Cooperative co-evolutionary algorithm-how to evaluate a module?

When we talk about co-evolution, we often consider it as competitive co-evolution (CompCE). Examples include co-evolution of training data and neural networks, co-evolution of game players, and so on. Recently, several researchers have studied another kind of co-evolution- cooperative co-evolution (CoopCE). While CompCE tries to get more competitive individuals through evolution, the goal of CoopCE is to find individuals from which better systems can be constructed. The basic idea of CoopCE is to divide-and-conquer: divide a large system into many modules, evolve the modules separately, and then combine them together again to form the whole system. Depending on how to divide-and-conquer, different cooperative co-evolutionary algorithms (CoopCEAs) have been proposed in the literature. Results obtained so far strongly support the usefulness of CoopCEAs. To study the CoopCEAs systematically, we proposed a society model, which is a common framework of most existing CoopCEAs. From this model, we can see that there are still many open problems related to CoopCEAs. To make CoopCEAs generally useful, it is necessary to study and solve these problems. In this paper, we focus the discussion on evaluation of the modules-which is one of the key point in using CoopCEAs. To be concrete, we will apply the model to evolutionary learning of RBF-neural networks, and show the effectiveness of different evaluation methods through experiments.