Adaptive mutation with fitness and allele distribution correlation for genetic algorithms

Proceedings of the ACM Symposium on Applied Computing

Volumn 2, Issue , 2006, Pages 940-944

  Yang, Shengxiang ^a   Uyar, Sima ^b  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

^b ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Fitness and allele distribution correlation; Gene based adaptive mutation; Genetic algorithms

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; GENETIC ALGORITHMS; PROBABILITY; PROBLEM SOLVING; STATISTICAL METHODS;

ALLELE DISTRIBUTION CORRELATION; DISTRIBUTION STATISTICS; GENE BASED ADAPTIVE MUTATION;

GENETIC ENGINEERING;

EID: 33751049895     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1141277.1141499     Document Type: Conference Paper

References (14)

1. T. Bäck. Self-adaptation in genetic algorithms. In Proc. of the 1st European Conf. on Artificial Life, pages 263-271, 1992.
2. T. Bäck. Mutation parameters. In T. Bäck, D. B. Fogel, and Z. Michalewicz (eds.), Handbook of Evolutionary Computation, E1.2.1-E1.2.7, 1997. Oxford University Press.
3. T. C. Fogarty. Varying the probability of mutation in the genetic algorithm. In Proc. of the 3rd Int. Conf. on Genetic Algorithms, pages 104-109, 1989.
4. D. A. Goldberg. The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Boston, MA: Kluwer Academic Publishers, 2002.
5. J. Hesser and R. Männer. Towards an optimal mutation probability in genetic algorithms. In Parallel Problem Solving from Nature 1, pages 23-32, 1991.
6. B. Julstrom. What have you done for me lately? adapting operator probabilities in a steady-state genetic algorithm. In Proc. of the 6th Int. Conf. on Genetic Algorithms, pages 81-87, 1995.
7. H. Mühlenbein. How genetic algorithms really work: I. mutation and hillclimbing. In Parallel Problem Solving from Nature 2, pages 15-29, 1992.
8. J. D. Schaffer, R. A. Caruana, L. J. Eshelman, and R. Das. A study of control parameters affecting online performance of genetic algorithms for function optimization. In Proc. of the 3rd Int. Conf. on Genetic Algorithms, pages 51-60, 1989.
9. J. E. Smith and T. C. Fogarty. Self-adaptation of mutation rates in a steady-state genetic algorithm. In Proc. of the 3rd IEEE Conf. on Evolutionary Computation, pages 318-323, 1996.
10. W. Spears. Crossover or mutation. In Foundations of Genetic Algorithms 2, 1992.
11. D. Thierens. Adaptive mutation control schemes in genetic algorithms. In Proc. of the 2002 Congress on Evolutionary Computation, 2002.
12. A. Tuson and P. Ross. Adapting operator settings in genetic algorithms. Evol. Comput., 6(2):161-184, 1998.
13. S. Uyar, S. Sariel, and G. Eryigit. A gene based adaptive mutation strategy for genetic algorithms. Proc. of the 2004 Genetic and Evolutionary Computation Conference, 2004.
14. S. Yang. Adaptive mutation using statistics mechanism for genetic algorithms. In F. Coenen, A. Preece and A. Macintosh (editors), Research and Development in Intelligent Systems XX, pages 19-32, 2003. London: Springer-Verlag.