An Improvement for the Dynamic Distributed Double Guided Genetic Algorithm for MAX-CSPs

Z. Nisrine, S. Bouamama, and K. Ghdira (Tunisia)


Maximal constraint satisfaction problems, multi-agent systems, genetic algorithms.


This paper propose a new approach to improve the Dynamic Distributed Double Guided Genetic Algorithm (D3 G2 A) dealing with Maximal Constraint Satisfaction Problems. Inspired by the NEO-DARWINISM theory and the nature laws, D3 G2 A consists in creating agents cooperating together to solve problems. In D3 G2 A, It was proved that the spent CPU time could be improved. The new approach Inspired by the D3 G2 A for CSOP and Ī£CSPs, 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.

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