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In this paper, we describe a study of the evolution of consensus, a cooperative behavior in which members in both homogeneous and heterogeneous groups, must agree on information sensed in their environment. We conducted the study using digital evolution, a form of evolutionary computation where a population of computer programs (digital organisms) exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. We placed these digital organisms into groups whose fitness relied upon their ability to perform consensus. We then tested different degrees and types of genetic variation present in the population, based on biologically inspired models of gene flow, including mutation, sexual recombination, migration, and horizontal gene transfer. Our experimental treatments examined the effect of these processes on genetic variation and groups' ability to reach consensus. The results of these experiments demonstrate that while genetic heterogeneity within groups increases the difficulty of the consensus task, a surprising number of groups were able to overcome these obstacles and evolve this cooperative behavior.