A Markov Chain Analysis on Simple Genetic Algorithms

IEEE Transactions on Systems, Man, and Cybernetics

Volumn 25, Issue 4, 1995, Pages 655-659

  Suzuki, Joe ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; EIGENVALUES AND EIGENFUNCTIONS; FUNCTIONS; MARKOV PROCESSES; MATRIX ALGEBRA; NUMERICAL ANALYSIS; OPTIMIZATION; PROBABILITY;

CONVERGENCE RATE; MARKOV CHAIN ANALYSIS; TRANSITION MATRIX;

GENETIC ALGORITHMS;

EID: 0029287775     PISSN: 00189472     EISSN: 21682909     Source Type: Journal    
DOI: 10.1109/21.370197     Document Type: Article

References (17)

1. J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press, 1975.
2. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
3. S. Kirkpatric, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, May 1983.
4. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, no. 6, pp. 721–741, Nov. 1984.
5. A. D. Bethke, Genetic algorithms as function optimizers, Ph.D. thesis, University of Michigan, 1980.
6. D. E. Goldberg, “Genetic algorithms and walsh functions: Part I, a gentle introduction, part II, deception and its analysis,” Complex Systems, vol. 3, pp. 129–171, 1989.
7. T. Bäck, “Optimal mutation rates in genetic algorithms,” in Stephanie Forrest, ed., International Conference on Genetic Algorithms '93, pp. 2–8, Morgan Kauffman Publishers, July 1993.
8. H. Mühlenbein, “How genetic algorithms really work I: Mutation and hillclimbing,” in Reinhard Männer and Bernard Manderick, eds., Parallel Problem Solving from Nature, 2 pp. 15–25, Elsevier Science Publishers, Amsterdam, 1992.
9. K. A. De Jong, An Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D. thesis, University of Michigan, Ann Arbor, Mich., 1975.
10. T. E. Davis, Toward an Extrapolation of the Simulated Annealing Convergence Theory onto the Simple Genetic Algorithm, Ph.D. thesis, University of Florida, 1991.
11. M. D. Vose and G. E. Liepins, “Punctuated equilibria in genetic search,” Complex Systems, vol. 5, pp. 31–44, 1991.
12. A. Nix and M. D. Vose, “Modeling genetic algorithm with Markov chains,” Annals of Mathematics and Artificial Intelligence, vol. 5, pp. 27–34, 1992.
13. M. D. Vose, “Modeling of genetic algorithms,” in L. Darrell Whitley, ed., Foundations of Genetic Algorithms 2, pp. 63–73, Aug. 1992.
14. D. B. Fogel, Evolving Artificial Intelligence, Ph.D. thesis, University of California, San Diego, 1992.
15. G. Rudolph, “Convergence analysis of canonical genetic algorithms,” IEEE Trans. Neural Networks, special issue on Evolutional Computing, vol. 5, no. 1, pp. 96–101, Jan. 1994.
16. L. B. Booker, “Classifier systems and genetic algorithms,” Artificial Intelligence, vol. 40, pp. 235–282, 1989.
17. M. Iosifescu, Finite Markov Processes and Their Applications, Wiley, Chichester, 1980.