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ReNCoDe: A Regulatory Network Computational Device

  • Rui L. Lopes
  • Ernesto Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

In recent years, our biologic understanding was increased with the comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development, and some researchers advocate the need to explore computationally this new understanding. One of the outcomes was the Artificial Gene Regulatory (ARN) model, first proposed by Wolfgang Banzhaf. In this paper, we use this model as representation for a computational device and introduce new variation operators, showing experimentally that it is effective in solving a set of benchmark problems.

Keywords

Gene Regulatory Network Benchmark Problem Symbolic Regression Computational Device Regulatory Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Banzhaf, W.: Artificial Regulatory Networks and Genetic Programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practice, ch. 4, pp. 43–62. Kluwer, Dordrecht (2003)CrossRefGoogle Scholar
  2. 2.
    Davidson, E.H.: The regulatory genome: gene regulatory networks in development and evolution. Academic Press, London (2006)Google Scholar
  3. 3.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Field, A.P., Hole, G.: How to design and report experiments. Sage Publications Ltd., Thousand Oaks (2003)Google Scholar
  5. 5.
    Koza, J., Keane, M.: Genetic breeding of non-linear optimal control strategies for broom balancing. Analysis and Optimization of Systes 144, 47–56 (1990)CrossRefzbMATHGoogle Scholar
  6. 6.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs (Complex Adaptive Systems). MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  7. 7.
    Kuo, P., et al.: Evolving dynamics in an artificial regulatory network model. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 571–580. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Kuo, P.D., et al.: Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. Biosystems 85(3), 177–200 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Langdon, W.: Why ants are hard. Cognitive Science Research Papers, 193–201 (1998)Google Scholar
  10. 10.
    Nicolau, M., Schoenauer, M.: Evolving specific network statistical properties using a gene regulatory network model. In: Raidl, G., et al. (eds.) GECCO 2009: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 723–730. ACM, Montreal (2009)Google Scholar
  11. 11.
    Nicolau, M., et al.: Evolving Genes to Balance a Pole. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 196–207. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Whitley, D., et al.: Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 919–926. ACM, New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rui L. Lopes
    • 1
  • Ernesto Costa
    • 1
  1. 1.Centro de Informática e Sistemas da Universidade de CoimbraCoimbraPortugal

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