Skip to main content
SpringerLink
Log in

The Relation Between Complete and Incomplete Search

  • Chapter
Book cover Hybrid Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 114))

This chapter compares complete and incomplete search methods, discusses hybrid approaches, contrasts modelling techniques, and speculates that the boundary between the two is more blurred than it might seem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. G. Audemard, L. Simon. GUNSAT: A Greedy Local Search Algorithm for Unsatisfiability. Poster paper, Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, 2007.

    Google Scholar 

  2. R. Backofen, S. Will. Excluding Symmetries in Constraint-Based Search. Proceedings of the Fifth International Conference on Principles and Practice of Constraint Programming, Lecture Notes in Computer Science vol. 1713, Springer-Verlag 1999, pp. 73–87.

    Google Scholar 

  3. A. B. Baker. The Hazards of Fancy Backtracking. Proceedings of the Twelfth National Conference on Artificial Intelligence, vol. 1, AAAI Press, 1994, pp. 288–293.

    Google Scholar 

  4. L. Baptista, J. P. Marques-Silva. Using Randomization and Learning to Solve Hard Real-World Instances of Satisfiability. Proceedings of the Sixth International Conference on Principles and Practice of Constraint Programming, Lecture Notes in Computer Science vol. 1894, Springer-Verlag 2000, pp. 489–494.

    Google Scholar 

  5. D. Beasley, D. R. Bull, R. R. Martin. Reducing Epistasis in Combinatorial Problems by Expansive Coding. Fifth International Conference on Genetic Algorithms, 1993, pp. 400–407.

    Google Scholar 

  6. J. C. Beck. Solution-Guided, Multi-Point Constructive Search. Journal of Artificial Intelligence Research 29:49–77, 2007.

    MATH  Google Scholar 

  7. J. F. Benders. Partitioning Procedures for Solving Mixed Variables Programming Problems. Numerische Mathematik 4:238–252, 1962.

    Article  MATH  MathSciNet  Google Scholar 

  8. F. Boussemart, F. Hemery, C. Lecoutre, L. Saïs. Boosting Systematic Search by Weighting Constraints. Proceedings of the Sixteenth European Conference on Artificial Intelligence, IOS Press, 2004, pp. 146–150.

    Google Scholar 

  9. C. A. Brown, L. Finkelstein, P. W. Purdom Jr. Backtrack Searching in the Presence of Symmetry. T. Mora (ed.), Applied Algebra, Algebraic Algorithms and Error-Correcting Codes. Lecture Notes in Computer Science vol. 357, Springer-Verlag 1988, pp. 99–110.

    Google Scholar 

  10. D. Clark, J. Frank, I. Gent, E. MacIntyre, N. Tomov, T. Walsh. Local Search and the Number of Solutions. Proceedings of the Second International Conference on Principles and Practices of Constraint Programming, 1996, pp. 119–133.

    Google Scholar 

  11. 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.

    Google Scholar 

  12. M. Crawford, M. Ginsberg, E. Luks, A. Roy. Symmetry Breaking Predicates for Search Problems. Proceedings of the Fifth International Conference on Principles of Knowledge Representation and Reasoning, 1996, pp. 148–159.

    Google Scholar 

  13. B. De Backer, V. Furnon, P. Kilby, P. Prosser, P. Shaw. Local Search in Constraint Programming: Application to the Vehicle Routing Problem. Proceedings of the CP’97 Workshop on Industrial Constraint-Directed Scheduling, 1997.

    Google Scholar 

  14. T. Fahle, S. Schamberger, M. Sellman. Symmetry Breaking. Proceedings of the Seventh International Conference on Principles and Practices of Constraint Programming, Lecture Notes in Computer Science vol. 2239, Springer-Verlag, 2001, pp. 93–107.

    Google Scholar 

  15. H. Fang, W. Ruml. Complete Local Search for Propositional Satisfiability. Proceedings of the Nineteenth National Conference on Artificial Intelligence, AAAI Press, 2004, pp. 161–166.

    Google Scholar 

  16. F. Focacci, M. Milano. Global Cut Framework for Removing Symmetries. Proceedings of the Seventh International Conference on Principles and Practices of Constraint Programming, Lecture Notes in Computer Science vol. 2239, Springer-Verlag 2001, pp. 77–92.

    Google Scholar 

  17. 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 2027–2032, Morgan Kaufmann, 1995.

    Google Scholar 

  18. P. Galinier, J. K. Hao. Hybrid Evolutionary Algorithms for Graph Coloring. Journal of Combinatorial Optimization 3(4):379–397, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  19. I. P. Gent, H. H. Hoos, A. G. D. Rowley, K. Smyth. Using Stochastic Local Search to Solve Quantified Boolean Formulae. Proceedings of the Ninth International Conference on Principles and Practice of Constraint Programming, Lecture Notes in Computer Science vol. 2833, Springer-Verlag, 2003, pp. 348–362.

    Google Scholar 

  20. I. P. Gent, B. Smith. Symmetry Breaking During Search in Constraint Programming. Proceedings of the Fourteenth European Conference on Artificial Intelligence, 2000, pp. 599–603.

    Google Scholar 

  21. M. L. Ginsberg, Dynamic Backtracking, Journal of Artificial Intelligence Research 1:25–46, 1993.

    MATH  Google Scholar 

  22. M. L. Ginsberg, D. McAllester. GSAT and Dynamic Backtracking. Proceedings of the International Conference on Principles of Knowledge and Reasoning, 1994, pp. 226–237.

    Google Scholar 

  23. M. K. Goldberg, R. D. Rivenburgh. Constructing Cliques Using Restricted Backtracking. 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, pp. 89–102.

    Google Scholar 

  24. C. P. Gomes, B. Selman, H. Kautz. Boosting Combinatorial Search Through Randomization. Proceedings of the Fifteenth National Conference on Artificial Intelligence, AAAI Press, 1998, pp. 431–437.

    Google Scholar 

  25. Y. Hanatani, T. Horiyama, K. Iwama. Density Condensation of Boolean Formulas. Proceedings of the Sixth International Conference on the Theory and Applications of Satisfiability Testing, Santa Margherita Ligure, Italy, 2003, pp. 126–133.

    Google Scholar 

  26. W. D. Harvey. Nonsystematic Backtracking Search. PhD thesis, Stanford University, 1995.

    Google Scholar 

  27. W. D. Harvey, M. L. Ginsberg. Limited Discrepancy Search. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, 1995, pp. 607–615.

    Google Scholar 

  28. M. Henz. Constraint Programming – An Oz Perspective. Tutorial at the Fifth Pacific Rim International Conferences on Artificial Intelligence, 1998, NUS, Singapore, November 1998.

    Google Scholar 

  29. E. A. Hirsch, A. Kojevnikov. Solving Boolean Satisfiability Using Local Search Guided by Unit Clause Elimination. Proceedings of the Seventh International Conference on Principles and Practice of Constraint Programming, Lecture Notes in Computer Science vol. 2239, Springer-Verlag, 2001, pp. 605–609.

    Google Scholar 

  30. J. N. Hooker. A Search-Infer-and-Relax Framework for Integrating Solution Methods. Roman Bartk and Michela Milano, eds., Proceedings of the Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, Lecture Notes in Computer Science vol. 3524, Springer-Verlag, 2005, pp. 243–257.

    Google Scholar 

  31. H. H. Hoos, T. Stützle. Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco, CA, USA, 2004.

    Google Scholar 

  32. Y. Interian, G. Corvera, B. Selman, R. Williams. Finding Small Unsatisfiable Cores to Prove Unsatisfiability of QBFs. Proceedings of the Ninth International Symposium on AI and Mathematics, 2006.

    Google Scholar 

  33. A. K. Jonsson, M. L. Ginsberg. Experimenting with New Systematic and Nonsystematic Search Techniques. Proceedings of the AAAI Spring Symposium on AI and NP-Hard Problems, Stanford, California, 1993.

    Google Scholar 

  34. D. E. Joslin, D. P. Clements. Squeaky Wheel Optimization. Journal of Artificial Intelligence Research 10:353–373, 1999.

    MATH  MathSciNet  Google Scholar 

  35. D. Joslin, A. Roy. Exploiting Symmetry in Lifted CSPs. Proceedings of the Fourteenth National Conference on Artificial Intelligence, American Association for Artificial Intelligence 1997, pp. 197–203.

    Google Scholar 

  36. N. Jussien and O. Lhomme. Local Search With Constraint Propagation and Conflict-Based Heuristics. Artificial Intelligence 139(1):21–45, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  37. K. Kask, R. Dechter. GSAT and Local Consistency. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann 1995, pp. 616–622.

    Google Scholar 

  38. H. Kautz, D. McAllester, B. Selman. Exploiting Variable Dependency in Local Search. Poster Sessions, Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1997.

    Google Scholar 

  39. P. Langley. Systematic and Nonsystematic Search Strategies. Artificial Intelligence Planning Systems: Proceedings of the First International Conference, 1992.

    Google Scholar 

  40. F. Lardeux, F. Saubion, J.-K. Hao. GASAT: a genetic local search algorithm for the satisfiability problem. Evolutionary Computation 14(2):223–253, 2006.

    Article  Google Scholar 

  41. I. Lynce, J. P. Marques-Silva. Random Backtracking in Backtrack Search Algorithms for Satisfiability. Discrete Applied Mathematics 155(12):1604–1612, 2007.

    Article  MATH  MathSciNet  Google Scholar 

  42. P. Meseguer, C. Torras. Exploiting Symmetries Within Constraint Satisfaction Search. Artificial Intelligence 129(1–2):133–163, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  43. B. Meyer, A. Ernst, Integrating ACO and Constraint Propagation. Proceedings of the Fourth International Workshop on Ant Colony Optimization and Swarm Intelligence, Lecture Notes in Computer Science vol. 3172, Springer-Verlag, 2004, pp. 166–177.

    Google Scholar 

  44. Z. Michalewicz. A Survey of Constraint Handling Techniques in Evolutionary Computation Methods. Proceedings of the Fourth Annual Conference on Evolutionary Programming 1995, pp. 135–155.

    Google Scholar 

  45. M. Milano, A. Roli. On the Relation Between Complete and Incomplete Search: An Informal Discussion. Proceedings of the Fourth International Workshop on Integration of AI and OR techniques in Constraint Programming for Combinatorial Optimization Problems, le Croisic, France, 2002, pp. 237–250.

    Google Scholar 

  46. S. Minton, M. D. Johnston, A. B. Philips, P. Laird. Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problems. Artificial Intelligence 58(1–3):160–205, 1992.

    MathSciNet  Google Scholar 

  47. C. Morgenstern, Distributed Coloration Neighborhood Search. 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, pp. 335–357.

    Google Scholar 

  48. M. Pelikan and D. E. Goldberg. Genetic Algorithms, Clustering, and the Breaking of Symmetry. Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, 2000.

    Google Scholar 

  49. G. Pesant, M. Gendreau. A View of Local Search in Constraint Programming. Proceedings of the Second International Conference on Principles and Practice of Constraint Programming, Lecture Notes in Computer Science vol. 1118, Springer-Verlag, 1996, pp. 353–366.

    Google Scholar 

  50. D. G. Pothos, E. B. Richards. An Empirical Study of Min-Conflict Hill Climbing and Weak Commitment Search. CP’95 Workshop on Studying and Solving Really Hard Problems, 1995, pp. 140–146.

    Google Scholar 

  51. C. N. Potts, S. L. van de Velde. Dynasearch – Iterative Local Improvement by Dynamic Programming: Part I, the Travelling Salesman Problem. Technical report, University of Twente, The Netherlands, 1995.

    Google Scholar 

  52. S. D. Prestwich. A Hybrid Search Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences. Proceedings of the Sixth International Conference on Principles and Practice of Constraint Programming, Lecture Notes in Computer Science vol. 1894, Springer-Verlag, 2000, pp. 337–352.

    Google Scholar 

  53. S. D. Prestwich. Trading Completeness for Scalability: Hybrid Search for Cliques and Rulers. Proceedings of the Third International Workshop on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 2001, pp. 159–174.

    Google Scholar 

  54. S. D. Prestwich. Local Search and Backtracking vs Non-Systematic Backtracking. AAAI Fall Symposium on Using Uncertainty within Computation, Technical report FS-01-04, AAAI Press, 2001, pp. 109–115.

    Google Scholar 

  55. S. D. Prestwich. SAT Problems With Chains of Dependent Variables. Discrete Applied Mathematics 3037:1–22, Elsevier, 2002.

    Google Scholar 

  56. S. D. Prestwich. Negative Effects of Modeling Techniques on Search Performance. Annals of Operations Research 118:137–150, Kluwer Academic Publishers, 2003.

    Google Scholar 

  57. S. D. Prestwich. Exploiting Relaxation in Local Search. Proceedings of the First International Workshop on Local Search Techniques in Constraint Satisfaction, Toronto, Canada, 2004.

    Google Scholar 

  58. S. D. Prestwich. Increasing Solution Density by Dominated Relaxation. Proceedings of the Fourth International Workshop on Modelling and Reformulating Constraint Satisfaction Problems, 2005, pp. 1–13.

    Google Scholar 

  59. S. D. Prestwich, Ines Lynce. Local Search for Unsatisfiability. Proceedings of the Ninth International Conference on Theory and Applications of Satisfiability Testing, Lecture Notes in Computer Science vol. 4121, Springer-Verlag, 2006, pp. 283–296.

    Google Scholar 

  60. S. D. Prestwich, A. Roli. Symmetry Breaking and Local Search Spaces. Proceedings of the Second International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, Lecture Notes in Computer Science vol. 3524, Springer-Verlag, 2005, pp. 273–287.

    Google Scholar 

  61. A. Prügel-Bennett. Symmetry Breaking in Population-Based Optimization. IEEE Transactions on Evolutionary Computation 8(1):63–79, 2004.

    Article  Google Scholar 

  62. J.-F. Puget. On the Satisfiability of Symmetrical Constrained Satisfaction Problems. J. Komorowski, Z. W. Ras (eds.), Methodologies for Intelligent Systems, Proceedings of the International Symposium on Methodologies for Intelligent Systems, Lecture Notes in Computer Science vol. 689, Springer-Verlag 1993, pp. 350–361.

    Google Scholar 

  63. J.-C. Régin. Minimization of the Number of Breaks in Sports League Scheduling Problems Using Constraint Programming. DIMACS Series in Discrete Mathematics and Theoretical Computer Science vol. 57, 2001.

    Google Scholar 

  64. E. T. Richards, B. Richards. Non-Systematic Search and No-Good Learning. Journal of Automated Reasoning 24(4):483–533, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  65. 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.

    Google Scholar 

  66. M. Sellmann, C. Ansótegui. Disco-Novo-GoGo: Integrating Local Search and Complete Search with Restarts. Proceedings of the Twenty First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, AAAI Press, 2006.

    Google Scholar 

  67. B. Selman, H. Levesque, D. Mitchell. A New Method for Solving Hard Satisfiability Problems. Proceedings of the Tenth National Conference on Artificial Intelligence, MIT Press, 1992, pp. 440–446.

    Google Scholar 

  68. P. Shaw. Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. Proceedings of the Principles and Practice of Constraint Programming, Proceedings of the Fourth International Conference, Lecture Notes in Computer Science vol. 1520, Springer-Verlag, 1998, pp. 417–431.

    Google Scholar 

  69. 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.

    Google Scholar 

  70. W. Wei, B. Selman. Accelerating Random Walks. Proceedings of the Eighth International Conference on Principles and Practice of Constraint Programming, Lecture Notes in Computer Science vol. 2470, Springer-Verlag, 2002, pp. 216–232.

    Google Scholar 

  71. D. H. Wolpert, W. G. Macready. No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1(1):67–82, 1997.

    Article  Google Scholar 

  72. M. Yokoo. Weak-Commitment Search for Solving Constraint Satisfaction Problems. Proceedings of the Twelfth National Conference on Artificial Intelligence 313–318, AAAI Press, 1994.

    Google Scholar 

  73. N. Yugami, Y. Ohta and H. Hara, Improving repair-based constraint satisfaction methods by value propagation, Proceedings of the Twelfth National Conference on Artificial Intelligence vol. 1, AAAI Press, 1994, pp. 344–349.

    Google Scholar 

  74. E. Zarpas. Back to the SAT05 Competition: an a Posteriori Analysis of Solver Performance on Industrial Benchmarks. Journal on Satisfiability, Boolean Modeling and Computation 2:229–237, 2006, research note.

    MATH  Google Scholar 

  75. W. Zhang. Depth-First Branch-and-Bound versus Local Search: A Case Study. Proceedings of the Seventeenth National Conference on Artificial Intelligence, 2002, pp. 930–935.

    Google Scholar 

  76. H. Zhang, M. E. Stickel. Implementing the Davis-Putnam Method. Journal of Automated Reasoning 24(1–2):77–296, 2000.

    MathSciNet  Google Scholar 

  77. 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.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cork Constraint Computation Centre, University College, Cork, Ireland

    Steven Prestwich

Authors
  1. Steven Prestwich
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 112, E-08034, Barcelona, Spain

    Christian Blum

  2. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 213, E-08034, Barcelona, Spain

    Maria José Blesa Aguilera

  3. Università di Bologna, Via Venezia 52, I-47023, Cesena, Italy

    Andrea Roli

  4. Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, B-1050, Brussels, Belgium

    Michael Sampels

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Prestwich, S. (2008). The Relation Between Complete and Incomplete Search. In: Blum, C., Aguilera, M.J.B., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. Studies in Computational Intelligence, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78295-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78295-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78294-0

  • Online ISBN: 978-3-540-78295-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation