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Within the framework of Constraint satisfaction and optimization problem (CSOP), we introduce a new optimization distributed method based on Genetic Algorithms (GA). This method consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA on its own sub-population. This GA is sometimes random and sometimes guided by both the template concept and by the Min-conflict-heuristic. In addition with guidance, our approach is based on NEO-DARWINISM theory and on the nature laws. In fact, by reference to their specificity the new algorithm will let the agents able to count their own GA parameters. In order to show DGA advantages, experimental comparison with GGA is provided by their application on the large processors configuration problem.

D3G2A: A DYNAMIC DISTRIBUTED DOUBLE GUIDED GENETIC ALGORITHM FOR THE CASE OF THE PROCESSORS CONFIGURATION PROBLEM