]. A. Dar01, . Darwichedm07-]-r, R. Dechter, . Mateescudp87-]-r, J. Dechter et al., Recursive conditioning AND/OR search spaces for graphical models The Cycle-cutset method for Improving Search Performance in AI Applications, Dec90] R. Dechter. Enhancement Schemes for Constraint Processing : Backjumping, Learning , and Cutset Decomposition Proceedings of the third IEEE on Artificial Intelligence Applications, pp.5-41273, 1987.

J. [. Dechter and . Pearl, Tree clustering for constraint networks, Artificial Intelligence, vol.38, issue.3, pp.353-366, 1989.
DOI : 10.1016/0004-3702(89)90037-4

M. [. Freuder and . Quinn, Taking Advantage of Stable Sets of Variables in Constraint Satisfaction Problems, Proceedings of the ninth International Joint Conference on Artificial IntelligenceFre78] E. Freuder. Synthesizing constraint expressions . CACM, pp.1076-1078958, 1978.

]. J. Gas79 and . Gaschnig, Performance Measurement and Analysis of Certain Search Algorithms, 1979.

N. [. Gottlob, F. Leone, and . Scarcello, A comparison of structural CSP decomposition methods, Artificial Intelligence, vol.124, issue.2, pp.343-282, 2000.
DOI : 10.1016/S0004-3702(00)00078-3

G. [. Haralick and . Elliott, Increasing tree search efficiency for constraint satisfaction problems, Artificial Intelligence, vol.14, issue.3, pp.263-31321, 1980.
DOI : 10.1016/0004-3702(80)90051-X

S. [. Jégou, C. Ndiaye, and . Terrioux, Strategies and Heuristics for Exploiting Treedecompositions of Constraint Networks, Inference methods based on graphical structures of knowledge (WIGSK'06), ECAI workshop, pp.13-18, 2006.

S. [. Jégou, C. Ndiaye, and . Terrioux, Dynamic heuristics for backtrack search on treedecomposition of csps, IJCAI, pp.112-117, 2007.

C. [. Jégou and . Terrioux, Hybrid backtracking bounded by tree-decomposition of constraint networks, Artificial Intelligence, vol.146, issue.1, pp.43-75, 2003.
DOI : 10.1016/S0004-3702(02)00400-9

E. [. Laarhoven and . Aarts, Simulated annealing : theory and applications, 1987.
DOI : 10.1007/978-94-015-7744-1

P. [. Mladenovic and . Hansen, Variable neighborhood search, Computers & Operations Research, vol.24, issue.11, pp.1097-1100, 1997.
DOI : 10.1016/S0305-0548(97)00031-2

URL : https://hal.archives-ouvertes.fr/hal-00979295

A. [. Minton, M. D. Philips, P. Johnston, and . Laird, Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems, Artificial Intelligence, vol.58, issue.1-3, pp.241-249, 1992.
DOI : 10.1016/0004-3702(92)90007-K

]. S. Pre02 and . Prestwich, Combining the scalability of local search with the pruning techniques of systematic search, Annals of Operations Research, vol.115, pp.51-72, 2002.

]. P. Pro95 and . Prosser, Forward checking with backmarking, Constraint Processing, pp.185-204, 1995.

G. [. Pralet and . Verfaillie, Travelling in the World of Local Searches in the Space of Partial Assignments, CPAIOR, pp.240-255, 2004.
DOI : 10.1007/978-3-540-24664-0_17

P. [. Robertson and . Seymour, Graph minors. II. Algorithmic aspects of tree-width, Journal of Algorithms, vol.7, issue.3, pp.309-322, 1986.
DOI : 10.1016/0196-6774(86)90023-4

[. Sabin and E. C. Freuder, Contradicting conventional wisdom in constraint satisfaction, PPCP, pp.10-20, 1994.
DOI : 10.1007/3-540-58601-6_86

H. [. Selman, B. Kautz, and . Cohen, Noise strategies for improving local search, Proceedings of the Twelfth National Conference on Artificial Intelligence, pp.337-343, 1994.

]. D. Wal75 and . Waltz, Understanding line drawings of scenes with shadows, The Psychology of Computer Vision, pp.19-91, 1975.