A hybrid search architecture applied to hard random 3-SAT and low-autocorrelation binary sequences

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

Volumn 1894, Issue , 2000, Pages 337-352

  Prestwich, Steven ^a  

^a UNIVERSITY COLLEGE CORK   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY SEQUENCES; COMPUTER PROGRAMMING; CONSTRAINT THEORY; LEARNING ALGORITHMS; LOCAL SEARCH (OPTIMIZATION); STOCHASTIC SYSTEMS;

COMBINATORIAL PROBLEM; COMMUNICATIONS ENGINEERING; CONSTRAINT PROGRAMMING; CONSTRAINT PROPAGATION; CONSTRAINT SATISFACTION; OPTIMISATION PROBLEMS; STOCHASTIC LOCAL SEARCHES; STOCHASTIC SEARCH ALGORITHMS;

CONSTRAINT SATISFACTION PROBLEMS;

EID: 84945891959     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45349-0_25     Document Type: Conference Paper

References (41)

1. R. J. Bayardo Jr, R. Schrag. Using CSP Look-Back Techniques to Solve Real World SAT Instances. Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference, AAAI Press / The MIT Press 1997, pp. 203-208.
2. G. Beenker, T. Claasen, P. Hermens. Binary Sequences With a Maximally Flat Amplitude Spectrum. Philips J. Res. vol. 40, pp. 289-304.
3. J. Bernasconi. Low Autocorrelation Binary Sequences: Statistical Mechanics and Configuration Space Analysis. J. Physique, vol. 48, pp. 559, 1987.
4. J. M. Crawford. Solving Satisfiability Problems Using a Combination of Systematic and Local Search. Second DIMACS Challenge: Cliques, Coloring, and Satisfiability, October 1993, Rutgers University, NJ, USA. Available on: http://www.cirl.uoregon.edu/crawford/papers/papers.html
5. J. M. Crawford, L. D. Auton. Experimental Results on the Crossover Point in Random 3SAT. Artificial Intelligence vol. 81, nos. 1-2, March 1996, pp. 31-57.
6. C. de Groot, D. Würtz, K. H. Hoffmann. Low Autocorrelation Binary Sequences: Exact Enumeration and Optimization by Evolutionary Strategies. Optimization vol. 23, Gordon & Breach Science Publishers S. A. 1992, pp. 369-384.
7. E. C. Freuder, R. Dechter, M. L. Ginsberg, B. Selman, E. Tsang. Systematic Versus Stochastic Constraint Satisfaction. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann 1995, pp. 2027-2032.
8. I. Gent, T. Walsh. CSPLIB: A Benchmark Library for Constraints. Available on: http://csplib.cs.strath.ac.uk
9. I. P. Gent, T. Walsh. Towards an Understanding of Hill-Climbing Procedures for SAT. Proceedings of the Eleventh National Conference on Artificial Intelligence, AAAI Press / MIT Press 1993, pp. 28-33.
10. I. Gent, B. Smith. Symmetry Breaking During Search in Constraint Programming. Research Report 99.02, Department of Computer Science, University of Leeds, 1999.
11. M. L. Ginsberg. Dynamic Backtracking. Journal of Artificial Intelligence Research vol. 1, AI Access Foundation and Morgan Kaufmann 1993, pp. 25-46.
12. M. L. Ginsberg, D. A. McAllester. GSAT and Dynamic Backtracking. Proceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning, Morgan Kaufmann 1994, pp. 226-237.
13. M. Golay. The Merit Factor of Long Low Autocorrelation Binary Sequences. IEEE Transactions on Information Theory IT-23, pp. 43-51.
14. M. K. Goldberg, R. D. Rivenburgh. Constructing Cliques Using Restricted Backtracking. In [18] pp. 89-102.
15. C. Gomes, B. Selman, H. Kautz. Boosting Combinatorial Search Through Randomization. Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI Press / The MIT Press 1998, pp. 431-437.
16. W. D. Harvey. Nonsystematic Backtracking Search. PhD thesis, Stanford University, 1995.
17. H. Hoos. SAT-Encodings, Search Space Structure, and Local Search Performance. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI’99), Morgan Kaufmann 1999, pp. 296-302.
18. D. S. Johnson, M. A. Trick (Eds). Cliques, Coloring and Satisfiability: Second DIMACS Implementation Challenge, DIMACS Series in Discrete Mathematics and Theoretical Computer Science vol. 26, American Mathematical Society 1996.
19. D. E. Joslin, D. P. Clements. Squeaky Wheel Optimization. Journal of Artificial Intelligence Research vol. 10, 1999, pp. 353-373.
20. N. Jussien, O. Lhomme. The Path-Repair Algorithm. Proceedings of the Workshop on Large Scale Combinatorial Optimization and Constraints, Electronic Notes in Discrete Mathematics vol. 4, 1999. Available on: http://www.elsevier.nl/locate/disc
21. H. Kautz, D. McAllester, B. Selman. Exploiting Variable Dependency in Local Search. Poster Sessions of the Fifteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann 1997.
22. P. Langley. Systematic and Nonsystematic Search Strategies. Artificial Intelligence Planning Systems: Proceedings of the First International Conference, Morgan Kaufmann 1992, pp. 145-152.
23. S. Mertens. Exhaustive Search for Low-Autocorrelation Binary Sequences. J. Phys. A: Math. Gen. vol. 29 L473-L481, 1996.
24. S. Mertens, C. Bessenrodt. On the Ground States of the Bernasconi Model. J. Phys. A: Math. Gen. vol. 31, 1998, pp. 3731-3749.
25. P. Morris. The Breakout Method for Escaping Local Minima. Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI’93), AAAI Press /MIT Press 1993, pp. 40-45.
26. A. J. Parkes, J. P. Walser. Tuning Local Search for Satisfiability Testing. Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, (AAAI’96, IAAI’96), vol. 1, AAAI Press / The MIT Press 1996, pp. 356-362.
27. G. Pesant, M. Gendreau. A View of Local Search in Constraint Programming. Principles and Practice of Constraint Programming, Proceedings of the Second International Conference, Lecture Notes in Computer Science vol. 1118, Springer-Verlag 1996, pp. 353-366.
28. S. D. Prestwich. A Hybrid Local Search Algorithm for Low-Autocorrelation Binary Sequences. Technical Report TR-00-01, Department of Computer Science, University College, Cork, Ireland 2000. Available on: http://csplib.cs.strath.ac.uk
29. S. D. Prestwich. Using an Incomplete Version of Dynamic Backtracking for Graph Colouring. Workshop on Large Scale Combinatorial Optimization, Electronic Notes in Discrete Mathematics vol. 1, 1998. Available on: http://www.elsevier.nl/locate/disc
30. S. D. Prestwich. Stochastic Local Search In Constrained Spaces. Proceedings of Practical Applications of Constraint Technology and Logic Programming, PACLP-2000, Practical Application Company Ltd 2000, pp. 27-39.
31. S. D. Prestwich. A Generic Approach to Combining Stochastic Algorithms With Systematic Constraint Solvers. Proceedings of Frontiers of Combining Systems 2 (FroCos’98), D. M. Gabbay and M. de Rijke (eds.), Research Studies Press/Wiley 1999, pp. 275-294.
32. E. T. Richards, B. Richards. Non-Systematic Search and Learning: an Empirical Study. Principles and Practice of Constraint Programming, Proceedings of the Fourth International Conference, Lecture Notes in Computer Science vol. 1520, Springer-Verlag 1998, pp. 370-384.
33. A. Schaerf. Combining Local Search and Look-Ahead for Scheduling and Constraint Satisfaction Problems. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann 1997, pp. 1254-1259.
34. B. Selman, H. Kautz, B. Cohen. Noise Strategies for Improving Local Search. Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI Press 1994, pp. 337-343.
35. Y. Shang, B. W. Wah. A Discrete Lagrangian-Based Global-Search Method for Solving Satisfiability Problems. Journal of Global Optimization vol. 10, Kluwer 1997, pp. 1-40.
36. P. Shaw. UsingConstraint Programming and Local Search to Solve Vehicle Routing Problems. Proceedings of the Fourth International Conference on Principles and Practice of Constraint Programming (CP’98), Lecture Notes in Computer Scienece vol. 1520, Springer-Verlag 1998, pp. 417-431.
37. G. Verfaillie, T. Schiex. Solution Reuse in Dynamic Constraint Satisfaction Problems. Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI Press 1994, pp. 307-312.
38. Q. Wang. Optimization by Simulating Molecular Evolution. Biol. Cybern. vol. 57, pp. 95-101, 1987.
39. M. Yokoo. Weak-Commitment Search for Solving Constraint Satisfaction Problems. Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI Press 1994, pp. 313-318.
40. M. Yokoo. Why Adding More Constraints Makes a Problem Easier for Hill-Climbing Algorithms: Analyzing Landscape of CSPs. Proceedings of the Third International Conference on Principles and Practice of Constraint Programming (CP’97), Lecture Notes in Computer Science vol. 1330, Springer-Verlag 1997, pp. 357-370.
41. J. Zhang, H. Zhang. Combining Local Search and Backtracking Techniques for Constraint Satisfaction. Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Conference on Innovative Applications of Artificial Intelligence, AAAI Press / The MIT Press 1996, pp. 369-374.