When Evolving Populations is Better than Coevolving Individuals: The Blind Mice Problem

This paper is about the evolutionary design of multi-agent systems. An important part of recent research in this domain has been focusing on collaborative revolutionary methods. We expose possible drawbacks of these methods, and show that for a non-trivial problem called the "blind mice" problem, a classical GA approach in which whole populations are evaluated, selected and crossed together (with a few tweaks) finds an elegant and non-intuitive solution more efficiently than cooperative coevolution. The difference in efficiency grows with the number of agents within the simulation. We propose an explanation for this poorer performance of cooperative coevolution, based on the intrinsic fragility of the evaluation process. This explanation is supported by theoretical and experimental arguments.