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Partial constraint satisfaction
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An improvement for the dynamic distributed double guided genetic algorithm for Max-CSPs
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
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AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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The Dynamic Distributed Double Guided Genetic Algorithm (D3G2A) deals with Maximal Constraint Satisfaction Problems. The approach consists in creating agents cooperating together to solve problems. This paper aims to improve the D3G2A. The main purpose is to balance agent loads this distributed approach. The proposed approach will redistribute the load of Species agents more equally in order to improve the CPU time. This improvement allows not only reduction of the number of Species agents but also decreases communications agents cost. In this regard, a sub-population is composed of chromosomes violating a number of constraints in the same interval. Secondly, another proposed approach will redistribute the work load. This improvement allows diminution of inactive Species agents and it results in a balanced workloads. In fact, by analogy with social animal flocks, Species agents cooperate together to do all tasks in a reduced CPU time. Several comparisons are made about new approaches with the old version of the D3G2A. Results are promising.