Exploiting problem decomposition in multi-objective constraint optimization

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

Volumn 5732 LNCS, Issue , 2009, Pages 592-607

  Marinescu, Radu ^a  

^a UNIVERSITY COLLEGE CORK   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

BRANCH AND BOUNDS; CONSTRAINT OPTIMIZATIONS; MEASURES OF PERFORMANCE; MULTI OBJECTIVE; PROBLEM DECOMPOSITION; PROBLEM STRUCTURE; SEARCH ALGORITHMS; SEARCH TREES; STATIC AND DYNAMIC;

COMPUTER PROGRAMMING; CONSTRAINT THEORY; LEARNING ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION;

CONSTRAINED OPTIMIZATION;

EID: 70350423550     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04244-7_47     Document Type: Conference Paper

References (17)

1. Dechter, R., Mateescu, R.: AND/OR search spaces for graphical models. Artificial Intelligence 171(2-3), 73-106 (2007)
2. Marinescu, R., Dechter, R.: AND/OR branch-and-bound for graphical models. In: International Joint Conference on Artificial intelligence (IJCAI), pp. 224-229 (2005)
3. Rollon, E., Larrosa, J.: Bucket elimination for multi-objective optimization problems. Journal of Heuristics 12, 307-328 (2006)
4. Freuder, E.C., Quinn, M.J.: Taking advantage of stable sets of variables in constraint satisfaction problems. In: IJCAI, pp. 1076-1078 (1985)
5. Bayardo, R., Miranker, D.: A complexity analysis of space-bound learning algorithms for the constraint satisfaction problem. In: AAAI, pp. 298-304 (1996)
6. Rollon, E., Larrosa, J.: Multi-objective Russian doll search. In: National Conference on Artificial Intelligence (AAAI), pp. 249-254 (2007)
7. Rollon, E., Larrosa, J.: Constraint optimization techniques for multiobjective branch and bound search. In: International Conference on Logic Programming, ICLP (2008)
8. Ehrgott, M., Gandibleux, X.: Bounds and bound sets for biobjective combinatorial optimization problems. Notes in Economics and Mathematical Systems 507, 241-253 (2001)
9. Nilsson, N.J.: Principles of Artificial Intelligence. Tioga, Palo Alto (1980)
10. Dechter, R., Rish, I.: Mini-buckets: A general scheme of approximating inference. Journal of ACM 50(2), 107-153 (2003)
11. Dechter, R.: Bucket elimination: A unifying framework for reasoning. Artificial Intelligence 113, 41-85 (1999)
12. Kask, K., Dechter, R.: A general scheme for automatic generation of search heuristics from specification dependencies. Artificial Intelligence 129(1-2), 91-131 (2001)
13. Verfaillie, G., Lematre, M., Schiex, T.: Russian doll search for solving constraint optimization problems. In: AAAI, pp. 181-187 (1996)
14. Ehrgott, M., Gandibleux, X.: Multiple criteria optimization. State of the art. Annotated bibliographic surveys. Kluwer Academic Publishers, Dordrecht (2002)
15. Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. In: ACM Electronic Commerce, pp. 66-76 (2000)
16. Holland, A.: Risk Management in Combinatorial Auctions. PhD thesis, University College Cork (2005)
17. Bistarelli, S., Montanari, U., Rossi, F.: Semiring based constraint solving and optimization. Journal of ACM 44(2), 309-315 (1997)