The dynamics of collective sorting robot-like ants and ant-like robots
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Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Swarm intelligence: from natural to artificial systems
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ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
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Evolving agent swarms for clustering and sorting
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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This paper describes an evolutionary approach to the design of controllers for a team of collective agents. The agents are able to perform ant-like annular sorting, similar to the sorting behaviour seen in the ant species Temnothorax albipennis. While most previous research on ant-like sorting has used hard-wired rules, this study uses neural network controllers designed by artificial evolution. The agents have very simple and purely local sensory capabilities, and can only communicate through stigmergy. Experiments are performed in simulation. The evolved behaviours are presented, analyzed, and compared to previous research on ant-like annular sorting. The results show that artificial evolution is able to create efficient, simple, and scalable controllers able to perform annular sorting of three object types.