Systematic and nonsystematic search strategies
Proceedings of the first international conference on Artificial intelligence planning systems
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Interleaved depth-first search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Enhancements of branch and bound methods for the maximal constraint satisfaction problem
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Load Balancing for the Dynamic Distributed Double Guided Genetic Algorithm for MAX-CSPs
International Journal of Artificial Life Research
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This paper propose a new approach to improve the Dynamic Distributed Double Guided Genetic Algorithm (D3G2A) dealing with Maximal Constraint Satisfaction Problems. Inspired by the NEO-DARWINISM theory and the nature laws, D3G2 A consists in creating agents cooperating together to solve problems. In D3G2 A, It was proved that the spent CPU time could be improved. The new approach Inspired by the D3G2A for CSOP and ΣCSPs, will redistribute the load of species agents more equally in order to better the CPU time. This improvement allows not only reduction in species agent's number but also decrease communications agents cost. Thus, a sub-population is composed of chromosomes violating a number of constraints in the same interval. In the present paper, the new approach is first described and then compared with the old one. Results of experimentations are analyzed and discussed.