Artificial evolution in the natural world

Evolutionary algorithms (EA) have proved as a robust optimisation meta-heuristic in engineering problems.  When we tackle problems in multi-agent systems, the nonlinear interactions set up an increasingly challenging context.  And if the multi-agents are groups of animals interacting with artificial devices, whose control is evolved the difficulties are even higher.  These occur both in perception when the EA needs to make sense of a collective behaviour from individual ones, and actuation with the uncertainty of how some localised action impacts on the collective.  In this talk we will illustrate these problems with evolution of swarm robots and of bio-hybrid systems in which robotic devices interact with groups of different animal species.