Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

A DNA-Based Genetic Algorithm Implementation for Graph Coloring Problem

  • Conference paper
Advances in Intelligent Computing (ICIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3645))

Included in the following conference series:

Abstract

This paper presents an implementation of Croitoru’s genetic algorithm for graph coloring problem, and some necessary modification and simplifying are made by using DNA operations. In this algorithm, each vertex and edge is encoded with a series of encodings incorporating position information, and the initial diverse candidate population is generated using POA. One crossover operator, two mutation operators, evaluation and selection operators are all implemented using basic operations on DNA. It is shown that the algorithm can be implemented with space complexity much decreased and time complexity O(mn2) to get a new generation, where n is the number of vertices and m is the number of edges. Moreover, borrowing ideas from the above implementation, an algorithm for Maximal Clique problem is also presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Johnson, D.S., Aragon, C.R., McGeoch, L.A., Schevon, C.: Optimization by simulated annealing: An experimental evaluation: Part II, graph coloring and number partitioning. Operations Research 39(3), 378–406 (1991)

    Article  MATH  Google Scholar 

  2. Croitoru, C., Luchian, H., Gheorghies, O., Apetrei, A.: A New Genetic Graph Coloring Heuristic. In: COLOR 2002, Ithaca, NY (2002)

    Google Scholar 

  3. Amos, M., Gibbons, A.: Error-resistant Implementation of DNA Computations. In: Proceedings of the Second Annual Meeting on DNA Based Computers, vol. 44, pp. 151–168 (1996)

    Google Scholar 

  4. Bach, E., Condon, A., Glaser, E., Tanguay, C.: DNA models and algorithms for NP-complete problems. In: Proceedings of 11th Conference on Computational Complexity, pp. 290–299. IEEE Computer Society Press, Los Alamitos (1996)

    Google Scholar 

  5. Adleman, L.: Molecular computation of solution to combinatorial problems. Science, 266, 1021–1024 (1994)

    Article  Google Scholar 

  6. Cai, W., Condon, A., Corn, R., Glaser, E., Fei, Z., Frutos, T., Guo, Z., Lagally, M., Liu, Q., Smith, L., Thiel, A.: The power of surface-based DNA computation. In: Proceedings of 1st International Conference on Computational Molecular Biology, pp. 67–74. ACM Press, New York (1997)

    Google Scholar 

  7. Díaz, S., Esteban, J.L., Ogihara, M.: A DNA-based random walk method for solving k-SAT. In: Condon, A., Rozenberg, G. (eds.) DNA 2000. LNCS, vol. 2054, pp. 209–220. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  8. Stemmer, W.P.C.: The evolution of molecular computation. Science 270, 1510–1510 (1995)

    Article  Google Scholar 

  9. Chen, J., Antipov, E., Lemieux, B., Cedeno, W., Wood, D.H.: DNA computing implementing genetic algorithms. In: Landweber, L.F., Winfree, E., Lipton, R., Freeland, S. (eds.) Evolution as Computation, pp. 39–49. Springer, New York (1999)

    Google Scholar 

  10. Rose, J., Takano, M., Suyama, A.: A PNA-mediated Whiplash PCR-based Program for In Vitro Protein Evolution. In: Hagiya, M., Ohuchi, A. (eds.) DNA 2002. LNCS, vol. 2568, pp. 47–60. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  11. Chen, K., Ramachandran, V.: A Space Efficient Randomized DNA Algorithm. In: Condon, A., Rozenberg, G. (eds.) DNA 2000. LNCS, vol. 2054, pp. 199–208. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  12. Ouyang, Q., Kaplan, P.D., Liu, S., Libechabe, A.: DNA Solution of the Maximal Clique Problem. Science 278, 446–449 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, X., Yin, J., Jwo, JS., Feng, Z., Dong, J. (2005). A DNA-Based Genetic Algorithm Implementation for Graph Coloring Problem. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_11

Download citation

  • DOI: https://doi.org/10.1007/11538356_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28227-3

  • Online ISBN: 978-3-540-31907-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics