Chapter 5 Local search methods

Foundations of Artificial Intelligence

Volumn 2, Issue C, 2006, Pages 135-167

  Hoos, Holger H ^a   Tsang, Edward ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77956746708     PISSN: 15746526     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S1574-6526(06)80009-X     Document Type: Chapter

References (126)

1. Abdullah S., Ahmadi S., Burke E., and Dror M. Applying Ahuja-Orlin's large neighbourhood for constructing examination timetabling solution. 5th International Conference on the Practice and Theory of Automated Timetabling (PATAT) (2004) 413-419
2. Ahuja R., Ergun O., Orlin J., and Punnen P. A survey of very large scale neighborhood search techniques. Discrete Applied Mathematics 123 1-3 (2002) 75-102
3. Backer B.D., Furnon V., Kilby P., Prosser P., and Shaw P. Solving vehicle routing problems using constraint programming and meta heuristics. Journal of Heuristics 6 4 (2000) 501-525
4. Battiti R., and Tecchiolli G. The reactive tabu search. ORSA Journal on Computing 6 2 (1994) 126-140
5. LNCS 2919. Bessière C., Hebrard E., and Walsh T. Local consistencies in SAT. Theory and Applications of Satisfiability Testing, 6th International Conference (SAT 2003). Selected Revised Papers (2004), Springer Verlag 299-314
6. LNCS 1579. Biere A., Cimatti A., Clarke E., and Zhu Y. Symbolic model checking without BDDs. Tools and Algorithms for Construction and Analysis of Systems (TACAS '99) (1999), Springer Verlag 193-207
7. Bistarelli S., Montanari U., and Rossi F. Semiring-based constraint solving and optimization. Journal of the ACM 44 2 (1997) 201-236
8. Bouju A., Boyce J., Dimitropoulos C., vom Scheidt G., and Taylor J. Intelligent search for the radio link frequency assignment problem. International Conference on Digital Signal Processing. Cyprus (1995)
9. Boyan J., and Moore A. Learning evaluation functions to improve optimization by local search. Journal of Machine Learning Research 1 (2000) 77-112
10. Bradwell R., Ford P.M.J., Tsang E., and Williams R. An overview of the CACP project: modelling and solving constraint satisfaction/optimisation problems with minimal expert intervention. CP 2000 Workshop on Analysis and Visualization of Constraint Programs and Solvers (2000)
11. Burke E., and Newall J. A new adaptive heuristic framework for examination timetabling problems. Annals of Operations Research 129 (2004) 107-134
12. Cha B., and Iwama K. Performance test of local search algorithms using new types of random CNF formulas. 14th International Joint Conference on Artificial Intelligence (1995), Morgan Kaufmann Publishers, Berlin, Germany 304-310
13. Ithaca, New York, USA. Chiarandini M., and Stützle T. An application of iterated local search to the graph coloring problem. Computational Symposium on Graph Coloring and its Generalizations (2002) 112-125
14. Craenen B., Eiben A., and van Hemert J. Comparing evolutionary algorithms on binary constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 7 5 (2003) 424-445
15. Seattle, Washington, USA. Crawford J., and Baker A. Experimental results on the application of satisfiability algorithms to scheduling problems. 12th National Conference on Artificial Intelligence (AAAI-94) volume 2 (1994), AAAI Press/MIT Press, San Francisco, CA, USA. 0-262-51078-2 1092-1097
16. Culberson J., Gent I., and Hoos H. On the probabilistic approximate completeness of Walk SAT for 2-SAT. Technical Report APES-15a-2000 (2000), APES Research Group
17. September. Davenport A. A comparison of complete and incomplete algorithms in the easy and hard regions. Workshop on Studying and Sloving Really Hard Problems, 1st International Conference on Principles and Practice of Constraint Programming (1995) 43-51
18. Davenport A. Extensions and evaluation of GENET in constraint satisfaction. PhD thesis, Department of Computer Science, University of Essex, Colchester, UK (1997)
19. London, April. Davenport A., and Tsang E. Solving constraint satisfaction sequencing problems by iterative repair. 1st International Conference on the Practical Application of Constraint Technologies and Logic Programming (PACLP) (1999) 345-357
20. Davenport A., Tsang E., Wang C., and Zhu K. GENET: a connectionist architecture for solving constraint satisfaction problems by iterative improvement. 12th National Conference for Artificial Intelligence (1994) 325-330
21. LNCS 3709. Dilkina B., Duan L., and Havens W. Extending systematic local search for job shop scheduling problems. 11th International Conference on Principles and Practice of Constraint Programming (CP 2005) (2005), Springer-Verlag 762-766
22. Dorigo M., and Stützle T. Ant Colony Optimization (2004), MIT Press
23. Doyle J. A truth maintenance system. Artificial Intelligence 12 (1979) 231-272
24. Ernst M.D., Millstein T.D., and Weld D.S. Automatic SAT-compilation of planning problems. 15th International Joint Conference on Artificial Intelligence (1997), Morgan Kaufmann Publishers, Cambridge, MA, USA 1169-1177
25. Fink A., and Voß S. Hotframe: A heuristic optimization framework. Optimization Software Class Libraries (2002), Kluwer, San Francisco, CA, USA 81-154
26. Frank J. Weighting for Godot: Learning heuristics for GSAT. 13th National Conference on Artificial Intelligence (1996), AAAI Press / The MIT Press 776-783
27. Frank J. Learning short-term clause weights for GSAT. 15th International Joint Conference on Artificial Intelligence (1997), Morgan Kaufmann Publishers, Menlo Park, CA, USA 384-389
28. Freuder E., and Wallace R. Partial constraint satisfaction. Artificial Intelligence, Special Volume on Constraint Based Reasoning 58 1-3 (1992) 21-70
29. LNCS 1330. Galinier P., and Hao J.-K. Tabu search for maximal constraint satisfaction problems. Principles and Practice of Constraint Programming - CP 1997 (1997), Springer Verlag, San Francisco, CA, USA 196-208
30. Galinier P., and Hao J.K. Hybrid evolutionary algorithms for graph coloring. Journal of Combinatorial Optimization 3 4 (1999) 379-397
31. Gaspero L.D., and Schaerf A. Easylocal++: an object-oriented framework for the flexible design of local-search algorithms. Software Practice and Experience 33 8 (2003) 733-765
32. APES-09-1999. Gent I., and Walsh T. CSPLib: a benchmark library for constraints. Technical report (1999)
33. Gent I.P., and Walsh T. Towards an understanding of hill-climbing procedures for SAT. 10th National Conference on Artificial Intelligence (1993), AAAI Press / The MIT Press, Berlin, Germany 28-33
34. Ginsberg M. Dynamic backtracking. Journal of Artificial Intelligence Research 1 (1993) 25-46
35. Glover F. Tabu search and adaptive memory programming - advances, applications and challenges. Interfaces in Computer Science and Operations Research (1996), Kluwer Academic Publishers, Menlo Park, CA, USA
36. Glover F., and Laguna M. Tabu Search (1997), Kluwer Academic Publishers
37. Gu J., and Puri R. Asynchronous Circuit Synthesis with Boolean Satisfiability. IEEE Transactions of Computer-Aided Design of Integrated Circuits and Systems 14 8 (1995) 961-973
38. Gu J., Purdom P., Franco J., and Wah B. Algorithms for the satisfiability (SAT) problem: A survey. Satisfiability problem: Theory and Applications, volume 35 of DIMACS Series on Discrete Mathematics and Theoretical Computer Science (1997), American Mathematical Society, Boston, MA, USA 19-151
39. Amsterdam. Hao J.-K., and Dorne R. A new population-based method for satisfiability problems. 11th European Conference on Artificial Intelligence (1994) 135-139
40. LNCS 3060. Havens W., and Dilkina B. A hybrid schema for systematic local search. Advances in Artificial Intelligence: 17th Conference of the Canadian Society for Computational Studies of Intelligence (2004), Springer Verlag, John Wiley & Sons 248-260
41. Hentenryck P.V. The OPL Optimization Programming Language (1999), MIT Press
42. Hentenryck P.V., and Michel L. Constraint-Based Local Search (2005), MIT Press, Cambridge, MA, USA
43. Holstein D., and Moscato P. Memetic algorithms using guided local search, a case study. New ideas in optimization (1999), McGraw Hill, Cambridge, MA, USA 235-243
44. Hoos H. Stochastic Local Search - Methods, Models, Applications. PhD thesis, TU Darmstadt, FB Informatik, Darmstadt, Germany (1998)
45. Hoos H. An adaptive noise mechanism for WalkSAT. 18th National Conference on Artificial Intelligence (2002), AAAI Press/The MIT Press 655-660
46. Hoos H. A mixture-model for the behaviour of SLS algorithms for SAT. 18th National Conference on Artificial Intelligence (2002), AAAI Press/The MIT Press, Menlo Park, CA, USA 661-667
47. Hoos H. On the run-time behaviour of stochastic local search algorithms for SAT. 16th National Conference on Artificial Intelligence (1999), AAAI Press/The MIT Press, Menlo Park, CA, USA 661-666
48. Hoos H. SAT-encodings, search space structure, and local search performance. 16th International Joint Conference on Artificial Intelligence (1999), Morgan Kaufmann Publishers, Menlo Park, CA, USA 296-302
49. Hoos H., and Stützle T. Local search algorithms for SAT: An empirical evaluation. Journal of Automated Reasoning 24 4 (2000) 421-481
50. Hoos H., and Stützle T. SATLIB: An Online Resource for Research on SAT. SAT 2000 volume 63 (2000), IOS Press, San Francisco, CA, USA 283-292
51. Hoos H., and Stützle T. Characterising the behaviour of stochastic local search. Artificial Intelligence 112 1-2 (1999) 213-232
52. Hoos H., and Stützle T. Stochastic Local Search: Foundations and Applications (2004), Elsevier / Morgan Kaufmann, Amsterdam, The Netherlands
53. LNCS 2470. Hutter F., Tompkins D.A.D., and Hoos H.H. Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. Principles and Practice of Constraint Programming - CP 2002 (2002), Springer Verlag 233-248
54. Edinburgh, September. Jin N. Equilibrium selection by co-evolution for bargaining problems under incomplete information about time preferences. Congress on Evolutionary Computation (CEC 2005) (2005) 2661-2668
55. Johnson D.S., Aragon C.R., McGeoch L.A., and Schevon C. Optimization by simulated annealing: An experimental evaluation: Part II, graph coloring and number partitioning. Operations Research 39 3 (1991) 378-406
56. Kask K., and Dechter R. GSAT and local consistency. 14th International Joint Conference on Artificial Intelligence (1995), Morgan Kaufmann Publishers, Berlin, Germany 616-623
57. Kask K., and Dechter R. A graph-based method for improving GSAT. 13th National Conference on Artificial Intelligence (1996), AAAI Press / The MIT Press, San Francisco, CA, USA 350-355
58. Kautz H., and Selman B. Pushing the envelope: Planning, propositional logic, and stochastic search. 13th National Conference on Artificial Intelligence volume 2 (1996), AAAI Press / The MIT Press, Menlo Park, CA, USA 1194-1201
59. Kern M. Parameter Adaptation in heuristic search - a population-based approach. PhD thesis, Department of Computer Science, University of Essex, Colchester, UK (2005)
60. Kilby P., Prosser P., and Shaw P. A comparison of traditional and constraint-based heuristic methods on vehicle routing problems with side constraints. Constraints 5 4 (2000) 389-414
61. Koopman B. The theory of search, part iii, the optimum distribution of searching effort. Operations Research 5 (1957) 613-626
62. LNCS 1520. Laburthe F., and Caseau Y. SALSA: A language for search algorithms. 4th International Conference on Principles and Practice of Constraint Programming (1998), Springer Verlag, Menlo Park, CA, USA 310-324
63. Lau H.C. A new approach for weighted constraint satisfaction. Constraints 7 2 (2002) 151-165
64. Lau T. Guilded Genetic Algorithm. PhD thesis, Department of Computer Science, University of Essex, Colchester, UK (1999)
65. December. Lau T., and Tsang E. Solving the processor configuration problem with a mutationbased genetic algorithm. International Journal on Artificial Intelligence Tools (IJAIT) 6 4 (1997) 567-585
66. Lau T., and Tsang E. Guided genetic algorithm and its application to radio link frequency assignment problems. Constraints 6 4 (2001) 373-398
67. Lee J., Leung H., and Won H. Extending GENET for non-binary CSPs. 17th International Conference on Tools with Artificial Intelligence (1995) 338-342
68. Cambridge, Massachusetts, USA, August. Lee J., Leung H., and Won H. Towards a more efficient stochastic constraint solver. 2nd International Conference on Principles and Practice of Constraint Programming (1996) 338-352
69. LNCS 3569. Li C.M., and Huang W. Diversification and determinism in local search for satisfiability. 8th International Conference on Theory and Applications of Satisfiability Testing (SAT 2005) (2005), Springer Verlag 158-172
70. Li J. FGP: A genetic programming based tool for financial forecasting. PhD thesis, Department of Computer Science, University of Essex, Colchester, UK (2001)
71. In: Luenberger D. (Ed). Linear and nonlinear programming (1984), Addison-Wesley Publishing Co., Inc.
72. Mazure B., Sais L., and Gregoire E. TWSAT: A new local search algorithm for SAT - performance and analysis. 14th National Conference on Artificial Intelligence (1997), AAAI Press / The MIT Press 281-285
73. McAllester D., Selman B., and Kautz H. Evidence for invariants in local search. 14th National Conference on Artificial Intelligence (1997), AAAI Press / The MIT Press, Menlo Park, CA, USA 321-326
74. Mills P. Extensions to guided local search. PhD thesis, Department of Computer Science, University of Essex Colchester, UK (2002)
75. Minton S. Automatically configuring constraint satisfaction programs, a case study. Constraints 1 1&2 (1996) 7-43
76. Minton S., Johnston M.D., Philips A.B., and Laird P. Solving large-scale constraint satisfaction and scheduling problems using a heuristic repair method. 8th National Conference on Artificial Intelligence (1990), AAAI Press / The MIT Press, Menlo Park, CA, USA 17-24
77. Minton S., Johnston M., Philips A., and Laird P. Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence 58 1-3 (1992) 161-205
78. Morris P. The breakout method for escaping from local minima. National Conference on Artificial Intelligence (1993) 40-45
79. Neto D. Efficient Cluster Compensation for Lin-Kernighan Heuristics. PhD thesis, University of Toronto, Department of Computer Science, Toronto, canada (1999)
80. Papadimitriou C.H. On selecting a satisfying truth assignment. 32nd Annual IEEE Symposium on Foundations of Computer Science (1991), IEEE Computer Society Press, Menlo Park, CA, USA 163-169
81. LNCS 2279. Paquete L., and Stützle T. An experimental investigation of iterated local search for coloring graphs. Applications of Evolutionary Computing (2002), Springer Verlag, Los Alamitos, CA, USA 122-131
82. Paredis J. Genetic state-space search for constrained optimization problems. 13th International Joint Conference on Artificial Intelligence (1993) 967-972
83. Patterson D.J., and Kautz H. Auto-walksat: A self-tuning implementation of walksat. LICS 2001 Workshop on Theory and Applications of Satisfiability Testing (SAT 2001) (2001), Elsevier, Berlin, Germany
84. Prestwich S. Local search on SAT-encoded CSPs. 6th International Conference on Theory and Applications of Satisfiability Testing (SAT 2003) (2003) 388-399
85. Prestwich S. Stochastic local search in constrained spaces. Practical Applications of Constraint Technology and Logic Programming (PACLP '00) (2000) 27-39
86. Richards T., Jiang Y., and Richards B. Ng-backmarking - an algorithm for constraint satisfaction. British Telecom Technology Journal 13 1 (1995) 102-109
87. August. Riff Rojas M. From quasi-solutions to solution: an evolutionary algorithm to solve CSPs. 2nd International Conference on Principles and Practice of Constraint Programming (1996) 367-381
88. Schiex T., Fargier H., and Verfaillie G. Valued constraint satisfaction problems: Hard and easy problems. 14th International Joint Conference on Artificial Intelligence (1995), Morgan Kaufmann Publishers, Amsterdam, The Netherlands 631-639
89. Schuurmans D., Southey F., and Holte R.C. The exponentiated subgradient algorithm for heuristic Boolean programming. 17th International Joint Conference on Artificial Intelligence (2001), Morgan Kaufmann Publishers, San Francisco, CA, USA 334-341
90. Selman B., and Kautz H. Domain-independent extensions to GSAT: Solving large structured satisfiability problems. 13th International Joint Conference on Artificial Intelligence (1993), Morgan Kaufmann Publishers, San Francisco, CA, USA 290-295
91. Selman B., Levesque H., and Mitchell D. A new method for solving hard satisfiability problems. 10th National Conference on Artificial Intelligence (1992), AAAI Press / The MIT Press, San Francisco, CA, USA 440-446
92. Selman B., Kautz H., and Cohen B. Noise strategies for improving local search. 12th National Conference on Artificial Intelligence (1994), AAAI Press / The MIT Press, Menlo Park, CA, USA 337-343
93. Shang Y., and Wah B.W. A discrete Lagrangian-based global-search method for solving satisfiability problems. Journal of Global Optimization 12 1 (1998) 61-99
94. LNCS 2671. Smyth K., Hoos H.H., and Stützle T. Iterated robust tabu search for MAX-SAT. Advances in Artificial Intelligence, 16th Conference of the Canadian Society for Computational Studies of Intelligence (2003), Springer Verlag, Menlo Park, CA, USA 129-144
95. Berlin, Germany, August. Solnon C. Solving permutation constraint satisfaction problems with artificial ants. 14th European Conference on Artificial Intelligence (2000) 118-122
96. Solnon C. Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 6 4 (2001) 347-357
97. Spears W.M. Simulated annealing for hard satisfiability problems. Technical report, Naval Research Laboratory, Washington D.C., USA (1993)
98. LNAI 1303. Steinmann O., Strohmaier A., and Stützle T. Tabu search vs. random walk. KI-97: Advances in Artificial Intelligence (1997), Springer Verlag, Berlin, Germany 337-348
99. Stone L.D. The process of search planning: current approaches and continuing problems. Operations Research 31 (1983) 207-233
100. Stuckey P., and Tam V. Improving evolutionary algorithms for efficient constraint satifaction. International Journal on Artificial Intelligence Tools (IJAIT), World Scientific 8 4 (1999) 363-383
101. Stützle T. Local Search Algorithms for Combinatorial Problems - Analysis, Improvements, and New Applications. PhD thesis, TU Darmstadt, FB Informatik, Darmstadt, Germany (1998)
102. Thornton J., Pham D., Bain S., and Ferreira V. Additive versus multiplicative clause weighting for SAT. 19th National Conference on Artificial Intelligence (2004), AAAI Press / The MIT Press, Berlin, Germany 191-196
103. Tompkins D., and Hoos H. Warped landscapes and random acts of SAT solving. 8th International Symposium on Artificial Intelligence and Mathematics (2004)
104. Tsang E. Foundations of constraint satisfaction (1993), Academic Press, Menlo Park, CA, USA
105. Tsang E., and Li J. EDDIE for financial forecasting. Genetic Algorithms and Programming in Computational Finance (2002), Kluwer Academic Publishers, London and San Diego 161-174
106. Tsang E., and Voudouris C. Fast local search and guided local search and their application to British Telecom's workforce scheduling problem. Operations Research Letters 20 3 (1997) 119-127
107. Tsang E., and Wang C. A generic neural network approach for constraint satisfaction problems. Neural network applications (1992), Springer-Verlag 12-22
108. Tsang E., and Warwick T. Applying genetic algorithms to constraint satisfaction problems. 9th European Conference on AI (1990) 649-654
109. (to appear). Tsang E., Ford J., Mills P., Bradwell R., Williams R., and Scott P. Towards a practical engineering tool for rostering. Annals of Operational Research, Special Issue on Personnel Scheduling and Planning (2006)
110. LNCS 3004. van Hemert J., and Solnon C. A study into ant colony optimization, evolutionary computation and constraint programming on binary constraint satisfaction problems. Evolutionary Computation in Combinatorial Optimization (EvoCOP 2004) (2004), Springer Verlag 114-123
111. London, April. Voudouris C., and Tsang E. Partial constraint satisfaction problems and guided local search. Practical Application of Constraint Technology (PACT'96) (1996) 337-356
112. Voudouris C., and Tsang E. Solving the radio link frequency assignment problem using guided local search. 13th NATO symposium on Frequency Assignment, Sharing and Conservation Systems (Aerospace), Research and Technology Organization (RTO) (1999), North Atlantic Treaty Organization (NATO), Berlin, Germany
113. Voudouris C., and Tsang E. Guided local search, chapter 7. Handbook of metaheuristics (2003), Kluwer
114. Voudouris C., Dorne R., Lesaint D., and Liret A. iopt: A software toolkit for heuristic search methods. 7th International Conference on Principles and Practice of Constraint Programming (2001), Springer Verlag 716-729
115. Wah B., and Shang Y. Discrete Lagrangian-based search for solving MAX-SAT problems. 15th International Joint Conference on Artificial Intelligence (1997) 378-383
116. LNCS 1106. Wallace R.J., and Freuder E.C. Heuristic methods for over-constrained constraint satisfaction problems. Over-Constrained Systems (1995), Springer Verlag 207-216
117. Walser J.P. Solving linear pseudo-Boolean constraint problems with local search. 14th National Conference on Artificial Intelligence (1997), AAAI Press/The MIT Press, Berlin, Germany 269-274
118. LNCS 1637. Walser J.P. Integer Optimization by Local Search: A Domain-Independent Approach (1999), Springer Verlag, Menlo Park, CA, USA
119. Wang C., and Tsang E. Solving constraint satisfaction problems using neuralnetworks. IEE 2nd International Conference on Artificial Neural Networks (1991) 295-299
120. Warwick T., and Tsang E. Using a genetic algorithm to tackle the processors configuration problem. ACM Symposium on Applied Computing (SAC) (1994) 217-221
121. Wu Z., and Wah B.W. An efficient global-search strategy in discrete Lagrangian methods for solving hard satisfiability problems. 17th National Conference on Artificial Intelligence (2000), AAAI Press / The MIT Press, Berlin, Germany 310-315
122. Wu Z., and Wah B.W. Trap escaping strategies in discrete Lagrangian methods for solving hard satisfiability and maximum satisfiability problems. 16th National Conference on Artificial Intelligence (1999), AAAI Press / The MIT Press, Menlo Park, CA, USA 673-678
123. Yagiura M., and Ibaraki T. Efficient 2 and 3-flip neighborhood search algorithms for the MAX SAT: Experimental evaluation. Journal of Heuristics 7 5 (2001) 423-442
124. Yao X. Dynamic neighbourhood size in simulated annealing. International Joint Conference on Neural Networks (IJCNN'92) volume 1 (1992), IEEE Press, Menlo Park, CA, USA 411-416
125. Yu X., Zheng W., Wu B., and Yao X. A novel penalty function approach to constrained optimization problems with genetic algorithms. Journal of Advanced Computational Intelligence 2 6 (1998) 208-213
126. Zhang H. Generating college conference basketball schedules by a SAT solver. 5th International Symposium on the Theory and Applications of Satisfiability Testing (SAT 2002) (2002) 281-291