Experimental Comparison of Two Evolutionary Algorithms for the Independent Set Problem

  • Pavel A. Borisovsky
  • Marina S. Zavolovskaya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)


This work presents an experimental comparison of the steady-state genetic algorithm to the (1+1)-evolutionary algorithm applied to the maximum vertex independent set problem. The penalty approach is used for both algorithms and tuning of the penalty function is considered in the first part of the paper. In the second part we give some reasons why one could expect the competitive performance of the (1+1)-EA. The results of computational experiment are presented.


Genetic Algorithm Evolutionary Algorithm Penalty Function Infeasible Solution Competitive Performance 
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.
    Motwani, R., Raghavan, P.: Randomized algorithms. Cambridge University Press (1995)Google Scholar
  2. 2.
    Schöning, U.: A probabilistic algorithm for k-SAT and constraint satisfaction problems. Proc. 40th IEEE Symposium on Foundations of Computer Science (1999)410–414Google Scholar
  3. 3.
    Borisovsky, P.A., Eremeev, A.V.: A Study on Performance of the (1+1)-Evolutionary Algorithm. Proc. of Foundations of Genetic Algorithms-2002 (FOGA-2002) (2002) 367–383Google Scholar
  4. 4.
    Droste, S., Jansen, T., Tinnefeld K., Wegener, I.: A new framework for the valuation of algorithms for black-box optimisation. Proc. of Foundations of Genetic Algorithms-2002 (FOGA-2002) (2002) 197–214Google Scholar
  5. 5.
    Fogel, D. B., Ghozeil, A.: Using fitness distributions to design more efficient evolutionary computations. In Fukuda, T (ed.): Proc. Third IEEE International Conference on Evolutionary Computation, IEEE (1996) 11–19Google Scholar
  6. 6.
    Kallel, L., Schoenauer, M.: A priori comparison of binary crossover operators: No universal statistical measure, but a set of hints. Proc. Artificial Evolution’97, LNCS Num. 1363. Springer Verlag (1997) 287–299Google Scholar
  7. 7.
    Bäck, Th., Khuri, S.: An Evolutionary Heuristic for the Maximum Independent Set Problem. Proc. First IEEE Conference on Evolutionary Computation, IEEE (1994) 531–535Google Scholar
  8. 8.
    Balas, E., Niehaus, W.: Optimized Crossover-Based Genetic Algorithms for the Maximum Cardinality and Maximum Weight Clique Problems. Journ. of Heuristics, 4 (4) (1998) 107–122zbMATHCrossRefGoogle Scholar
  9. 9.
    Bomze, I. M., Budinich M., Pardalos P. M., Pelillo, M.: The maximum clique problem. In Du, D.-Z. and Pardalos, P. M. (eds): Handbook of Combinatorial Optimization, Vol. 4. Kluwer Academic Publishers (1999)Google Scholar
  10. 10.
    Goldberg, D. E., Deb, K.: A comparative study of selection schemes used in genetic algorithms. In G. Rawlins (ed.): Foundations of Genetic Algorithms(FOGA). Morgan Kaufmann (1991) 69–93Google Scholar
  11. 11.
    Syswerda, G.: A study of reproduction in generational and steady state genetic algorithm. In G. Rawlins (ed.): Foundations of Genetic Algorithms (FOGA). Morgan Kaufmann (1991) 94–101Google Scholar
  12. 13.
    Richardson, J.T., Palmer, M.R., Liepins, G., Hilliard M.: Some guidelines for genetic algorithms with penalty functions. In J. David Schaffer (ed.): Proc. of the 3rd International Conference on Genetic Algorithms. Morgan Kaufmann (1989) 191–197.Google Scholar
  13. 14.
    Bäck, Th., Schwefel H.-P.: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1 (1) (1993) 1–23CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Pavel A. Borisovsky
    • 1
  • Marina S. Zavolovskaya
    • 2
  1. 1.Omsk Branch of Sobolev Institute of MathematicsOmskRussia
  2. 2.Mathemetical DepartmentOmsk State UniversityOmskRussia

Personalised recommendations