Genetic Programming and Evolvable Machines

, Volume 7, Issue 3, pp 253–281 | Cite as

Unbounded evolutionary dynamics in a system of agents that actively process and transform their environment

Original article

Abstract

Bedau et al.'s statistical classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this test more rigorous, and passing it, are two of the most important open problems for research into systems of agents that actively process and transform their environment. This paper presents a detailed description of the application of this test to ‘Geb’, a system designed to verify and extend theories behind the generation of evolutionarily emergent systems. The result is that, according to these statistics, Geb exhibits unbounded evolutionary dynamics, making it the first autonomous artificial system to pass this test. However, having passed it, the most prudent course of action is to look for weaknesses in the test. The test is criticized, most significantly with regard to its normalization method for artificial systems. Furthermore, this paper presents a modified normalization method, based on component activity normalization, that overcomes these criticisms. The results of the revised test, when applied to Geb, indicate that this system does indeed exhibit open-ended evolution.

Keywords

Evolutionary dynamics Variable-size genomes Coevolution Biotic selection Emergence 

References

  1. 1.
    C. Adami and C. T. Brown, “Evolutionary learning in the 2D artificial life system ‘Avida’,” in: Proceedings of Artificial Life IV, R. A. Brooks and P. Maes (eds.), MIT Press: Cambridge, MA, 1994, pp. 377–381.Google Scholar
  2. 2.
    W. B. Arthur, “Inductive reasoning and bounded rationality,” American Economic Review (Papers and Proceedings), vol. 84, pp. 406–411, 1994.Google Scholar
  3. 3.
    W. Banzhaf, “Self-replicating sequences of binary numbers—the build-up of complexity,” Complex Systems, vol. 8, pp. 205–215, 1994.Google Scholar
  4. 4.
    M. A. Bedau and C. T. Brown, “Visualizing evolutionary activity of genotypes,” Artificial Life, vol. 5, pp. 17–35, 1999.Google Scholar
  5. 5.
    M. A. Bedau and N. H. Packard, “Measurement of evolutionary activity, teleology, and life,” in Proceedings of Artificial Life II, C. G. Langton, C. Taylor, J. D. Farmer and S. Rasmussen (eds.), Addison-Wesley: Redwood City, CA, 1991, pp. 431–461.Google Scholar
  6. 6.
    M. A. Bedau, E. Snyder, C. T. Brown and N. H. Packard, “A comparison of evolutionary activity in artificial evolving systems and the biosphere,” in Proceedings of the Fourth European Conference on Artificial Life (ECAL97), Brighton, P. Husbands and I. Harvey (eds.), MIT Press: Cambridge, MA, 1997, pp. 125–134.Google Scholar
  7. 7.
    M. A. Bedau, E. Snyder, and N. H. Packard, “A classification of long-term evolutionary dynamics,” in Proceedings of Artificial Life VI, Los Angeles, C. Adami, R. Belew, H. Kitano and C. Taylor (eds.), MIT Press: Cambridge, MA, 1998, pp. 228–237.Google Scholar
  8. 8.
    E. J. W. Boers and H. Kuiper, “Biological metaphors and the design of modular artificial neural networks,” Master's thesis, Departments of Computer Science and Experimental Psychology, Leiden University, The Netherlands, 1992.Google Scholar
  9. 9.
    S. Bullock and M. A. Bedau, “Exploring the dynamics of adaptation with evolutionary activity plots,” Artificial Life, vol. 12, pp. 193–197, 2006.Google Scholar
  10. 10.
    A. D. Channon, “Passing the ALife test: Activity statistics classify evolution in Geb as unbounded,” in Advances in Artificial Life: Proceedings of the Sixth European Conference on Artificial Life (ECAL2001), Prague, J. Kelemen and P. Sosik (eds.), Springer-Verlag: Heidelberg, 2001, pp. 417–426.Google Scholar
  11. 11.
    A. D. Channon, “Improving and still passing the ALife test: Component-normalised activity statistics classify evolution in Geb as unbounded,” in Proceedings of Artificial Life VIII, Sydney, R. K. Standish, M. A. Bedau and H. A. Abbass (eds.), MIT Press: Cambridge, MA, 2003, pp. 173–181.Google Scholar
  12. 12.
    A. D. Channon and R. I. Damper, “Perpetuating evolutionary emergence,” in From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior (SAB98), Zurich, R. Pfeifer, B. Blumberg, J.-A. Meyer and S. Wilson (eds.), MIT Press: Cambridge, MA, 1998, pp. 534–539.Google Scholar
  13. 13.
    A. D. Channon and R. I. Damper, “Towards the evolutionary emergence of increasingly complex advantageous behaviours,” International Journal of Systems Science, vol. 31, pp. 843–860, 2000.Google Scholar
  14. 14.
    D. Cliff, I. Harvey, and P. Husbands, “Incremental evolution of neural network architectures for adaptive behaviour,” University of Sussex School of Cognitive and Computing Sciences, Tech. Rep. CSRP256, 1992.Google Scholar
  15. 15.
    P. Dittrich and W. Banzhaf, “Self-evolution in a constructive binary string system,” Artificial Life, vol. 4, pp. 203–220, 1998.Google Scholar
  16. 16.
    I. Harvey, P. Husbands, and D. Cliff, “Issues in evolutionary robotics,” in From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (SAB92), J. A. Meyer, H. Roitblat and S. Wilson (eds.), MIT Press/Bradford Books: Cambridge, MA, 1992, pp. 364–373, also available as report CSRP219, School of Cognitive and Computing Sciences, University of Sussex.Google Scholar
  17. 17.
    J. H. Holland, Adaptation in Natural and Artificial Systems, 2nd edition, University of Michigan Press, MIT Press: Ann Arbor, MI, 1975, 1992.Google Scholar
  18. 18.
    P. Husbands, I. Harvey and D. Cliff, “Analysing recurrent dynamical networks evolved for robot control,” in Proceedings of the Third IEE International Conference on Artificial Neural Networks (IEE-ANN93), IEE Press, London, 1993, pp. 158–162.Google Scholar
  19. 19.
    W. B. Langdon, “Pfeiffer—A distributed open-ended evolutionary system,” in Proceedings of the Joint Symposium on Socially Inspired Computing, B. Edmonds, N. Gilbert, S. Gustafson, D. Hales and N. Krasnogor (eds.), Brighton: AISB: The Society for the Study of Artificial Intelligence and the Simulation of Behaviour, 2005, pp. 7–13.Google Scholar
  20. 20.
    K. Lindgren, “Evolutionary phenomena in simple dynamics,” in Proceedings of Artificial Life II, C. G. Langton, C. Taylor, J. D. Farmer and S. Rasmussen (eds.), Addison-Wesley: Redwood City, CA, 1991, pp. 295–312.Google Scholar
  21. 21.
    C. C. Maley, “Four steps toward open-ended evolution,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO99), W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela and R. E. Smith (eds.), Morgan Kaufmann: San Francisco, CA, 1999, vol. 2, pp. 1336–1343.Google Scholar
  22. 22.
    W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, vol. 5, pp. 115–133, 1943.Google Scholar
  23. 23.
    N. H. Packard, “Intrinsic adaptation in a simple model for evolution,” in Santa Fe Institute Studies in the Sciences of Complexity, Vol VI: Proceedings of the Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems (Artificial Life I), C. G. Langton (ed), Addison-Wesley: Redwood City, CA, 1989, pp. 141–155.Google Scholar
  24. 24.
    K. Sims, “Evolving 3d morphology and behavior by competition,” in Proceedings of Artificial Life IV, R. A. Brooks and P. Maes (eds.), MIT Press: Cambridge, MA, 1994, pp. 28–39.Google Scholar
  25. 25.
    K. Sims, “Evolving virtual creatures,” in Proceedings of Computer Graphics (Siggraph ’94) Annual Conference Proceedings, ACM Siggraph: New York, 1994, pp. 15–22.Google Scholar
  26. 26.
    R. K. Standish, “An ecolab perspective on the Bedau evolutionary statistics,” in Proceedings of Artificial Life VII, M. A. Bedau, J. S. McCaskill, N. H. Packard and S. Rasmussen (eds.), MIT Press: Cambridge, MA, 2000, pp. 238–242.Google Scholar
  27. 27.
    A. Stout and L. Spector, “Validation of evolutionary activity metrics for long-term evolutionary dynamics,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005), H.-G. Beyer and U.-M. O’Reilly (eds.), ACM Press: New York, 2005, vol. 1, pp. 137–142.Google Scholar
  28. 28.
    T. Taylor and J. Hallam, “Replaying the tape: An investigation into the role of contingency in evolution,” in Proceedings of Artificial Life VI, Los Angeles, C. Adami, R. Belew, H. Kitano and C. Taylor (eds.), MIT Press, Cambridge, MA, 1998, pp. 256–265.Google Scholar
  29. 29.
    T. Taylor and C. Massey, “Recent developments in the evolution of morphologies and controllers for physically simulated creatures,” Artificial Life, vol. 7, pp. 77–87, 2001.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.School of Computer ScienceThe University of BirminghamEdgbastonUK

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