Identifying species by genetic clustering

Complex artificial life simulations can yield substantially distinct populations of agents corresponding to different adaptations to a common environment or specialized adaptations to different environments. Here we show how a standard clustering algorithm applied to the artificial genomes of such agents can be used to discover and characterize these subpopulations. As gene changes propagate throughout the population, new subpopulations are produced, which show up as new clusters. Cluster centroids allow us to characterize these different subpopulations and identify their distinct adaptation mechanisms. We suggest these subpopulations may reasonably be thought of as species, even if the simulation software allows interbreeding between members of the different subpopulations, and provide evidence of both sympatric and allopatric speciation in the Polyworld artificial life system. Analyzing intraand inter-cluster fecundity differences and offspring production rates suggests that speciation is being promoted by a combination of post-zygotic selection (lower fitness of hybrid offspring) and pre-zygotic selection (assortative mating), which may be fostered by reinforcement (the Wallace effect).