Genetic algorithms in a multi-agent system

Determining an optimal solution is almost impossible but trying to improve an existing solution is a way to lead to a better scheduling. We use a multi-agent system guided by a multiobjective genetic algorithm to find a balance point with respect to a solution of the Pareto front. This solution is not the best one but it allows a multicriteria optimization. By crossover and mutation of agents, according to their fitness function, we improve an existing solution. Therefore, the construction of some system simulating living organisms or social systems, cannot be modelled using a strictly mechanical approach. They are typically adaptive and their behaviour is not regular. The multi-agent system must express radical characters, such as reification of emergence, the property of controlled self-reproduction of groups of agents and not linear behaviour