Environment-driven Embodied Evolution in a Population of Autonomous 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 resulting algorithm, termed Minimal Environment-driven Distributed Evolutionary Adaptation (mEDEA), is shown to be both efficient in unknown environment and robust with regards to abrupt, unpredicted, and possibly lethal changes in the environment