Population sizing for entropy-based model building in discrete estimation of distribution algorithms

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 601-608

  Yu, Tian Li ^a   Sastry, Kumara ^b   Goldberg, David E ^b   Pelikan, Martin ^c  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^c UNIVERSITY OF MISSOURI ST LOUIS   (United States)

Author keywords

Entropy; Estimation of distribution algorithms; Genetic algorithms; Model building; Mutual information; Population sizing

Indexed keywords

DATA STRUCTURES; DISTRIBUTION FUNCTIONS; FINITE DIFFERENCE METHOD; MODEL CHECKING; PARAMETER ESTIMATION; POPULATION DYNAMICS;

ESTIMATION OF DISTRIBUTION ALGORITHMS; MODEL BUILDING; MUTUAL INFORMATION; POPULATION SIZING;

GENETIC ALGORITHMS;

EID: 34548104671     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277080     Document Type: Conference Paper

References (32)

1. M. Abramowitz and L. Stegun. Handbook of Mathematical Functions. Dover, New York, 1970.
2. C. Aporntewan and P. Chongstitvatana. Building-block identification by simultaneity matrix. Proceedings of the Genetic and Evolutionary Computation Conference, pages 1566-1567, 2003.
3. T. Bäck. Generalized convergence models for tournament and (μ;λ) selection. Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA 1995), pages 2-8, 1995.
4. T. Blickle and L. Thiele. A mathematical analysis of tournament selection. Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA'95), pages 9-16, 1995.
5. T. M. Cover and J. A. Thomas. Elements of Information Theory, pages 18-26. Wiley, New York, 1991.
6. R. Etxeberria and P. Larrañaga. Global optimization using bayesian networks. Proceedings of the Second Symposium on Artificial Intelligence Adaptive Systems, pages 332-339, 1999.
7. W. Feller. An Introduction to Probability Theory, volume 2. Wiley, New York, 1966.
8. D. E. Goldberg. Simple genetic algorithms and the minimal, deceptive problem. In Genetic Algorithms and Simulated Annealing, chapter 6, pages 74-88. Pitman Publishing, London, 1987.
9. D. E. Goldberg. Sizing populations for serial and parallel genetic algorithms. Proceedings of the Third International Conference on Genetic Algorithms, pages 70-79, 1989.
10. D. E. Goldberg. The design of innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Boston, MA, 2002.
11. D. E. Goldberg, K. Deb, and J. H. Clark. Genetic algorithms, noise, and the sizing of populations. Complex Systems, 6:333-362, 1992.
12. D. E. Goldberg, K. Sastry, and T. Latoza. On the supply of building blocks. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 336-342, 2001.
13. G. Harik. Linkage learning via probabilistic modeling in the ecga. IlliGAL Report No. 99010, University of Illinois at Urbana-Champaign, Urbana, IL, February 1999.
14. G. Harik, E. Cantú-Paz, D. E. Goldberg, and B. L. Miller. The gambler's ruin problem, genetic algorithms, and the sizing of populations. Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, pages 7-12, 1997.
15. J. H. Holland. Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI, 1975.
16. M. Hutter and M. Zaffalon. Distribution of mutual information from complete and incomplete data. Computational Statistics and Data Analysis, 48(3):633-657, 2005.
17. P. Larrañaga and J. Lozano, editors. Estimation of Distribution Algorithms. Kluwer Academic Publishers, Boston, MA, 2002.
18. M. Mitchell, S. Forrest, and J. H. Holland. The royal road for genetic algorithms: Fitness landscapes and GA performance. Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pages 245-254, 1992.
19. H. Mühlenbein and R. Höns. The estimation of distributions and the minimum relative entropy principle. Evolutionary Computation, 13(1):1-27, 2005.
20. M. Munetomo and D. E. Goldberg. Identifying linkage groups by nonlinearity/non-monotonicity detection. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), 1:433-440, 1999.
21. J. Ocenasek. Entropy-based convergence measurement in discrete estimation of distribution algorithms. In L. Jose A., L. Pedro, and I. Inaki, editors, Towards a New Evolutionary Computation: Advances in Estimation of Distribution Algorithms, pages 39-49. Springer Verlag, New Yorks, 2006.
22. M. Pelikan, D. E. Goldberg, and E. Cantú-Paz. BOA: The Bayesian optimization algorithm. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), 1:525-532, 1999.
23. M. Pelikan, D. E. Goldberg, and E. Cantú-Paz. Bayesian optimization algorithm, population sizing, and time to convergence. Proceedings of the Genetic and Evolutionary Computation Conference, pages 275-282, 2000.
24. M. Pelikan, K. Sastry, and E. Cantú-Paz, editors. Scalable Optimization via Probabilistic Modeling from Algorithms to Applications. Springer, Berlin, 2006.
25. M. Pelikan, K. Sastry, and D. E. Goldberg. Scalability of the Bayesian optimization algorithm. International Journal of Approximate Reasoning, 31(3):221-258, 2003.
26. C. Reeves. Using genetic algorithms with small populations. Proceedings of the Fifth International Conference on Genetic Algorithms, pages 92-99, 1993.
27. K. Sastry and D. E. Goldberg. On extended compact genetic algorithm. Late-Breaking Paper at the Genetic and Evolutionary Computation Conference, pages 352-359, 2000.
28. K. Sastry and D. E. Goldberg. Designing competent mutation operators via probabilistic model building of neighborhoods. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), 2:114-125, 2004.
29. C. E. Shannon. A mathematical theory of communication. The Bell System Technical Journal, 27:379-423, 1948.
30. A. Wright, R. Poli, C. Stephens, W. B. Landgon, and S. Pulavarty. An estimation of distribution algorithm based on maximum entropy. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), pages 343-354, 2004.
31. T.-L. Yu and D. E. Goldberg. Conquering hierarchical difficulty by explicit chunking: Substructural chromosome compression. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), pages 1385-1392, 2006.
32. T.-L. Yu, D. E. Goldberg, A. Yassine, and Y.-p. Chen. Genetic algorithm design inspired by organizational theory: Pilot study of a dependency structure matrix driven genetic algorithm. Proceedings of Artificial Neural Networks in Engineering (ANNIE 2003), pages 327-332, 2003.