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
Link To Document :
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