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First, we study conditions favouring the evolution of cooperation, as it opens the doors for the potentially following specialisation. We demonstrate that these conditions are sensitive to the mechanisms of intra-specific selection (or selection methods). Next, we take an engineering perspective and we study division of labour at the genetic level in teams of artificial agents. We devise efficient algorithms to evolve fixed assignments of agents to castes (or team compositions). To this end, we propose a novel technique that exchanges agents between teams, which greatly eases the search for the optimal composition. Finally, we take a biological perspective and we study division of labour at the behavioural level in simulated ant colonies. We quantify the efficiency of task allocation algorithms, which have been used to explain specialisation in social insects. We show that these algorithms fail to induce precise reallocation of the workforce in response to changes in the environment. We overcome this issue by modelling task allocation with artificial neural networks, which lead to near optimal colony performance.
Overall, this work contributes both to biology and to engineering. We shed light on the evolution of cooperation and division of labour in social insects, and we show how to efficiently optimise teams of artificial agents. We resolve the encountered methodological issues and demonstrate the power of evolutionary simulations to address biological questions and to tackle engineering problems.",
EPFL Presentee le 22 mars 2013 Suisse
THESE NO 5687 (2013)
acceptee sur proposition du jury: Prof. A. Billard, presidente du jury Prof. D. Floreano, Prof. L. Keller, directeurs de these Prof. M.-O. Hongler, rapporteur Prof. L. Lehmann, rapporteur",
Genetic Programming entries for Pawel Lichocki