Environment-driven Open-ended Evolution with a Population of Autonomous Robots

This paper summarizes work done since 2009 on running swarm of autonomous robots with Environment-driven Distributed Evolutionary Adaptation algorithms (EDEA). The motivation behind this work is to design algorithms that can cope with unknown environmental pressure, that is to learn (through evolution) efficient survival strategies in order to address a priori unknown constraints existing in the environment. We are concerned with a fixed-size population of autonomous robotic agents facing unknown, possibly changing, environments, which may require the robots to evolve particular behaviors such as cooperative behaviors, specialized behaviors, etc. We describe a particular flavor of EDEA, termed minimal EDEA (mEDEA), and summarize results obtained so far, both with real robots and in simulation. This algorithm is shown to be both efficient in unknown environment and robust with respect to abrupt, unpredicted, and possibly lethal changes in the environment. Moreover, the ability of mEDEA to evolve various types of behavior, including cooperative and specialized behaviors, is summarized.