Skip to main content
Springer Nature Link
Log in

Using Viruses to Improve GAs

  • Conference paper
Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

Included in the following conference series:

Abstract

In this paper, we will introduce an evolutionary algorithm for finding approximate solutions to the Weighted Minimum Hitting Set Problem. The proposed genetic algorithm, denoted by HEAT-V, makes use of a newly defined concept of virus. We will test its performance against a well known and efficient greedy algorithm, and on several families of sets.

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

Access this chapter

Log in via an institution

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. Bäck, T., Eiben, A., Vink, M.: A superior evolutionary algorithm for 3-SAT. In: Proceedings of the 7th Annual Conference on Evolutionary Programming, pp. 125–136 (1998)

    Google Scholar 

  2. Bellare, M., Goldwasser, S., Lund, G., Russel, A.: Efficient probabilistically checkable proofs and applications to approximations. In: Proceedings of the 25th Annual ACM Symposium on Theory of Computing, pp. 294–304 (1993)

    Google Scholar 

  3. Cutello, V., Mastriani, E., Pappalardo, F.: An evolutionary algorithm for the T-constrained variation of the Minimum Hitting Set Problem. In: Proceedings of 2002 IEEE Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 366–371 (2002)

    Google Scholar 

  4. Feige, U.: A threshold of logn for approximating set cover. Journal of ACM 45, 634–652 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  5. Galinier, P.: Hybrid Evolutionary Algorithms for Graph Coloring. Journal of Combinatorial Optimization 3(4), 379–397 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  6. Garey, M.R., Johnson, D.S.: Computers and Intractability: A guide to the theory of NP-completeness. W. H. Freeman and Company, New York (1979)

    MATH  Google Scholar 

  7. Johnson, D.S.: Approximation algorithms for combinatorial problems. J. Comput. System Sci. 1, 256–278 (1974)

    Article  Google Scholar 

  8. Khuri, S., Bäck, T.: An Evolutionary Heuristic for the Minimum Vertex Cover Problem. In: Proc. of the KI 1994 Workshop (1994)

    Google Scholar 

  9. Laguna, M., Moscato, P.: On Genetic Crossover Operators for Relative Order Preservation. In: Diaz, B.A. (ed.) C3P Report 778, Algoritmos Geneticos, ch. 3. Optimizacion Heuristica y Redes Neuronales, Paraninfo, Madrid, Espanya (1996)

    Google Scholar 

  10. Manner, R., Manderick, B.: Genetic Operators, the Fitness Landscape and the Traveling Salesman Problem. In: Parallel Problem Solving from Nature-PPSN 2, pp. 219–228 (1992)

    Google Scholar 

  11. Papadimitriou, C.H.: Computational Complexity. Addison Wesley, Reading (1994)

    MATH  Google Scholar 

  12. Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization, pp. 407–408. Prentice-Hall, Englewood Cliffs (1982)

    MATH  Google Scholar 

  13. Raz, R., Safra, S.: A sub-constant error-probability low-degree test, and sub-constant error-probability PCP characterization of NP. In: Proceedings of the 29th Ann. ACM Symp. on Theory of Computation, pp. 475–484 (1997)

    Google Scholar 

  14. Slavik, P.: A tight analysis of the greedy algorithm for set cover. In: Proceedings of 28th ACM Symposium on Theory of Comp., pp. 435–439 (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Pharmacy and Dept. of Mathematics & Computer Science, University of Catania, Italy

    Francesco Pappalardo

Authors
  1. Francesco Pappalardo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pappalardo, F. (2005). Using Viruses to Improve GAs. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_19

Download citation

  • DOI: https://doi.org/10.1007/11539902_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation