Environment-driven Distributed Evolutionary Adaptation in a Population of Autonomous Robotic Agents

International audienceThis paper is concerned with a fixed-size population of autonomous agents facing unknown, possibly changing, environments. The motivation is to design an embodied evolutionary algorithm that can cope with the implicit fitness function hidden in the environment so as to provide adaptation in the long run at the level of the population. The proposed algorithm, termed mEDEA, is shown to be both efficient in an unknown environments and robust to abrupt and unpredicted changes in the environment. The emergence of consensus towards specific behavioural strategies is examined, with a particular focus on algorithmic stability. To conclude the paper a real world implementation of the algorithm in a population of 20 real-world e-puck robots is described and the algorithm is shown to perform remarkably well in the face of environmental constraints and technical issues