• DocumentCode
    2325482
  • Title

    An evolutionary heuristic for the maximum independent set problem

  • Author

    Bäck, Thomas ; Khuri, Sami

  • Author_Institution
    Dept. of Comput. Sci., Dortmund Univ., Germany
  • fYear
    1994
  • fDate
    27-29 Jun 1994
  • Firstpage
    531
  • Abstract
    The results obtained from the application of a genetic algorithm, GENEsYs, to the NP-complete maximum independent set problem are reported. In contrast to many other genetic algorithm-based approaches that use domain-specific knowledge, the approach presented in this paper relies on a graded penalty term component of the fitness function to penalize infeasible solutions. The method is applied to several large problem instances of the maximum independent set problem. The results clearly indicate that genetic algorithms can be successfully used as heuristics for finding good approximative solutions for this highly constrained optimization problem
  • Keywords
    computational complexity; genetic algorithms; heuristic programming; optimisation; set theory; GENEsYs; NP-complete problem; approximative solutions; evolutionary heuristic; fitness function; genetic algorithm; graded penalty term component; highly constrained optimization problem; infeasible solutions penalization; maximum independent set problem; Algorithm design and analysis; Application software; Computer science; Encoding; Genetic algorithms; Genetic mutations; Mathematics; Polynomials; Probability; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1899-4
  • Type

    conf

  • DOI
    10.1109/ICEC.1994.350004
  • Filename
    350004