Resource-aware exploration of the emergent dynamics of simulated systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
Designing computational steering facilities for distributed agent based simulations
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
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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.