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A Developmental Method for Growing Graphs and Circuits

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Evolvable Systems: From Biology to Hardware (ICES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2606))

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Abstract

A review is given of approaches to growing neural networks and electronic circuits. A new method for growing graphs and circuits using a developmental process is discussed. The method is inspired by the view that the cell is the basic unit of biology. Programs that construct circuits are evolved to build a sequence of digital circuits at user specified iterations. The programs can be run for an arbitrary number of iterations so circuits of huge size could be created that could not be evolved. It is shown that the circuit building programs are capable of correctly predicting the next circuit in a sequence of larger even parity functions. The new method however finds building specific circuits more difficult than a non-developmental method.

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References

  1. Astor J. C., and Adami C. (2000), “A Development Model for the Evolution of Artificial Neural Networks”, Artificial Life, Vol. 6, pp. 189–218.

    Article  Google Scholar 

  2. Bentley P., and Kumar S. (1999), “Three ways to grow designs: A comparison of embryogenies for an Evolutionary Design Problem”, in Proceedings of the Congress on Evolutionary Computation, IEEE Press, pp. 35–43.

    Google Scholar 

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

    Google Scholar 

  4. Bongard J. C. and Pfeifer R. (2001), “Repeated Structure and Dissociation of Genotypic and Phenotypic Complexity in Artificial Ontogeny”, in Spector L. et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Morgan-Kaufmann, pp. 829–836.

    Google Scholar 

  5. Cangelosi, A., Parisi, D., and Nolfi, S. (1993), “Cell Division and Migration in a ‘Genotype’ for Neural Networks”, Technical report PCIA-93, Institute of Psychology, CNR, Rome.

    Google Scholar 

  6. Dellaert, F. (1995), “Toward a Biologically Defensible Model of Development”, Masters thesis, Department of Computer Engineering and Science, Case Western Reserve University.

    Google Scholar 

  7. Edwards R. T (2003), “Circuit Morphologies and Ontogenies”, in NASA/DOD Conference on Evolvable Hardware, IEEE Computer Society, pp. 251–260.

    Google Scholar 

  8. Eggenberger P. (1997), “Evolving morphologies of simulated 3D organisms based on differential gene expression”, in Proceedings of 4th European Conference on Artificial Life, pp. 205–213.

    Google Scholar 

  9. Gordon T., and Bentley P. J. (2003) “Towards Development in Evolvable Hardware”, in NASA/DOD Conference on Evolvable Hardware, IEEE Computer Society, pp. 241–250.

    Google Scholar 

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

    Google Scholar 

  11. Gruau, F., Whitley, D., and Pyeatt, L. (1996) “A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks”, in Proceedings of the 1st Annual Conference on Genetic Programming, Stanford.

    Google Scholar 

  12. Hemmi H., Mizoguchi, J., and Shimohara K. (1994), “Development and Evolution of Hardware Behaviors”, in Proceedings of Artificial Life IV, pp. 371–376.

    Google Scholar 

  13. Haddow, P. C., Tufte G., and van Remortel P. (2001) “Shrinking the genotype: L-systems for evolvable hardware”, in 4th International Conference on Evolvable Systems: From Biology to Hardware, Lecture Notes in Computer Science, Vol. 2210, Springer-Verlag, pp. 128–139.

    Google Scholar 

  14. Hornby G. S., and Pollack J. B. (2001), “The Advantages of Generative Grammatical Encodings for Physical Design”, in Proceedings of the Congress on Evolutionary Computation, IEEE Press, pp. 600–607.

    Google Scholar 

  15. Huelsbergen L. (1998), “Finding general Solutions to the Parity Problem bu Evolving Machine-Language Representations”, in Proc. Conf. on Genetic Programming, pp. 158–166.

    Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  18. Kodjabachian, J. and Meyer, J-A. (1998), “Evolution and Development of Neural Controllers for Locomotion, Gradient-Following and Obstacle-Avoidance in Artificial Insects”, IEEE Transactions on Neural Networks, Vol. 9, pp. 796–812.

    Article  Google Scholar 

  19. Koza, J., Bennett III, F. H., Andre, D., and Keane, M. A. (1999). Genetic Programming III. Darwinian Invention and Problem Solving. Morgan Kaufmann.

    Google Scholar 

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

    Article  Google Scholar 

  21. Miller, J. F. and Thomson, P. (2000), “Cartesian genetic programming”, in Proceedings of the 3rd European Conference on Genetic Programming. LNCS, Vol. 1802, pp.121–132.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  25. Vassilev V. K., and Miller J. F. (2000), “The Advantages of Landscape Neutrality in Digital Circuit Evolution”, 3rd International Conference on Evolvable Systems: From Biology to Hardware, Lecture Notes in Computer Science, Vol. 1801, Springer-Verlag, pp. 252–263.

    Google Scholar 

  26. Vassilev V. K. and Miller J. F., (2000), “Scalability Problems of Digital Circuit Evolution”, in Proceedings of the 2nd NASA/DOD Workshop on Evolvable Hardware, IEEE Computer Society, pp. 55–64

    Google Scholar 

  27. Wong M. L., and Leung K. S. (1996), “Evolving Recursive Functions for the Even-Parity Problem Using Genetic Programming”, in Advances in GP, MIT Press, Vol. 2, pp. 221–240.

    Google Scholar 

  28. Yu, T. (1999), “An analysis of the Impact of Functional Programming Techniques on Genetic Programming, Ph. D. Thesis, University College London, UK.

    Google Scholar 

  29. Yu, T. and Miller, J. (2001), “Neutrality and the evolvability of Boolean function landscape”, in Proceedings of the 4th European Conference on Genetic Programming, Springer-Verlag, pp. 204–217.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, The University of Birmingham, UK

    Julian F. Miller

  2. School of Computing, Napier University, UK

    Peter Thomson

Authors
  1. Julian F. Miller
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  2. Peter Thomson
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Editor information

Editors and Affiliations

  1. The Department of Electronics, The University of York, YO10 5DD, Heslington, York, United Kingdom

    AAndy M. Tyrrell

  2. Department of Computer and Information Science, The Norwegian University of Science and Technology, Sem Sælands Vei, 7491, Trondheim, Norway

    Pauline C. Haddow

  3. Department of Informatics, University of Oslo, P.O. Box 1080, 0316, Blindern, Oslo, Norway

    Jim Torresen

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© 2003 Springer-Verlag Berlin Heidelberg

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Miller, J.F., Thomson, P. (2003). A Developmental Method for Growing Graphs and Circuits. In: Tyrrell, A.M., Haddow, P.C., Torresen, J. (eds) Evolvable Systems: From Biology to Hardware. ICES 2003. Lecture Notes in Computer Science, vol 2606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36553-2_9

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  • DOI: https://doi.org/10.1007/3-540-36553-2_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00730-2

  • Online ISBN: 978-3-540-36553-2

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