Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Applying tabu search to the job-shop scheduling problem
Annals of Operations Research - Special issue on Tabu search
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Bounded incremental computation
Bounded incremental computation
A constraint-based architecture for local search
OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Constraints
Yet Another Local Search Method for Constraint Solving
SAGA '01 Proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications
Specific Filtering Algorithms for Over-Constrained Problems
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Constraint and Integer Programming in OPL
INFORMS Journal on Computing
Scheduling social golfers locally
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Hi-index | 0.01 |
One of the most appealing features of constraint programming is its rich constraint language for expressing combinatorial optimization problems. This paper demonstrates that traditional combinators from constraint programming have natural counterparts for local search, although their underlying computational model is radically different. In particular, the paper shows that constraint combinators, such as logical and cardinality operators, reification, and first-class expressions can all be viewed as differentiable objects. These combinators naturally support elegant and efficient modelings, generic search procedures, and partial constraint satisfaction techniques for local search. Experimental results on a variety of applications demonstrate the expressiveness and the practicability of the combinators.