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 comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Techniques for analysis and calibration of multi-agent simulations
ESAW'04 Proceedings of the 5th international conference on Engineering Societies in the Agents World
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Contribution to the Control of a MAS's Global Behaviour: Reinforcement Learning Tools
Engineering Societies in the Agents World IX
Evolving viral marketing strategies
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Finding forms of flocking: evolutionary search in ABM parameter-spaces
MABS'10 Proceedings of the 11th international conference on Multi-agent-based simulation
Estimating functional agent-based models: an application to bid shading in online markets format
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Synergy in ant foraging strategies: memory and communication alone and in combination
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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When developing 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 brings 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.