In this paper we investigate the performance of Cultural Algorithms over the complete range of system complexities, from fixed to chaotic.In order to apply the Cultural Algorithm over all complexity classes we generalize on its co-evolutionary nature to keep the variation in the population across all complexities. Based on previous cultural algorithm approaches, we were to extend the existing models to produce a more general one that could be applied across all complexity classes. We produced a new version of the Cultural Algorithms Toolkit, CAT 2.0, which supported a variety of co-evolutionary features at both the Knowledge and Population levels. We then applied the system to the solution of a 150 randomly generated problems that ranged from simple to chaotic complexity classes. As a result we were able to produce the following conclusions: No homogeneous Social Fabric tested was dominant over all categories of complexity. As the complexity of problems increased, so did the complexity of the Social Fabric that was need to deal with it efficiently. In other words, there was experimental evidence that social structure can be related to the frequency and complexity type of the problems that presented to a cultural system.
['Xiangdong Che', 'Mostafa Z. Ali', 'Robert G. Reynolds']
Weaving the Social Fabric: The Past, Present, and Future of Optimization Problem Solving with Cultural Algorithms