A genetic local search algorithm for random binary constraint satisfaction problems
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Multiple Populations Guided by the Constraint-Graph for CSP
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
Inheriting Parents Operators: A New Dynamic Strategy for Improving Evolutionary Algorithms
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Distributed constraint satisfaction, restricted recombination, and genetic protocols
Applied Soft Computing
Managing Diversity on an AIS That Solves 3-Colouring Problems
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
NAIS: a calibrated immune inspired algorithm to solve binary constraint satisfaction problems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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We combine the concept of evolutionary search with the systematic search concepts of arc revision and hill climbing to form a hybrid system that quickly finds solutions to static and dynamic constraint satisfaction problems (CSPs). Furthermore, we present the results of two experiments. In the first experiment, we show that our evolutionary hybrid outperforms a well-known hill climber, the iterative descent method (IDM), on a test suite of 750 randomly generated static CSPs. These results show the existence of a “mushy region” which contains a phase transition between CSPs that are based on constraint networks that have one or more solutions and those based on networks that have no solution. In the second experiment, we use a test suite of 250 additional randomly generated CSPs to compare two approaches for solving CSPs. In the first method, all the constraints of a CSP are known by the hybrid at run-time. We refer to this method as the static method for solving CSPs. In the second method, only half of the constraints of a CSPs are known at run-time. Each time that our hybrid system discovers a solution that satisfies all of the constraints of the current network, one additional constraint is added. This process of incrementally adding constraints is continued until all the constraints of a CSP are known by the algorithm or until the maximum number of individuals has been created. We refer to this second method as the dynamic method for solving CSPs. Our results show hybrid evolutionary search performs exceptionally well in the presence of dynamic (incremental) constraints, then also illuminate a potential hazard with solving dynamic CSPs