  A robotic swarm that is required to operate for long periods in a potentially
unknown environment can use both evolution and individual learning methods in
order to adapt. However, the role played by the environment in influencing the
effectiveness of each type of learning is not well understood. In this paper,
we address this question by analysing the performance of a swarm in a range of
simulated, dynamic environments where a distributed evolutionary algorithm for
evolving a controller is augmented with a number of different individual
learning mechanisms. The learning mechanisms themselves are defined by
parameters which can be either fixed or inherited. We conduct experiments in a
range of dynamic environments whose characteristics are varied so as to present
different opportunities for learning. Results enable us to map environmental
characteristics to the most effective learning algorithm.

Neural and Evolutionary Computing