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Using feedback in a regulatory network computational device

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Published:12 July 2011Publication History

ABSTRACT

The relationship between the genotype and the phenotype in Evolutionary Algorithms (EA) is a recurrent issue among researchers. Based on our current understanding of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, some researchers start exploring computationally this new insight, including those mechanism in the EA. The Artificial Gene Regulatory (ARN) model, proposed by Wolfgang Banzhaf was one of the first tentatives. Following his seminal work some variants were proposed with increased capabilities. In this paper, we present another modification of this model, consisting in the use the regulatory network as a computational device where feedback edges are used. Using two classical benchmarks, the n-bit parity and the Fibonacci sequence problems, we show experimentally the effectiveness of the proposal.

References

  1. W. Banzhaf. Artificial regulatory networks and genetic programming. Genetic Programming Theory and Practice, pages 43--62, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  2. J. Bongard. Evolving modular genetic regulatory networks. In IEEE 2002 Congress on Evolutionary Computation (CEC2002), pages 1872--1877. IEEE Press, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. E. H. Davidson. The regulatory genome: gene regulatory networks in development and evolution. Academic Press, 2006.Google ScholarGoogle Scholar
  4. P. Dwight Kuo, W. Banzhaf, and A. Leier. Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. Bio Systems, 85(3):177--200, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  5. P. Eggenberger. Evolving morphologies of simulated 3D organisms based on differential gene expression. In P. Husbands and I. Harvey, editors, Fourth European Conference of Artificial Life. MIT Press, 1997.Google ScholarGoogle Scholar
  6. A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Springer Verlag, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. C. Ferreira. Genetic representation and genetic neutrality in gene expression programming. Advances in Complex Systems, 5(4):389--408, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  8. S. Harding, J. Miller, and W. Banzhaf. Self modifying cartesian genetic programming: Fibonacci, squares, regression and summing. Genetic Programming, pages 133--144, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Harding, J. F. Miller, and W. Banzhaf. Developments in Cartesian Genetic Programming: self-modifying CGP. Genetic Programming and Evolvable Machines, 11(3--4):397--439, June 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs (Complex Adaptive Systems). The MIT Press, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. P. Kuo, A. Leier, and W. Banzhaf. Evolving dynamics in an artificial regulatory network model. Lecture Notes in Computer Science, pages 571--580, 2004.Google ScholarGoogle Scholar
  13. T. Kuyucu, M. A. Trefzer, J. F. Miller, and A. M. Tyrrell. A scalable solution to n-bit parity via artificial development. Research in Microelectronics and Electronics, pages 144--147, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  14. R. L. Lopes and E. Costa. ReNCoDe : A Regulatory Network Computational Device. In S. Silva and J. Foster, editors, EuroGP2011, Lecture Notes in Computer Science, vol 6621, volume 6621, pages 142---153, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M. Nicolau and M. Schoenauer. Evolving specific network statistical properties using a gene regulatory network model. In G. and others Raidl, editor, GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 723--730, Montreal, 2009. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. M. Nicolau, M. Schoenauer, and W. Banzhaf. Evolving genes to balance a pole. European Conference on Genetic Programming, 6021:196--207, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Roggen, D. Federici, and D. Floreano. Evolutionary morphogenesis for multi-cellular systems. Genetic Programming and Evolvable Machines, 8(1):61--96, Dec. 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. a. Teichmann and M. M. Babu. Gene regulatory network growth by duplication. Nature genetics, 36(5):492--6, May 2004.Google ScholarGoogle ScholarCross RefCross Ref
  19. M. L. Wong. Evolving recursive programs by using adaptive grammar based genetic programming. Genetic Programming and Evolvable Machines, 7(1):127, Mar. 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. L. Wong and K. S. Leung. Evolving recursive functions for the even-parity problem using genetic programming, pages 221--240. MIT Press, Cambridge, MA, USA, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image ACM Conferences
        GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
        July 2011
        2140 pages
        ISBN:9781450305570
        DOI:10.1145/2001576

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        • Published: 12 July 2011

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