Self adaptation of operator rates in evolutionary algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3102, Issue , 2004, Pages 1162-1173

  Gomez, Jonatan ^a,b  

^a UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

^b UNIVERSITY OF MEMPHIS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS;

BENCHMARK FUNCTIONS; RANDOM LEARNING; SELF ADAPTATION;

EVOLUTIONARY ALGORITHMS;

EID: 35048823373     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24854-5_113     Document Type: Article

References (19)

1. J. H. Holland, Adaptation in Natural and Artificial Systems. The University of Michigan Press, 1975.
2. M. Mitchell, An intoduction to genetic algorithms. MIT Press, 1996.
3. A. E. Eiben, R. Hinterding, and Z. Michalewicz, "Parameter control in evolutionary algorithms," IEEE Transactions in Evolutionary Computation, vol. 3(2), pp. 124-141, 1999.
4. K. De Jong, An analysis of the Behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, 1975.
5. J. Schaffer, R. Caruana, L. Eshelman, and R. Das, "A study of control parameters affecting online performance of genetic algorithms for function optimization," in Third International Conference on Genetic Algorithms, pp. 51-60, 1989.
6. D. Goldberg and K. Deb, "A comparative analysis of selection schemes used in genetic algorithms," Foundations of Genetic Algorithms, no. 1, pp. 69-93, 1991.
7. T. Back and H. Schwefel, "An overview of evolutionary algorithms for parallel optimization," Evolutionary Computation, vol. 1, no. 1, pp. 1-23, 1993.
8. F. Lobo, The parameter-less genetic algorithm: rational and automated parameter selection for simplified genetic algorithm operation. PhD thesis, Nova University of Lisboa, 2000.
9. L. Davis, "Adapting operator probabilities in genetic algorithms," in Third International Conference on Genetic Algorithms and their Applications, pp. 61-69, 1989.
10. B. Julstrom, "What have you done for me lately? adapting operator probabilities in a steady-state genetic algorithm," in Sixth International Conference on Genetic Algorithms, pp. 81-87, 1995.
11. A. Tuson and P. Ross, "Adapting operator settings in genetic algorithms," Evolutionary Computation, 1998.
12. W. Spears, "Adapting crossover in evolutionary algorithms," in Evolutionary Programming Conference, 1995.
13. M. Srinivas and L. M. Patnaik, "Adaptive probabilities of crossover and mutation in genetic algorithms," Transactions on Systems, Man and Cybernetics, vol. 24(4), pp. 656-667, 1994.
14. T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, 1996.
15. D. Goldberg, B. Korb, and K. Deb, "Messy genetic algorithms: motivation, analysis, and first results," Complex Systems, vol. 3, pp. 493-530, 1989.
16. S. Forrest and M. Mitchell, "Relative building blocks fitness and the building block hypothesis," Foundations of Genetic Algorithms, no. 2, 1993.
17. A. Simoes and E. Costa, "Transposition: a biologically inspired mechanism to use with genetic algorithms," in Fourth International Conference on Neural Networks and Genetic Algorithms, pp. 612-619, 1999.
18. J. Digalakis and K. Margaritis, "An experimental study of benchmarking functions for genetic algorithms," in IEEE Conferences Transactions, Systems, Man and Cybernetics, vol. 5, pp. 3810-3815, 2000.
19. A. Patton, T. Dexter, E. Goodman, and W. Punch, "On the application of cohort-driven operators to continuous optimization problems using evolutionary computation," Evolutionary Programming, no. 98, 1998.