,G.; and Loveland, D. 1962. A Machine Program for TheoremProving. Communicationsof the ACM5:394-397.
8. de Backer,B.; Furnon,V.; Kilby, P.; Prosser, P.; and Shaw, P. 1997. Local Search in Constraint Programming:Application to the Vehicle Routing Problem.In Constraint Programming97, Proceedings of the Workshopon Industrial Constraint-Directed Scheduling.
9. Freeman,J. W.1994. Improvementsto Propositional Satisfiability Search Algorithms.Ph.D. diss., University of Pennsylvania,Philadelphia.
10. Freuder, E. C.; Dechter, R.; Ginsberg, M.L.; Selman,B.; and Tsang, E. 1995. Systematic Versus Stochastic Constraint Satisfaction. In Proceedings of the FourteenthInternational Joint Conferenceon Artificial Intelligence, 2027-2032. Montreal, Quebec: MorganKaufmann.
11. Ginsberg, M. L. 1993. DynamicBacktracking. Journal of Artificial Intelligence Research1:25-46.
12. Ginsberg, M. L., and McAllester, D. A. 1994. GSATand DynamicBacktracking. In Proceedings of the Fourth International Conferenceon Principles of KnowledgeRepresentation and Reasoning, 226-237. Bonn, Germany:Morgan Kaufmann.
13. Gomes,C.; Selman, B.; and Kautz, H. 1998. Boosting Combinatorial Search ThroughRandomization. In Proceedingsof the Fifteenth NationalConferenceonArtificial Intelligence, 431-437. Madison,WI:AAAI Press.
14. Gu, J. 1992. Efficient Local Search for VeryLarge-Scale Satisfiability Problems.SIGARTBulletin 3(1):8-12.
15. Harvey, W.D. 1995. NonsystematicBacktracking Search. Ph.D.diss., StanfordUniversity.
16. Hoos, H. H. 1998. Stochastic Local Search - Methods, Models, Applications. PhDdiss., Dept. of ComputerScience, TUDarmstadt.
17. Johnson, D. S., and Trick, M. A. eds. 1996. Cliques, Coloring and Satisfiability: Second DIMACS Implementation Challenge, DIMACS Series in Discrete Mathematics and Theoretical ComputerScience 26. AmericanMathematical Society.
18. J6nsson, A. K., and Ginsberg, M. L. 1993. Experimenting with NewSystematic and Nonsystematic Search Techniques. In Proceedings of the AAAISpring Symposiumon AI and NP-HardProblems. Stanford, California: AAAI Press.
19. Joslin D. E., and Clements, D. P. 1999. SqueakyWheel Optimization. Journalof Artificial Intelligence Research 10:353-373.
20. Jussien, N., and Lhomme,O. 2000. Local Search With Constraint Propagation and Conflict-Based Heuristics. In Proceedingsof the SeventeenthNational Conferenceon Artificial Intelligence, 169-174.Austin, TX:AAAI Press.
21. Langley, P. 1992. Systematic and Nonsystematic Search Strategies. In Proceedingsof the First International Conference on Artificial Intelligence PlanningSystems, 145-152. University of Maryland: MorganKaufman.
22. Li, C. M., and Anbulagan. 1997. Look-Ahead Versus Look-Backfor Satisfiability Problems. In Proceedings of the Third International Conferenceon Principles and Practice of Constraint Programming,341-355. Schloss Hagenburg, Austria: Lecture Notes in ComputerScience 1330, Springer-Verlag.
23. Minton,S.; Johnston, M.D.; Philips, A. B.; and Laird, P. 1992. MinimizingConflicts: A Heuristic Repair Method for Constraint Satisfaction and SchedulingProblems.Artificial lnteUigence58 (1-3): 160-205.
24. Morris, P. 1993. The Breakout Methodfor Escaping Local Minima.In Proceedingsof the Eleventh National Conference on Artificial Intelligence, 40-45. WashingtonDC: AAAIPress.
25. Pesant, G., and Gendreau, M. 1996. A View of Local Search in Constraint Programming. In Proceedingsof the SecondInternational Conferenceon Principles and Practice of Constraint Programming,353-366. Cambridge, MA:Lecture Notes in ComputerScience 1118, Springer-Verlag.
26. Pothos, D. G., and Richards, E. B. 1995. AnEmpirical Study of Min-Conflict Hill Climbing and WeakCommitment Search. In Proceedings of the CP-95Workshopon Studying and Solving Really HardProblems, 140-146. Cassis, France.
27. Prestwich, S. D. 2000a. Stochastic Local Search In Constrained Spaces. In Proceedingsof Practical Applications of Constraint TechnologyandLogic Programming, 27-39. Manchester, England: The Practical Applications CompanyLtd.
28. Prestwich, S. D. 2000b. A HybridSearch Architecture Applied to Hard Random3-SATand Low-Autocorrelation Binary Sequences.In Proceedingsof the Sixth International Conferenceon Principles and Practice of Constraint Programming, 337-352. Singapore: Lecture Notes in Computer Science 1894, Springer-Verlag.
29. Prestwich, S. D. 2001a. Coloration NeighbourhoodSearch With Forward Checking. Annals of Mathematicsand Artificial Intelligence, special issue on LargeScale Combinatorial Optimizationand Constraints. Forthcoming.
30. Prestwich, S. D. 200lb. Trading Completenessfor Scalability: HybridSearch for Cliques and Rulers. In Proceedings of the Third International Workshopon Integration of Al and ORTechniques in Constraint Programmingfor Combinatorial Optimization Problems, 159-174. Ashford, Kent, England. Forthcoming.
31. Resende, M. G. C., and Feo, T. 1996. AGRASP for Satisfiability. In (Johnsonand Trick 1996)499-520.
32. Schaerf, A. 1997. Combining Local Search and Look-Aheadfor Scheduling and Constraint Satisfaction Problems. In Proceedingsof the Fifteenth International Joint Conferenceon Artificial Intelligence, 1254-1259.Nagoya, Japan: MorganKaufmann.
33. Selman, B.; Levesque, H.; and Mitchell, D. 1992. ANew Methodfor Solving HardSatisfiability Problems. In Proceedingsof the TenthNationalConferenceon Artificial Intelligence, 440-446. SanJose, CA:AAAI Press.
34. Selman, B.; Kautz, H.; and Cohen,B. 1994. Noise Strategies for ImprovingLocal Search. In Proceedings of the Twelfth National Conferenceon Artificial Intelligence, 337-343. Seattle, WA:AAAIPress.
35. Shaw, P. 1998. Using Constraint Programmingand Local Search Methodsto Solve Vehicle Routing Problems. In Proceedingsof the FourthInternational Conferenceon Principles and Practice of Constraint Programming, 417-431. Pisa, Italy: Lecture Notes in ComputerScience 1520, Springer-Verlag.
36. Silva, J. P. M., and Sakallah, K. A. 1996. Conflict Analysis in SearchAlgorithmsfor Propositional Satisfiability. In Proceedingsof the Eighth International Conferenceon Tools with Artificial Intelligence. Toulouse, France: IEEE ComputerSociety Press.
37. Verfaillie, G., and Schiex, T. 1994. Solution Reusein Dy-namicConstraint Satisfaction Problems.In Proceedingsof the Twelfth NationalConferenceon Artificial Intelligence, 307-312. Seattle, WA:AAAIPress.
38. Wu,Z., and Wah,B. W.2000. AnEfficient Global-Search Strategy in Discrete LagrangianMethodsfor Solving Hard Satisfiability Problems.In Proceedingsof the Seventeenth NationalConferenceon Artificial Intelligence, 31 0-315. Austin, TX: AAAIPress.
39. Yokoo, M. 1994. Weak-CommitmentSearch for Solving Constraint Satisfaction Problems. In Proceedingsof the TwelfthNationalConferenceonArtificial Intelligence, 313-318. Seattle, WA:AAAIPress.
40. Zhang,H. 1997. SATO: AnEfficient Propositional Prover. In Proceedingsof the FourteenthInternational Conference on AutomatedDeduction, 272-275. Townsville, Australia: Lecture Notes in ComputerScience vol. 1249, Springer-Verlag.
41. J. Zhang, H. Zhang. CombiningLocal Search and Backtracking Techniquesfor Constraint Satisfaction. Proceedings of the Thirteenth NationalConferenceon Artificial Intelligence andEighthConferenceon Innovative Applications of Artificial Intelligence, 369-374.Portland, OR: AAAIPress / MITPress.