PlasmidPL: A Plasmid-Inspired Language for Genetic Programming

  • Lidia Yamamoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4971)


We present PlasmidPL, a plasmid-inspired programming language designed for Genetic Programming (GP), and based on a chemical metaphor. The basic data structures in PlasmidPL are circular virtual molecules or rings which may contain code and data. Rings may react with each other to perform computations on the rings themselves. A virtual chemical reactor stochastically chooses which reactions should occur and when. Code and data may be rewritten in the process, leading to a system that constantly modifies itself. In order to be closer to chemistry, PlasmidPL relies solely on the data and code stored in molecules.

After describing the language, we show some hand-written sample programs that implement initial program generation, mutation and crossover within self-modifying chemical programs. These programs are then used to solve a typical symbolic regression problem, as a feasibility study. Finally, we discuss future directions into specific application scenarios that can benefit from such a chemical model.


Genetic Programming Knapsack Problem Genetic Operator Code Fragment Symbolic Regression 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ziegler, J., Banzhaf, W.: Evolving Control Metabolisms for a Robot. Artificial Life 7(2), 171–190 (2001)CrossRefGoogle Scholar
  2. 2.
    Taylor, T., Ottery, P., Hallam, J.: An approach to time- and space-differentiated pattern formation in multi-robot systems. In: Proc. TAROS (2007)Google Scholar
  3. 3.
    Dressler, F., et al.: Efficient Operation in Sensor and Actor Networks Inspired by Cellular Signaling Cascades. In: Proc. Autonomics, Rome, Italy (2007)Google Scholar
  4. 4.
    Dittrich, P., Ziegler, J., Banzhaf, W.: Artificial Chemistries – A Review. Artificial Life 7(3), 225–275 (2001)CrossRefGoogle Scholar
  5. 5.
    Banzhaf, W., Lasarczyk, C.: Genetic Programming of an Algorithmic Chemistry. In: GPTP II, O., et al. (eds.), vol. 8, pp. 175–190. Kluwer/Springer (2004)Google Scholar
  6. 6.
    Dittrich, P., Banzhaf, W.: Self-Evolution in a Constructive Binary String System. Artificial Life 4(2), 203–220 (1998)CrossRefGoogle Scholar
  7. 7.
    Spector, L., Robinson, A.: Genetic Programming and Autoconstructive Evolution with the Push Programming Language. GPEM Journal 3(1), 7–40 (2002)zbMATHGoogle Scholar
  8. 8.
    Spector, L., Stoffel, K.: Ontogenetic programming. In: Proc. Genetic Programming 1st Annual Conf., Stanford University, CA, USA, pp. 394–399. MIT Press, Cambridge (1996)Google Scholar
  9. 9.
    Head, T., et al.: Computing with DNA by operating on plasmids. BioSystems 57, 87–93 (2000)CrossRefGoogle Scholar
  10. 10.
    Henkel et al., C.V.: DNA computing of solutions to knapsack problems. BioSystems 88(1-2), 156–162 (2007)CrossRefGoogle Scholar
  11. 11.
    Fontana, W., Buss, L.W.: The Arrival of the Fittest: Toward a Theory of Biological Organization. Bulletin of Mathematical Biology 56, 1–64 (1994)zbMATHGoogle Scholar
  12. 12.
    Sipper, M.: Fifty years of research on self-replication: an overview. Artificial Life 4(3), 237–257 (1998)CrossRefGoogle Scholar
  13. 13.
    Laing, R.: Automaton models of reproduction by self-inspection. Journal of Theoretical Biology 66, 437–456 (1977)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Ikegami, T.: Evolvability of Machines and Tapes. Artificial Life and Robotics 3(4), 242–245 (1999)CrossRefGoogle Scholar
  15. 15.
    Spector, L., Klein, J., Keijzer, M.: The Push3 execution stack and the evolution of control. In: Proc. GECCO 2005, Washington DC, USA, pp. 1689–1696 (2005)Google Scholar
  16. 16.
    Giavitto, J.-L., Michel, O.: MGS: a rule-based programming language for complex objects and collections. Electr. Notes in Theor. Computer Science 59 (2001)Google Scholar
  17. 17.
    Gillespie, D.T.: Exact Stochastic Simulation of Coupled Chemical Reactions. Journal of Physical Chemistry 81(25), 2340–2361 (1977)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Lidia Yamamoto
    • 1
  1. 1.Computer Science DepartmentUniversity of BaselBaselSwitzerland

Personalised recommendations