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Extending Artificial Development: Exploiting Environmental Information for the Achievement of Phenotypic Plasticity

  • Gunnar Tufte
  • Pauline C. Haddow
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4684)

Abstract

Biological organisms have an inherent ability to respond to environmental changes. The response can emerge as organisms that can develop to structural and behavioural different phenotypes. The cue to what phenotypic property to express is cued by the environment. This implies that the information necessary for a single genotype to develop to different phenotypes is the genome itself and the information provided by the environment i.e. phenotypic plasticity. This concept is incorporated in the development model presented herein so as to demonstrate how an evolved genome can express different phenotypes depending on the present environment which the phenotype has to develop and survive in. An experimental approach is used to show the concept to evolved robust behaviour in different environments and to evolve genomes that can be triggered to express different behaviour depending on the present environment.

Keywords

External Environment Phenotypic Plasticity Empty Cell Development Step Active Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kitano, H.: Designing neural networks using genetic algorithms with graph generation systems. Complex Systems 4(4), 461–476 (1990)zbMATHGoogle Scholar
  2. 2.
    Sipper, M., Sanchez, E., Mange, D., Tomassini, M., Pérez-Uribe, A., Stauffer, A.: A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems. IEEE Transactions on Evolutionary Computation 1(1), 83–97 (1997)CrossRefGoogle Scholar
  3. 3.
    Lantin, M., Fracchia, F.: Generalized context-sensative cell systems. In: Proceedings of Information Processing in Cells and Tissues, University of Liverpool, pp. 42–54 (1995)Google Scholar
  4. 4.
    Larsen, E.W.: Environment, development, and Evolution Toward a Synthesis. In: A View of Phenotypic Plastisity from Molecules to Morphogenisis, pp. 117–124. MIT Press, Cambridge (2004)Google Scholar
  5. 5.
    Quick, T., Dautenhahn, K., Nehaniv, C.L., Roberts, G.: On bots and bacteria: Ontology independent embodiment. In: Floreano, D., Mondada, F. (eds.) ECAL 1999. LNCS, vol. 1674, pp. 339–343. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Beer, R.D.: A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 1–2(72), 173–215 (1995)CrossRefGoogle Scholar
  7. 7.
    Miller, J.F.: Evolving developmental programs for adaptation, morphogenesis, and self-repair. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 256–265. Springer, Heidelberg (2003)Google Scholar
  8. 8.
    Tufte, G., Haddow, P.C.: Achieving environmental tolerance through the initiation and exploitation of external information. In: CEC 2007. Congress on Evolutionary Computation, IEEE Computer Society Press, Los Alamitos (2007)Google Scholar
  9. 9.
    Federici, D.: Evolving a neurocontroller through a process of embryogeny. In: SAB 2004. LNCS, pp. 373–384. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Gordon, T.G.W., Bentley, P.J.: Development brings scalability to hardware evolution. In: EH 2005. The 2005 NASA/DOD Conference on Evolvable Hardware, pp. 272–279. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  11. 11.
    Miller, J.F.: Evolving a self-repairing, self-regulating, french flag organism. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 129–139. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Tufte, G., Haddow, P.C.: Identification of functionality during development on a virtual sblock fpga. In: CEC 2003. Congress on Evolutionary Computation, pp. 731–738. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  13. 13.
    Bongard, J.C., Pfeifer, R.: Morpho-functional Machines: The New Species (Designing Embodied Intelligence). In: Evolving complete agents using artificial ontogeny, pp. 237–258. Springer, Heidelberg (2003)Google Scholar
  14. 14.
    Viswanathan, S., Pollack, J.B.: How artificial ontogenies can retard evolution. In: GECCO 2005. Genetic and Evolutionary Computation, ACM Press, New York (2005)Google Scholar
  15. 15.
    Tufte, G.: Cellular development: A search for functionality. In: CEC 2006. Congress on Evolutionary Computation, IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  16. 16.
    Haddow, P.C., Tufte, G.: An evolvable hardware FPGA for adaptive hardware. In: CEC 2000. Congress on Evolutionary Computation, pp. 553–560 (2000)Google Scholar
  17. 17.
    Sipper, M.: Evolution of Parallel Cellular Machines The Cellular Programming Approach. Springer, Heidelberg (1997)Google Scholar
  18. 18.
    Beiu, V., Yang, J.M., Quintana, L., Avedillo, M.J.: Vlsi implementations of threshold logic-a comprehensive survey. IEEE Transactions on Neural Networks 14(5), 1217–1243 (2003)CrossRefGoogle Scholar
  19. 19.
    Spears, W.M.: Gac ga archives source code collection webpage (1991), http://www.aic.nrl.navy.mil/galist/src/
  20. 20.
    Nallatech: BenERA User Guide, nt107-0072 (issue 3) 09-04-2002 edn. (2002)Google Scholar
  21. 21.
    Tufte, G., Haddow, P.C.: Biologically-inspired: A rule-based self-reconfiguration of a virtex chip. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2004. LNCS, pp. 1249–1256. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gunnar Tufte
    • 1
  • Pauline C. Haddow
    • 1
  1. 1.The Norwegian University of Science and Technology, Department of Computer and Information Science, Sem Selandsvei 7-9, 7491 TrondheimNorway

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