Skip to Main Content
Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems using an influence function inspired by the marginal value theorem from population biology. We briefly describe an implementation of this approach, the cultural algorithms toolkit (CAT) in the Repast agent-based simulation environment. Next we introduce the notion of "social fabric" which provides a framework in which the knowledge sources can access the social networks to which individuals can belong. A computational version of the social fabric idea is then implemented as an extension of the influence function in the CAT system. We then apply the enhanced system to a problem in engineering design, the "pressure vessel problem". For this problem, we show that the enhanced system with the social fabric outperforms the CAT system without it. We demonstrate also that the frequency with which the knowledge sources are able to access the network can affect the problem solving process.