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Genetic clustering for the identification of species

Published:12 July 2011Publication History

ABSTRACT

Artificial life simulations can yield distinct populations of agents representing different adaptations to a common environment or specialized adaptations to different environments. Here we apply a standard clustering algorithm to the genomes of such agents to discover and characterize these subpopulations. As evolution proceeds 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. Our results indicate both sympatric and allopatric speciation are present in the Polyworld artificial life system. Our analysis suggests that intra- and inter-cluster fecundity differences may be sufficient to foster sympatric speciation in artificial and biological ecosystems.

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  1. Genetic clustering for the identification of species

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          cover image ACM Conferences
          GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
          July 2011
          1548 pages
          ISBN:9781450306904
          DOI:10.1145/2001858

          Copyright © 2011 Authors

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 12 July 2011

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