A computational basis for the presence of sub-cultures in cultural algoithms

Cultural Algorithms are computational models of social evolution based upon principle of Cultural Evolution. A Cultural Algorithm consists of a Belief Space consisting of a network of active and passive knowledge sources and a Population Space of agents. The agents are connected via a social fabric over which information used in agent problem solving as passed. The knowledge sources in the Belief Space compete with each other in order to influence the decision making of agents in the Population Space. Likewise, the problem solving experiences of agents in the Population Space are sent back to the Belief Space and used to update the knowledge sources there. It is a dual inheritance system in which both the Population and Belief spaces evolve in parallel. In this paper we investigate why sub-cultures can emerge in the Population Space in response to the complexity of the problems presented to a Cultural System. This system is compared with other evolutionary approaches relative to a variety of benchmark problem of varying complexity. We show that the presence of sub-cultures can provide computational advantages in problem landscape that are generated by multiple independent processes. These advantages can increase problem solving efficiency along with the ability to dampen the impact of increase in problem complexity.