Analysis of selection algorithms: A markov chain approach

Evolutionary Computation

Volumn 4, Issue 2, 1996, Pages 133-167

  Chakraborty, Uday Kumar ^a,c   Chakraborty, Mandira ^a   Deb, Kalyanmoy ^b  

^a JADAVPUR UNIVERSITY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

^c NONE   (India)

Author keywords

Evolution strategies; Genetic algorithms; Markov chain; Selection; Takeover

Indexed keywords

EID: 0041035961     PISSN: 10636560     EISSN: None     Source Type: Journal    
DOI: 10.1162/evco.1996.4.2.133     Document Type: Article

References (23)

1. Asoh, H., & Mühlenbein, H. (1994). On the mean convergence time of evolutionary algorithms without selection and mutation. In Y. Davidor, H.-P. Schwefel, & R. Männer (Eds.), Parallel problem solving from nature 3 (pp. 88-97). Berlin: Springer-Verlag.
2. Bäck, T. (1994). Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In Proceedings of the First IEEE Conference on Evolutionary Computation, Vol. 1 (pp. 57-62). Piscataway, NJ: IEEE Press.
3. Bäck, T., & Hoffmeister, F. (1991). Extended selection mechanisms in genetic algorithms. In R. K. Belew & L. B. Booker (Eds.), Proceedings of the Fourth International Conference on Genetic Algorithms (pp. 92-99). San Mateo, CA: Morgan Kaufmann.
4. Bäck, T., & Schwefel, H.-P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1, 1-23.
5. Baker, J. E. (1985). Adaptive selection methods for genetic algorithms. In J. J. Grefenstette (Ed.), Proceedings of the First International Conference on Genetic Algorithms (pp. 101-111). Hillsdale, NJ: Lawrence Erlbaum.
6. Baker, J. E. (1989). Analysis of the effects of selection in genetic algorithms. Unpublished doctoral dissertation. Vanderbilt University, Nashville, Tennessee.
7. Bhattacharyya, G. K., & Johnson, R. A. (1977). Statistical concepts and methods. New York: Wiley.
8. Brindle, A. (1981). Genetic algorithms for function optimization. Unpublished doctoral dissertation. University of Alberta, Edmonton, Canada.
9. Chakraborty, U. K. (1995a). A simpler derivation of schema hazard in genetic algorithms. Information Processing Letters 56, 77-78.
10. Chakraborty, U. K. (1995b). A branching process model for genetic algorithms. Information Processing Letters 56, 281-292.
11. Chakraborty, U.K. (1995c). An analysis of selection in generational and steady-state genetic algorithms: A Markov chain approach. In Proceedings of the National Conference on Molecular Electronics, NERIST (A.P.), India.
12. Chakraborty, U. K., & Dastidar, D. G. (1993). Using reliability analysis to estimate the number of generations to convergence in genetic algorithms. Information Processing Letters 46, 199-209.
13. Davis, T. E., & Principe, J. C. (1993). A Markov chain framework for the simple genetic algorithm. Evolutionary Computation 1, 269-288.
14. De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Dissertation Abstracts International 36 (10), 5140B. (University Microfilms No. 76-9381.)
15. Mühlenbein, H., & Schlierkamp-Voosen, D. (1994). The science of breeding and its application to the breeder genetic algorithm. Evolutionary Computation 1, 335-360.
16. Nix, A. E., & Vose, M. D. (1992). Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence 5, 79-88.
17. Schwefel, H.-P. (1981). Numerical optimization of computer models. Chichester: Wiley.
18. Suzuki, J. (1993). A Markov chain analysis on a genetic algorithm. In S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms (pp. 146-153). San Mateo, CA: Morgan Kaufmann.
19. Syswerda, G. (1989). Uniform crossover in genetic algorithms. In J. D. Schaffer (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (pp. 2-9). San Mateo, CA: Morgan Kaufmann.
20. Syswerda, G. (1991). A study of reproduction in generational and steady state genetic algorithms. In G. J. E. Rawlins (Ed.), Foundations of genetic algorithms 1 (pp. 94-101). San Mateo, CA: Morgan Kaufmann.
21. Vose, M. D. (1993). Modeling simple genetic algorithms. In L. D. Whitley (Ed.), Foundations of genetic algorithms 2 (pp. 63-74). San Mateo, CA: Morgan Kaufmann.
22. Whitley, L. D. (1989). The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In J. D. Schaffer (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (pp. 116-121). San Mateo, CA:Morgan Kaufmann.
23. Whitley, L. D., & Kauth, J. (1988). GENITOR: A different genetic algorithm. In Proceedings of the Rocky Mountain Conference on Artificial Intelligence (pp. 118-130).