A Critical View of the Evolutionary Design of Self-assembling Systems

  • Natalio Krasnogor
  • Graciela Terrazas
  • David A. Pelta
  • Gabriela Ochoa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)


The automated design of systems which self-assemble is a fundamental cornerstone of nanotechnology. In this paper we review some work in which we have applied Evolutionary Algorithms (EAs) for the automated design of systems self-assembly. We will focus in three important minimalist self-assembly problems and we discuss the difficulties encountered while applying EAs to these test cases. We also suggest some promising lines of work that could possibly help overcome current limitations in the evolutionary design of self-assembling systems.


Evolutionary Algorithm Cellular Automaton Cellular Automaton Automate Design Protein Structure Prediction 
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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  1. 1.
    Adleman, L., Cheng, Q., Goel, A., Huang, M., Kempe, D., Espanes, P.M.d., Rothemund, P.W.K.: Combinatorial optimization problems in self-assembly. In: Proceedings of the Annual ACM Symposium on Theory of Computing (STOC), ACM Press, New York (2002)Google Scholar
  2. 2.
    Bedau, M.A., Packard, N.H.: Measurement of evolutionary activity, teleology and life. In: Langton, C.G., Taylor, C., Farmer, D., Rasmussen, S. (eds.) Artificial Life II, vol. 98-03-023, pp. 431–461. Addison-Wesley, Reading (1992)Google Scholar
  3. 3.
    Berger, B., Leight, T.: Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. In: Proceedings of The Second Annual International Conference on Computational Molecular Biology, RECOMB 1998, pp. 30–39. ACM Press, New York (1998)CrossRefGoogle Scholar
  4. 4.
    Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24, 1501 (1985)CrossRefGoogle Scholar
  5. 5.
    Escuela, G., Ochoa, G., Krasnogor, N.: Evolving l-systems to capture protein structure native conformations. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J.I., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Glover, F., Taillard, E., de Werra, D.: A user’s guide to tabu search. Annals of Operations Research 41, 3–28 (1993)CrossRefzbMATHGoogle Scholar
  7. 7.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Klavins, E.: Automatic synthesis of controllers for distributed assembly and formation forming. In: Proceedings of the IEEE Conference on Robotics and Automation (2002)Google Scholar
  9. 9.
    Krasnogor, N., Gustafson, S.: A family of conceptual problems in the automated design of self-assembly. In: Proceedings of the 2nd International Conference on the Fundations of Nanoscience: Self-Assembled Architecture and Devices, Utah, Snowbird resort, April 24-29 (2005)Google Scholar
  10. 10.
    Krasnogor, N., Pelta, D.A., Marcos, D.H., Risi, W.A.: Protein structure prediction as a complex adaptive system. In: Proceedings of Frontiers in Evolutionary Algorithms 1998 (1998)Google Scholar
  11. 11.
    Collet, P., Lutton, E., Raynal, F., Schoenauer, M.: Polar ifs + parisian genetic programming = efficient ifs inverse problem solving. Genetic Programming and Evolvable Machines 1, 339–361 (2000)CrossRefzbMATHGoogle Scholar
  12. 12.
  13. 13.
    Rothemund, P., Winfree, E.: The program-size complexity of self-assembled squares. In: Proceedings of STOC (2000)Google Scholar
  14. 14.
    Wang, H.: Probing theorems by pattern recognition. Bell Systems Technical Journal 40, 1–42 (1961)CrossRefGoogle Scholar
  15. 15.
    Krasnogor, N., Hart, W.E., Smith, J.E.: Recent Advances in Memetic Algorithms. In: Studies in Fuzziness and Soft Computing Series, Springer, Heidelberg (2004)Google Scholar
  16. 16.
    Whiteside, G.M., Boncheva, M.: Beyond molecules: Self-assembly of mesoscopic and macroscopic components. Proceedings of the National Academy of Science (PNAS) 99(8), 4769–4774 (2002)CrossRefGoogle Scholar
  17. 17.
    Whiteside, G.M., Grzybowski, B.: Self-assembly at all scales. Science 295, 2418–2421 (2002)CrossRefGoogle Scholar
  18. 18.
    Wolfram, S.: A New Kind of Science. Wolfram Media Inc. (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Natalio Krasnogor
    • 1
  • Graciela Terrazas
    • 1
  • David A. Pelta
    • 2
  • Gabriela Ochoa
    • 3
  1. 1.Automated Scheduling, Optimisation and Planning Research Group, School of Computer Science and Information TechnologyUniversity of NottinghamUK
  2. 2.Departamento de Ciencias de la Computacion, ETSI InformaticaUniversidad de GranadaSpain
  3. 3.Departamento de Ciencias de la ComputacionUniversidad Simon BolivarVenezuela

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