Evolving heterogeneous social fabrics for the solution of real valued optimization problems using cultural algorithms

A question of interest to those studying the emergence of social systems is the extent to which their organizational structure reflects the structures of the problems that are presented to them. In a recent study [14] used Cultural Algorithms as a framework in which to empirically address this and related questions. There, a problem generator based upon Langton´s model of complexity was used to produce multi-dimensional real-valued problem landscapes of varying complexities. Various homogeneous social networks were then tested against the range of problems to see whether certain homogeneous networks were better at distributing problem solving knowledge from the Belief Space to individuals in the population.The experiments suggested that different network structures worked better in the distribution of knowledge for some optimization problems than others. If this is the case, then in a situation where several different problems are presented to a group, they may wish to utilize more than one network to solve them. In this paper, we investigate the advantages of utilizing a heterogeneous network over a suite of different problem. We show that heterogeneous approaches begin to dominate homogeneous ones as the problem complexity increases.