Abstract
We investigate the effect that population structure has upon the course of artificial evolution. We represent an arbitrary population structure by embedding a population of individuals in a graph. Each individual resides at a vertex of the graph and can only choose a mating partner from among its neighbors in the graph. Each individual mates with the selected partner and is replaced by the resultant offspring in the next generation. We embed populations in a variety of trees and mesh-structured graphs and observe differences in rates of change of average fitness and percent polymorphism over successive generations. Results indicate that populations embedded in sparse random graphs having relatively low diameter yield results similar to those embedded in complete graphs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Albert, R., Barabasi, A.L.: Statistical mechanics of complex networks. Review of Modern Physics 74, 47–97 (2002)
De Jong, K.A., Sarma, J.: On decentralizing selection algorithms. In: Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA 1995), pp. 17–23. Morgan Kaufmann, San Francisco (1995)
Farley, A.M.: Minimal broadcast graphs. Networks 9, 313–332 (1979)
Giacobini, M., Alba, E., Tomassini, M.: Selection intensity in asynchronous cellular evolutionary algorithms. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, Springer, Heidelberg (2003)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Cambridge (1989)
Hedrick, P.W.: Genetics of Populations. Jones and Bartlett, Sudbury,MA (2000)
Kauffman, S.A.: The Origins of Order. Oxford University Press, New York (1993)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge, Ma. (1996)
Mitchell, M., Forrest, S., Holland, J.H.: The royal road for genetic algorithms: Fitness landscapes and GA performance. In: Varela, F.J., Bourgine, P. (eds.) Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, MIT Press, Cambridge, MA (1992)
Maynard Smith, J.: The Theory of Evolution, 3rd edn. Cambridge Univ. Press, New York (1993)
Maynard Smith, J.: Evolutionary Genetics, 2nd edn. Oxford University Press, New York (1998)
Proskurowski, A.: Minimum broadcast trees. IEEE Transactions on Computers 5, 363–366 (1981)
Sarma, J., De Jong, K.A.: An analysis of local selection algorithms in a spatially structured evolutionary algorithm. In: Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA 1997), pp. 181–187. Morgan Kaufmann, San Francisco (1997)
Vose, M.: The Simple Genetic Algorithm: Foundations and Theory. MIT Press, Cambridge, MA (1999)
Watts, D.: Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton Univ. Press, Princeton (1999)
Watts, D., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998)
Whitley, D., Rana, S., Heckendorn, R.: Exploiting separability in search: The island model genetic algorithm. Journal of Computing and Information Technology 7, 33–47 (1999)
Wright, S.: Evolution in Mendelian populations. Genetics 16, 97–159 (1931)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Farley, A.M. (2006). Population Structure and Artificial Evolution. In: Talbi, EG., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2005. Lecture Notes in Computer Science, vol 3871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11740698_19
Download citation
DOI: https://doi.org/10.1007/11740698_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33589-4
Online ISBN: 978-3-540-33590-0
eBook Packages: Computer ScienceComputer Science (R0)