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Challenges of evolvable systems: Analysis and future directions

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1259))

Abstract

The goal of research in evolutionary systems is to establish technologies for building highly complex functional systems using evolutionary apporachs. Ideally, such a system should exhibit a certain level of ’intelligence.’ Evolvable hardware research is an effort to accomplish direct hardware implementation of such a system. In this paper, we analyze fundermental problems in current resaerch and provide perspectives for evolving intelligent systems.

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References

  1. Bak, P., Tang, C. and Wiesenfeld, K., “Self-Organized Criticality,” Physical Review A, Vol. 38, No. 1., 364–374, 1988.

    Google Scholar 

  2. Brooks, R., “Intelligence without Reason,” Proc. of IJCAI-91, 1991.

    Google Scholar 

  3. Brooks, R. (1986). “A Robust Layered Control System For A Mobile Robot,” IEEE Journal of Robotics and Automation, Vol. RA-2, no. 1, 1986.

    Google Scholar 

  4. Chiba, A., Snow, P., Keshishian, H., and Hotta, Y., “Fasciclin III as a synaptic target recognition molecular in Drosophila,” Nature, 374, 166–168, 1995.

    Google Scholar 

  5. Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M. and Reitboeck, H., “Coherent oscillations: A mechanism of feature linking in the visual cortex?”, Biological Cybernetics, 60, 121–130, 1988.

    Google Scholar 

  6. Gray, C., Koenig, P., Engel, A. and Singer, W., “Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties,” Nature, 338, 334–337, 1989.

    Google Scholar 

  7. Gruau, F., and Whitley, D., “Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect,” Evolutionary Computation, 1(3): 213–233, 1993.

    Google Scholar 

  8. Hao, B., “Symbolic dynamics and characterization of complexity,” Non-linear Science, The MIT Press, 1991.

    Google Scholar 

  9. Hawkins, J. and Gell-Mann, M., The Evolution of Human Languages, Addison-Wesley, 1992.

    Google Scholar 

  10. Hemmi, H., Mizoguchi, J., Shimohara, K., “Development and evolution of hardware behaviors”, Proc. of Artificial Life IV, MIT Press, 1994.

    Google Scholar 

  11. Higuchi, T., Iba, H., Manderick, B., “Evolvable Hardware”, in Massively Parallel Artificial Intelligence, (ed. H. Kitano), MIT Press, 1994.

    Google Scholar 

  12. Hillis, D., “Co-evolving parasite improve simulated evolution as an optimization procedure,” Emergent Computation, The MIT Press, 1991.

    Google Scholar 

  13. Hosoya, T., Takizawa, K., Nitta, K., and Hotta, Y., “glial cells missing: A Binary Switch between Neuronal and Glial Determination in Drosophila,” Cell, 82, 1025–1036, 1995.

    Google Scholar 

  14. Ikegami, T. and Kaneko, K., “Computer symbiosis — Emergence of symbiotic behavior through evolution,” Emergent Computation, The MIT Press, 1991.

    Google Scholar 

  15. Kitano, H., “Morphogenesis for Complex Systems”, Toward Evolvable Hardware, Springer-Verlag, 1996.

    Google Scholar 

  16. Kitano, H (1995). “Hormonal Modulation of Learning,” Proc. of IJCAI-95, Montreal, 1995.

    Google Scholar 

  17. Kitano, H., “A Simple Model of Neurogenesis and Cell Differentiation Based on Evolutionary Large-Scale Chaos”, Artificial Life, 2: 79–99, 1995.

    Google Scholar 

  18. Kitano, H., “Evolution of Metabolism for Morphgenesis”, Proc. of Artificial Life IV, 1994.

    Google Scholar 

  19. Kitano, H., “Toward Adaptive Intelligent Systems”, Proc. of IEEE International Conference on Evolutionary Computation, Orland, 1994.

    Google Scholar 

  20. Kitano, H (1993). “Challenges of Massive Parallelism,” Proc. of IJCAI-93. (The Computers and Thought Award Lecture)

    Google Scholar 

  21. Kitano, H., “Designing Neural Networks using Genetic Algorithms with Graph Generation System,” Complex Systems, Vol. 4, No. 4, 1990.

    Google Scholar 

  22. Kitano, H. and Imai, S., “Two distinct instrinstic mechanism regulate the stochastic and catastriphic phases in cellular senescence,” manuscript, 1996.

    Google Scholar 

  23. Knoblich, J., Jan, L., and Jan, Y., “Asymmetric segregation of Numb and Prospero during cell division,” Nature, Vol. 377, 624–626, 1995.

    Google Scholar 

  24. Kondo, S., “A reaction-diffusion wave on the skin of the marine angelfish Pomacanthus,” Nature, Vol. 376, No. 6543, pp. 765–768, 1995.

    Google Scholar 

  25. Kondo, S., “A mechanistic model for morphogenesis and regeneration of limbs and imaginal discs,” Mechanisms of Development, 39, 161–170, 1992.

    Google Scholar 

  26. Konishi, M., Trends in Neurosci., 9, 163–168, 1986.

    Google Scholar 

  27. Langton, C. (1989). “Artificial Life,” Artificial Life, Addison Wesley.

    Google Scholar 

  28. McGaugh, J., “Involvement of hormonal and neuromodulatory systems in the regulation of memory storage,” Ann. Rev. Neurosci. 12:255–87, 1989.

    Google Scholar 

  29. Newell, A. and Simon, H., “Computer science as empirical inquiry: Symbols and search,” Communications of the ACM, 19(3), 113–126, 1976.

    Google Scholar 

  30. Pollack, J., “The Induction of Dynamical Recognizers,” Machine Learning, 7, 227–252, 1991.

    Google Scholar 

  31. Rolls, E., “The representation and storage of information in neuronal networks in the primate cerebral cortex and hippocampus,” Durbin, et al. (Eds.), The Computing Neuron, Addison-Wesley, 1989.

    Google Scholar 

  32. Rhyu, M., Jan, L., and Jan, Y., “Asymmetric Distribution of Numb Protein during Division of the Sensory Organ Precursor Cell Confers Distinct Fates to Daughter Cells,” Cell, Vol. 76, 477–491, 1994.

    Google Scholar 

  33. Sims, K., “Evolving Virtual Creatures,” Proc. of SIGGRAPH-94, 1994.

    Google Scholar 

  34. Steels, L., “Self-Organizing Vocabularies,” Proc. of Alife-V, 1996.

    Google Scholar 

  35. Thompson, A., “Evolving electronic robot controllers that exploit hardware resources”, Proc. of the 3rd European Conf. on Artificial Life, 1995.

    Google Scholar 

  36. Tsuda, I., “Dynamic Link of Memory — Chaotic Memory Map in Nonequilibrium Neural Networks,” Neural Networks, Vol. 5, 313–326, 1992.

    Google Scholar 

  37. Wada, K., Doi, H., Tanaka, S., Wada, Y., and Furusawa, M., “A neo-Darwinian algorithm: Asymmetrical mutations due to semiconservative DNA-type replication promote evolution,” Proc. Natl. Acad. Sci. USA, Vol. 90, 11934–11938, 1993.

    Google Scholar 

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Tetsuya Higuchi Masaya Iwata Weixin Liu

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© 1997 Springer-Verlag Berlin Heidelberg

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Kitano, H. (1997). Challenges of evolvable systems: Analysis and future directions. In: Higuchi, T., Iwata, M., Liu, W. (eds) Evolvable Systems: From Biology to Hardware. ICES 1996. Lecture Notes in Computer Science, vol 1259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63173-9_42

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  • DOI: https://doi.org/10.1007/3-540-63173-9_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63173-6

  • Online ISBN: 978-3-540-69204-1

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