Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
New methods to color the vertices of a graph
Communications of the ACM
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
The Many Paths to Satisfaction
Constraint Processing, Selected Papers
Relevance estimation and value calibration of evolutionary algorithm parameters
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Solving constraint satisfaction problems using hybrid evolutionarysearch
IEEE Transactions on Evolutionary Computation
Ants can solve constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
Comparing evolutionary algorithms on binary constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
Towards a population-based framework for improving stochastic local search algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Constraint Directed Network Artificial Immune System is an artificial immune algorithm, recently proposed, to solve constraint satisfaction problems. The algorithm has shown to be able to solve hard instances. However, some problems are still unsolved using this approach. In this paper, we propose a method to improve the search done by the algorithm. Our method can be included in other immune algorithms which manage constraints. The tests are carried out to solve very hard instances randomly generated of 3-colouring problems. The results show that using our method, the algorithm is able to solve more problems in less execution time.