  This paper proposes a self-adaptation mechanism to manage the resources
allocated to the different species comprising a cooperative coevolutionary
algorithm. The proposed approach relies on a dynamic extension to the
well-known multi-armed bandit framework. At each iteration, the dynamic
multi-armed bandit makes a decision on which species to evolve for a
generation, using the history of progress made by the different species to
guide the decisions. We show experimentally, on a benchmark and a real-world
problem, that evolving the different populations at different paces allows not
only to identify solutions more rapidly, but also improves the capacity of
cooperative coevolution to solve more complex problems.
