Application of genetic algorithms to robotic swarm simulation

Research projects about evolution of agents in a cellular world are not new topics in the artificial life (AL) fields. However, most of the studies focus on those fundamental, social behaviours like energy preservation, pattern formation or leader following etc. This paper presents experiments about applications of genetic algorithms (GAs) to an empirical multiple robot cooperative task: unknown environment exploration. These experiments investigate the effectiveness of GAs for evolving behaviours of individual swarm members that constitute good collective results. They try to answer the questions of (i) Can GAs find such behaviours, or, do such behaviours exist? (ii) Are these behaviours sensitive to environmental changes?.