Cultural Algorithms: Knowledge-driven engineering optimization via weaving a social fabric as an enhanced influence function

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems. While many successful real-world applications of Cultural Algorithms have been produced, we are interested in studying the fundamental computational processes involved the use of Cultural Systems as problem solvers. In previous work the influence of the knowledge sources have been on individuals in the population only. In this paper we introduce the notion of a social fabric in which the expression of knowledge sources can be distributed through the population. We apply the social fabric function to the solution of a tension/compression spring design problem. We show that different parameter combinations can affect the rate of solution.