Evolving aggregation behaviors for swarm robotic systems: a systematic case study

Abstract

When one attempts to use artificial evolution to develop behaviors for a swarm robotic system, he is faced with decisions to be made regarding the parameters of the evolution. In this paper, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm robotic system are systematically studied with different parameter settings. Four experiments are conducted varying some of the parameters, and rules of thumb are derived, which can be of guidance to the use of evolutionary methods to generate other swarm robotic behaviors.