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Genetic algorithm in DNA computing: A solution to the maximal clique problem

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Chinese Science Bulletin

Abstract

Genetic algorithm is one of the possible ways to break the limit of brute-force method in DNA computing. Using the idea of Darwinian evolution, we introduce a genetic DNA computing algorithm to solve the maximal clique problem. All the operations in the algorithm are accessible with today’s molecular biotechnology. Our computer simulations show that with this new computing algorithm, it is possible to get a solution from a very small initial data pool, avoiding enumerating all candidate solutions. For randomly generated problems, genetic algorithm can give correct solution within a few cycles at high probability. Although the current speed of a DNA computer is slow compared with silicon computers, our simulation indicates that the number of cycles needed in this genetic algorithm is approximately a linear function of the number of vertices in the network. This may make DNA computers more powerfully attacking some hard computational problems.

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References

  1. Ruben, A. J., Landweber, L. F., The past, present and future of molecular computing, Nature Reviews Molecular Cell Biology, 2000, 1: 69–72.

    Article  Google Scholar 

  2. Adleman, L., Molecular computation of solutions to combinatorial problems, Science, 1994, 266: 1021–1024.

    Article  Google Scholar 

  3. Ouyang, Q., Kaplan, P. D., Liu S. et al., DNA solution of the maximal clique problem, Science, 1997, 278: 446–449.

    Article  Google Scholar 

  4. Braich, R. S., Chelyapov, N., Johnson, C. et al., Solution of a 20-variable 3-SAT problem on a DNA computer, Science, 2002, 296: 499–502.

    Article  Google Scholar 

  5. Faulhammer, D., Cukras, A. R., Lipton, R. J. et al., Molecular computation: RNA solutions to chess problems, Proc. Natl. Acad. Sci., 2000, U.S.A. 97: 1385–1389.

    Article  Google Scholar 

  6. Benenson, Y., Paz-Elizur, T., Adar, R. et al., Programmable and autonomous computing machine made of biomolecules, Nature, 2001, 414: 430–434.

    Article  Google Scholar 

  7. Liu, Q., Wang, L., Frutos, A. G. et al., DNA computing on surfaces, Nature, 2000, 403: 175–179.

    Article  Google Scholar 

  8. Ogihara, M., Ray, A., DNA computing on a chip, Nature, 2000, 403: 143–144.

    Article  Google Scholar 

  9. Sakamoto, K., Gouzu, H., Komiya, K. et al., Molecular computation by DNA hairpin formation, Science, 2000, 288: 1223–1226.

    Article  Google Scholar 

  10. Wang, L., Hall, J. G., Lu, M. et al., A DNA computing readout operation based on structure-specific cleavage, Nat. Biotechnol., 2001, 19: 1053–1059.

    Article  Google Scholar 

  11. Zimmermann, K. -H., On applying molecular computation to binary linear codes, IEEE Trans. Inform. Theory, 2002, 48: 505–510.

    Article  Google Scholar 

  12. Impagliazzo, R., Paturi, R., Zane, F., Which problems have strongly exponential complexity? J. Comput. Syst. Sci., 2001, 23: 512–530 (doi: 10.1006/jcss.2001.1774).

    Article  Google Scholar 

  13. Holland, J. H., Genetic algorithm, Scientific American, 1992, 267(1): 66–72.

    Article  Google Scholar 

  14. Foster, J. A., Evolutionary computation, Nat. Rev. Genet., 2001, 2: 428–436.

    Article  Google Scholar 

  15. Chiu, D. T., Pezzoli, E., Wu, H. et al., Using three-dimensional microfluidic networks for solving computationally hard problems, Proc. Natl. Acad. Sci. U.S.A., 2001, 98: 2961–2966.

    Article  Google Scholar 

  16. Kaplan, P. D., Ouyang, Q., Thaler, D. S. et al., Parallel overlap assembly for the construction of computational DNA libraries, J. Theor. Biol., 1997, 188: 333–341 (doi: 10.1006/jtbi.l997.0475).

    Article  Google Scholar 

  17. Arita, M., Kobayashi, S., The power of sequence design in DNA computing, ICCIMA 2001: Proceedings of 4th International Conference on Computational Intelligence and Multimedia Applications, 163–167.

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Correspondence to Qi Ouyang.

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Li, Y., Fang, C. & Ouyang, Q. Genetic algorithm in DNA computing: A solution to the maximal clique problem. Chin. Sci. Bull. 49, 967–971 (2004). https://doi.org/10.1007/BF03184020

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  • DOI: https://doi.org/10.1007/BF03184020

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