The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Constraint Processing
Ant Colony Optimization
Backjump-Based Techniques versus Conflict-Directed Heuristics
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Artificial Intelligence
Experimental evaluation of preprocessing techniques in constraint satisfaction problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
The breakout method for escaping from local minima
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Weighting for godot: learning heuristics for GSAT
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Combining local search and backtracking techniques for constraint satisfaction
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
Challenging heuristics: evolving binary constraint satisfaction problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Managing dynamic CSPs with preferences
Applied Intelligence
A new crossover for solving constraint satisfaction problems
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Specifying and solving symbolic and numeric temporal constraints
International Journal of Knowledge-based and Intelligent Engineering Systems
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A Constraint Satisfaction Problem (CSP) is a powerful framework for representing and solving constraint problems. When solving a CSP using a backtrack search method, one important factor that reduces the size of the search space drastically is the order in which variables and values are examined. Many heuristics for static and dynamic variable ordering have been proposed and the most popular and powerful are those that gather information about the failures during the constraint propagation phase, in the form of constraint weights. These later heuristics are called conflict driven heuristics. In this paper, we propose two of these heuristics respectively based on Hill Climbing (HC) and Ant Colony Optimization (ACO) for weighing constraints. In addition, we propose two new value ordering techniques, respectively based on HC and ACO, that rank the values based on their ability to satisfy the constraints attached to their corresponding variables. Several experiments were conducted on various types of problems including random, quasi random and patterned problems. The results show that the proposed variable ordering heuristics, are successful especially in the case of hard random problems. Also, when using the proposed value and variable ordering together, we can improve the performance particularly in the case of random problems.