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 Multi-Objective Genetic Algorithm to find a balance point in the respect of a solution of the Pareto front. Of course, this solution isn't the best but allows a multi-criteria 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, property of controlled self-reproduction of groups of agents and not linear behaviour.