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Evolving Developmental Programs for Adaptation, Morphogenesis, and Self-Repair

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Advances in Artificial Life (ECAL 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2801))

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Abstract

A method for evolving a developmental program inside a cell to create multicellular organisms of arbitrary size and characteristics is described. The cell genotype is evolved so that the organism will organize itself into well defined patterns of differentiated cell types (e.g. the French Flag). In addition the cell genotypes are evolved to respond appropriately to environmental signals that cause metamorphosis of the whole organism. A number of experiments are described that show that the organisms exhibit emergent properties of self-repair and adaptation.

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References

  1. Abelson, H., Allen, D., Coore, D., Hanson, C., Homsy, G., Knight Jr., T., Nagpal, R., Rauch, E., Sussman, G.J., Weiss, R.: Amorphous Computing, MIT Tech. report, AI Memo 1665 (1999)

    Google Scholar 

  2. Astor, J.C., Adami, C.: A Development Model for the Evolution of Artificial Neural Networks. Artificial Life 6, 189–218 (2000)

    Article  Google Scholar 

  3. Bentley, P., Kumar, S.: Three ways to grow designs: A comparison of embryogenies for an Evolutionary Design Problem. In: Proceedings of the Congress on Evolutionary Computation, pp. 35–43. IEEE Press, Los Alamitos (1999)

    Google Scholar 

  4. Boers, E.J.W., Kuiper, H.: Biological metaphors and the design of modular neural networks, Masters thesis, Department of Computer Science and Department of Experimental and Theoretical Psychology, Leiden University (1992)

    Google Scholar 

  5. Bongard, J.C., Pfeifer, R.: Repeated Structure and Dissociation of Genotypic and Phenotypic Complexity in Artificial Ontogeny. In: Spector, L., et al. (eds.) Proceedings of the Genetic and Evol. Comput. Conference, pp. 829–836. Morgan-Kaufmann, San Francisco (2001)

    Google Scholar 

  6. Cangelosi, A., Parisi, D., Nolfi, S.: Cell Division and Migration in a ’Genotype’ for Neural Networks, Tech. report PCIA-93, Inst. of Psych., CNR, Rome (1993)

    Google Scholar 

  7. Dellaert, F.: Toward a Biologically Defensible Model of Development, Masters thesis, Dept. of Computer Eng. and Science, Case Western Reserve University (1995)

    Google Scholar 

  8. Eggenberger, P.: Evolving morphologies of simulated 3D organisms based on differential gene expression. In: Proc. Of European Conf. on Artificial Life, pp. 205–213 (1997)

    Google Scholar 

  9. Fleischer, K., Barr, A.H.: A simulation testbed for the study of multicellular development: The multiple mechanisms of morphogenesis. In: Langton, C.G. (ed.) Proceedings of the 3rd Workshop on Artificial Life, pp. 389–416. Addison-Wesley, Reading (1992)

    Google Scholar 

  10. Furusawa, C., Kaneko, K.: Emergence of Multicellular Organisms with Dynamic Differentiation and Spatial Pattern. In: Adami, C., et al. (eds.) Proceedings of the 6th International Conference on Artificial Life. MIT Press, Cambridge (1998)

    Google Scholar 

  11. Gruau, F.: Neural Network Synthesis using Cellular Encoding and the Genetic Algorithm, PhD thesis, Ecole Normale Supérieure de Lyon (1994)

    Google Scholar 

  12. Gruau, F., Whitley, D., Pyeatt, L.: A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks. In: Proc. of the 1st Annual Conference on Genetic Programming, Stanford (1996)

    Google Scholar 

  13. Hogeweg, P.: Evolving Mechanisms of Morphogenesis: on the Interplay between Differential Adhesion and Cell Differentiation. J. Theor. Biol. 203, 317–333 (2000)

    Article  Google Scholar 

  14. Hogeweg, P.: Shapes in the Shadow: Evolutionary Dynamics of Morphogenesis. Artificial Life 6, 85–101 (2000)

    Article  Google Scholar 

  15. Hornby, G.S., Pollack, J.B.: The Advantages of Generative Grammatical Encodings for Physical Design. In: Proceedings of the Congress on Evolutionary Computation, pp. 600–607. IEEE Press, Los Alamitos (2001)

    Google Scholar 

  16. Jacobi, N.: Harnessing Morphogenesis, Cognitive Science Research Paper 423, COGS. University of Sussex (1995)

    Google Scholar 

  17. Kitano, H.: Designing neural networks using genetic algorithms with graph generation system. Complex Systems 4, 461–476 (1990)

    MATH  Google Scholar 

  18. Kodjabachian, J., Meyer, J.-A.: Evolution and Development of Neural Controllersfor Locomotion, radient-Following and Obstacle-Avoidance in Artificial Insects. IEEE Transactions on Neural Networks 9, 796–812 (1998)

    Article  Google Scholar 

  19. Koza, J.R.: Genetic Programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992); Genetic Programming II: Automatic Discovery of Reusable Subprograms. MIT Press (1994)

    MATH  Google Scholar 

  20. Lindenmeyer, A.: Mathematical models for cellular interaction in development, parts I and II. Journal of Theoretical Biology 18, 280–315 (1968)

    Article  Google Scholar 

  21. Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  22. Nolfi, S., Parisi, D.: Growing neural networks, Technical report PCIA-91-15, Institute of Psychology, CNR, Rome (1991)

    Google Scholar 

  23. Siddiqi, A.A., Lucas, S.M.: A comparison of matrix rewriting versus direct encoding for evolving neural networks. In: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, pp. 392–397. IEEE Press, Los Alamitos (1998)

    Google Scholar 

  24. Sims, K.: Evolving 3D morphology and behaviour by competition, In: Proceedings of Artificial Life IV, pp. 28–39 (1994)

    Google Scholar 

  25. Vassilev, V.K., Miller, J.F.: The advantages of landscape neutrality in digital circuit evolution. In: Miller, J.F., Thompson, A., Thompson, P., Fogarty, T.C. (eds.) ICES 2000. LNCS, vol. 1801, pp. 252–263. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  26. Wolpert, L.: Principles of Development. Oxford University Press, Oxford (1998)

    Google Scholar 

  27. Yu, T., Miller, J.: Neutrality and the evolvability of Boolean function landscape. In: Proceedings of the 4th European Conference on Genetic Programming, pp. 204–217. Springer, Heidelberg (2001)

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Miller, J.F. (2003). Evolving Developmental Programs for Adaptation, Morphogenesis, and Self-Repair. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds) Advances in Artificial Life. ECAL 2003. Lecture Notes in Computer Science(), vol 2801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_28

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  • DOI: https://doi.org/10.1007/978-3-540-39432-7_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20057-4

  • Online ISBN: 978-3-540-39432-7

  • eBook Packages: Springer Book Archive

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