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Inspired by the guided genetic algorithm (GGA) and by the dynamic distributed double guided genetic algorithm for Max_CSPs, D3G2A is a new multi-agent approach which addresses additive constraint satisfaction problems (ΣCSPs). This algorithm consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA, guided by both the template concept and the min-conflict-heuristic. In one hand, genetic algorithms (GAs) efficiency provides good solution quality for additive CSPs and, in the other hand, multi-agent principles reduces GA temporal complexity. First, our approach is enhanced by a new parameter called guidance operator. The latter allows not only diversification but also an escaping from local optima. In the second step, the performed GAs no longer have the same cross-over and mutation probabilities. This is done on the basis of NEO-DARWINISM theory and the nature laws. In fact the new algorithm let the species agents able to count their GA parameters. In order to show D3G2A advantages, experimental comparison with GGA is provided.