A general framework for cooperative co-evolutionary algorithms: a society model

Compared with conventional algorithms, evolutionary algorithms (EAs) are usually more efficient for system design because they can provide more opportunity for obtaining the global optimal solution. However, the EAs cannot be used directly to design large-scale systems because a large amount of computations are required. To solve this problem, many approaches have been proposed in the literature. Cooperative co-evolutionary algorithms (CCEA) are possibly one of the most efficient approaches. The basic idea of most CCEAs is divide-and-conquer: divide the system into many modules, define an individual as a candidate of a module, assign a population to each module, find good individuals within each population, and put them together again to form the whole system. The author generalizes earlier studies, and introduces a society model for the study of CCEAs. Based on the society model, the author formulates existing CCEAs in a general framework. The author also provides several case studies, all of which are interesting topics, for future research.

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