Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
New methods to color the vertices of a graph
Communications of the ACM
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
The Many Paths to Satisfaction
Constraint Processing, Selected Papers
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
Using self-adaptable probes for dynamic parameter control of parallel evolutionary algorithms
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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Constraint satisfaction problems (CSPs) occur widely in artificial intelligence. In the last twenty years, many algorithms and heuristics were developed to solve CSP. Recently, a constraint-graph based evolutionary algorithm was proposed to solve CSP, [17]. It shown that it is advantageous to take into account the knowledge of the constraint network to design genetic operators. On the other hand, recent publications indicate that parallel genetic algorithms (PGA's) with isolated evolving subpopulations (that exchange individuals from time to time) may offer advantages over sequential approaches, [1]. In this paper we examine the gain of the performance obtained using multiple populations - that evolve in parallel - of the constraint-graph based evolutionary algorithm with a migration policy. We show that a multiple populations approach outperforms a single population implementation when applying it to the 3-coloring problem.