Automatic Tuning of Agent-Based Models Using Genetic Algorithms

When developping multi-agent systems (MAS) or models in the context of agent-based simulation (ABS), the tuning of the model constitutes a crucial step of the design process. Indeed, agent-based models are generally characterized by lots of parameters, which together determine the global dynamics of the system. Moreover, small changes made to a single parameter sometimes lead to a radical modification of the dynamics of the whole system. The development and the parameter setting of an agent-based model can thus become long and tedious if we have no accurate, automatic and systematic strategy to explore this parameter space. That’s the development of such a strategy that we work on, suggesting the use of genetic algorithms. The idea is to capture in the fitness function the goal of the design process (efficiency for MAS that realize a given function, realism for agent-based models, etc.) and to make the model automatically evolve in that direction. However the use of genetic algorithms (GA) in the context of ABS raises specific difficulties that we develop in this article, explaining possible solutions and illustrating them on a simple and well-known model: the food-foraging by a colony of ants. We apply the method to a more complex example. We work on the simulation of the glycolysis and the phosphotranferase systems in Escherichia coli. In this work, we are interested in testing the hypothesis of hyperstructures, which are believed to improve the behavior of a cell. We try to determine under what conditions this may be true, and how these hyperstructures may function.