Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
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
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
Learning and optimization using the clonal selection principle
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
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We propose in this paper an artificial immune system to solve CSPs. The algorithm has been designed following the framework proposed by de Castro and Timmis. We have calibrated our algorithm using Relevance Estimation and Value Calibration (REVAC), that is a new technique, recently introduced to find the parameter values for evolutionary algorithms. The tests were carried out using random generated binary constraint satisfaction problems on the transition phase where are the hardest problems. The algorithm shown to be able to find quickly good quality solutions.