Exploiting the deep structure of constraint problems
Artificial Intelligence
Analysis of two simple heuristics on a random instance of k-SAT
Journal of Algorithms
Scaling Effects in the CSP Phase Transition
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Approximating the Unsatisfiability Threshold of Random Formulas (Extended Abstract)
ESA '96 Proceedings of the Fourth Annual European Symposium on Algorithms
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Problem structure in the presence of perturbations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Boosting ACO with a Preprocessing Step
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Constraint (Logic) Programming: A Survey on Research and Applications
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
A tabu search evolutionary algorithm for solving constraint satisfaction problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Is computational complexity a barrier to manipulation?
Annals of Mathematics and Artificial Intelligence
On generators of random quasigroup problems
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
Challenging heuristics: evolving binary constraint satisfaction problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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We study the experimental consequences of a recent theoretical result by Achlioptas et al. that shows that conventional models of random problems are trivially insoluble in the limit. We survey the literature to identify experimental studies that lie within the scope of this result. We then estimate theoretically and measure experimentally the size at which problems start to become trivially insoluble. Our results demonstrate that most (but not all) of these experimental studies are luckily unaffected by this result. We also study an alternative model of random problems that does not suffer from this asymptotic weakness. We show that, at a typical problem size used in experimental studies, this model looks similar to conventional models. Finally, we generalize this model so that we can independently adjust the constraint tightness and density.