Impact of problem decomposition on Cooperative Coevolution

Variable Interaction Learning (VIL) is an emerging technique regarding detecting interacting variables so that Cooperative Coevolutionary Evolutionary Algorithms (CCEAs) can decompose problems accordingly and tackle subproblems of smaller sizes. While previous approaches are developed to efficiently perform VIL, no study has been on the actual usefulness of the detected variable interactions in terms of the performance of CCEAs. Since VIL is a computationally expensive task by itself, overly spending time on VIL without notable benefits for CCEAs should be avoided. It is hence critical to study the real impact of problem decomposition on CCEAs. We conduct empirical studies to address three closely related questions: 1) will a better problem decomposition lead to better performance of CCEAs, 2) when will improving problem decomposition benefit CCEAs, and 3) to what extent will improving problem decomposition enhance the performance of CCEAs.