Liviu Panait and Sean Luke
Cooperative coevolutionary algorithms are a popular approach to learning via problem decomposition. One important aspect of cooperative coevolutionary algorithms concerns how to select collaborators for computing the fitness of individuals in different populations. We argue that using a fixed number of collaborators during the entire search may be suboptimal. We experiment with a simple ad-hoc scheme that varies the numbers of collaborators over time. Empirical comparisons in a series of problem domains indicate that decreasing the numbers of collaborators over time fares better than keeping the number fixed. We conclude with a brief discussion of our findings and suggest directions for future research.