A real-coded genetic algorithm for two-mode KL-means partitioning with application to homogeneity blockmodeling

The two-mode KL-means partitioning (TMKLMP) problem has a number of important applications in the social and physical sciences. For example, the intra-block variability measure associated with TMKLMP underscores its direct relevance to two-mode homogeneity blockmodeling of binary and real-valued social networks. We present a real-coded genetic algorithm for obtaining TMKLMP solutions. A simulation study showed that the new algorithm compares favorably to a multistart implementation of a two-mode KL-means heuristic, which is recognized as a top-performing method for TMKLMP. The merit of the proposed method is demonstrated via an application to the blockmodeling of social network data associated with signing of environmental advertisements in the New York Times as a part of the Turning Point Project.