Incorporating a metropolis method in a distribution estimation using markov random field algorithm

2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings

Volumn 3, Issue , 2005, Pages 2576-2583

  Shakya, Siddhartha K ^a   McCall, John A W ^a   Brown, Deryck F ^a  

^a ROBERT GORDON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS; OPTIMIZATION; PROBABILITY; PROBLEM SOLVING;

ESTIMATION OF DISTRIBUTION ALGORITHMS; FITNESS EVALUATION; MARKOV RANDOM FIELD; METROPOLIS METHOD;

MARKOV PROCESSES;

EID: 27144449634     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. S. Baluja. Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning,. Technical Report CMU-CS-94-163, Pittsburgh, PA, 1994.
2. S. Baluja and S. Davies. Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space. In Proceedings of the 1997 International Conference on Machine Learning, 1997.
3. C. A. Bouman. Markov random elds and stochastic image models. Tutorial presented at IEEE International Conference on Image Processing, Washington, 23-25 Oct, 1995.
4. D. F. Brown, A. B. Garmendia-Doval, and J. A. W. McCall. Markov Random Field Modelling of Royal Road Genetic Algorithms. Lecture Notes in Computer Science, 2310:65 78, January 2002.
5. L. Davis, editor. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991.
6. J. S. de Bonet, C. L. Isbell, Jr., and P. Viola. MIMIC: Finding optima by estimating probability densities. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems, volume 9, page 424. The MIT Press, 1997.
7. L. delaOssa, J. A. Gamez, and J. M. Puerta. Migration of Probability Models Instead of Individuals: An Alternative When Applying the Island Model to EDAs. In Parallel Problem Solving from Nature VIII, pages 242 252. Springer, 2004.
8. H. Derin and P. A. Kelly. Discrete-index Markov-type random elds. Proceedings of the IEEE, 77:1485 1510, 1989.
9. S. Droste, T. Jansen, and I. Wegener. On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science, 276:5181, 2002.
10. D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
11. G. Golub and C. Van Loan. Matrix Computations. Baltimore, MD, second edition, 1989.
12. G. Harik. Linkage learning via probabilistic modeling in the ECGA. Technical Report IlliGAL Report No. 99010, University of Illinois at Urbana-Champaign, 1999.
13. G. R. Harik, F. G. Lobo, and D. E. Goldberg. The compact genetic algorithm. IEEE-EC, 3(4):287, November 1999.
14. H. H. Hoos and T. Stutzle. Towards a characterisation of the behaviour of stochastic local search algorithms for SAT. Arti cial Intelligence, 112(1-2):213 232, 1999.
15. P. Larranaga, R. Etxeberria, J. Lozano, and J. Pena. Optimization by learning and simulation of bayesian and gaussian networks. Technical Report EHU-KZAA-IK-4/99, University of the Basque Country, 1999.
16. P. Larranaga and J. A. Lozano. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, 2002.
17. S. Z. Li. Markov Random Field modeling in computer vision. Springer-Verlag, 1995.
18. N. Metropolis. Equations of state calculations by fast computational machine. Journal of Chemical Physics, 21:1087 1091, 1953.
19. H. Muhlenbein. The science of breeding and its application to the breeder genetic algorithm. Evolutionary Computation, 1:pp. 335 360., 1994.
20. H. Muhlenbein. Evolutionary algorithms and the boltzmann distribution. In Foundations of Genetic Algorithms (FOGA2002), 2002.
21. H. Muhlenbein and R. Hons. A theoretical and experimental investigation of estimation of distribution algorithms. In Optimization by Building and Using Probabilistic Models (OBUPM-2004), 2004.
22. H. Muhlenbein and T. Mahnig. A new adaptive boltzmann selection schedule sds. In Proceedings of the 2001 Congress on Evolutionary Computation, 2001.
23. H. Muhlenbein, T. Mahnig, and A. R. Ochoa. Schemata, distributions and graphical models in evolutionary optimization. Journal of Heuristics, 5(2):215 247, 1999.
24. H. Muhlenbein and G. Paa. From recombination of genes to the estimation of distributions: I. binary parameters. In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature PPSN IV, pages 178 187, Berlin, 1996. Springer.
25. I. Murray and Z. Ghahramani, Bayesian Learning in Undirected Graphical Models: Approximate MCMC algorithms. In Twentieth Conference on Uncertainty in Arti cial Intelligence (UAI 2004), Banff, Canada, 8-11 July 2004.
26. M. Pelikan. Bayesian optimization algorithm: From single level to hierarchy. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL, 2002. Also IlliGAL Report No. 2002023.
27. M. Pelikan and D. E. Goldberg. Hierarchical BOA solves Ising spin glasses and MAXSAT. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003), pages 1271 1282, 2003. Also IlliGAL Report No. 2003001.
28. M. Pelikan, D. E. Goldberg, and E. Cant'u-Paz. BOA: The Bayesian Optimization Algorithm. In W. Banzhaf et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference GECCO99, volume I, pages 525 532, San Fransisco, CA, 1999. Morgan Kaufmann Publishers.
29. M. Pelikan, D. E. Goldberg, and F. Lobo. A survey of optimization by building and using probabilistic models. Technical Report 99018, Illinois Genetic Algorithms Lab, UIUC, Urbana, IL, 1999.
30. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C: The An of Scienti c Computing. Cambridge University Press, Cambridge, UK, 2nd edition, 1993.
31. R. Santana. Estimation of Distribution Algorithms with Kikuchi Approximation. Evolutonary Computation, 13:67 98, 2005.
32. S. Shakya, J. McCall, and D. Brown. Estimating the distribution in an EDA. In B. Ribeiro, R. F. Albrechet, A. Dobnikar, D. W. Pearson, and N. C. Steele, editors, Proceedings of the International Conference on Adaptive and Natural computiNG Algorithms (ICANNGA 2005), pages 202 205, Coimbra, Portugal, 2005. Springer-Verlag, Wien.
33. S. Shakya, J. McCall, and D. Brown. Using a Markov Network Model in a Univariate EDA: An Emperical Cost-Bene t Analysis. In. Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2005), Washington, D.C., USA, 2005. ACM.
34. S. K. Shakya, J. A. W. McCall, and D. F. Brown. Updating the probability vector using MRF technique for a Univariate EDA. In E. Onaindia and S. Staab, editors, Proceedings of the Second Starting AI Researchers' Symposium, pages 15 25, Valencia, Spain, 2004. IOS press.