  In this paper, we study the impact of selection methods in the context of
on-line on-board distributed evolutionary algorithms. We propose a variant of
the mEDEA algorithm in which we add a selection operator, and we apply it in a
taskdriven scenario. We evaluate four selection methods that induce different
intensity of selection pressure in a multi-robot navigation with obstacle
avoidance task and a collective foraging task. Experiments show that a small
intensity of selection pressure is sufficient to rapidly obtain good
performances on the tasks at hand. We introduce different measures to compare
the selection methods, and show that the higher the selection pressure, the
better the performances obtained, especially for the more challenging food
foraging task.

Artificial Intelligence;Multiagent Systems;Neural and Evolutionary Computing;Robotics