Improved EDNA (Estimation of Dependency Networks Algorithm) using combining function with bivariate probability distributions

GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Volumn , Issue , 2008, Pages 407-414

  Gámez, José A ^a   Mateo, Juan L ^a   Puerta, José M ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

Combinatorial optimization; Dependency networks; Estimation of distribution algorithms; Probability combining function; Scalability

Indexed keywords

BAYESIAN NETWORKS; COMBINATORIAL OPTIMIZATION; COMPLEX NETWORKS; LEARNING ALGORITHMS; SCALABILITY;

BIVARIATE STATISTICS; DEPENDENCY NETWORKS; ESTIMATION OF DISTRIBUTION ALGORITHMS; MULTIVARIATE MODELS; PARAMETER LEARNING; PROBABILISTIC GRAPHICAL MODELS; STRUCTURAL LEARNING; STRUCTURE-LEARNING;

PROBABILITY DISTRIBUTIONS;

EID: 57349199340     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1389095.1389171     Document Type: Conference Paper

References (28)

1. S. Baluja and S. Davies. Combining Multiple Optimization Runs with Optimal Dependency Trees. Technical Report CMU-CS-97-157, Carnegie Mellon University, 1997.
2. J. S. D. Bonet, C. L. Isbell, and P. Viola. MIMIC: Finding optima by estimating probability densities. In Advances in Neural Information Processing Systems, volume 9, pages 424-430. MIT Press, Cambridge, 1997.
3. N. Friedman and M. Goldszmidt. Learning bayesian networks with local structure. In 12th Annual Conference on Uncertainty in Artificial Intelligence (UAI-96), pages 252-262, 1996.
4. J. A. Gámez, J. L. Mateo, and J. M. Puerta. Dependency networks based classifiers: learning models by using independence test. In Third European Workshop on Probabilistica Graphical Models (PGM06), pages 115-122, 2006.
5. J. A. Gámez, J. L. Mateo, and J. M. Puerta. EDNA: Estimation of Dependency Networks Algorithm. In International Work-Conference on the Interplay between Natural and Artificial Computation (IWINAC'07), volume 1, pages 427-436, 2007.
6. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:147-156, 1984.
7. D. E. Goldberg, K. Deb, and J. Horn. Massive multimodality, deception, and genetic algorithms. In Parallel Problem Solving from Nature (PPSN), volume 2, 1992. [8] G. Harik. Linkage learning in via probabilistic modelling in the EcGA. Technical Report 99010, Illinois Genetic Algorithms Laboratory, 1999.
8. G. Harik. Finding multimodal solutions using restricted tournament selection. In Proceedings of the Sixth International Conference on Genetic Algorithms, pages 24-31, 2005.
9. G. Harik, F. G. Lobo, and D. E. Golberg. The compat genetic algorithm. In Proceedings of the 4th International Conference on Evolutionary Computation, pages 1-5, 1997.
10. D. Heckerman. A Tutorial on Learning Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research, Mar. 1995.
11. D. Heckerman, D. M. Chickering, and C. Meek. Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research, 1:49-75, 2000.
12. D. Heckerman, D. Geiger, and D. M. Chickering. Learning bayesian networks: The combination of knowledge and statistical data. In 10th Conf. Uncertainty in Artificial Intelligence, pages 293-301. Morgan Kaufmann Publishers, 1994.
13. J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
14. M. Jaeger. Complex probabilistic modeling with recursive relational bayesian networks. Annals of Mathematics and Artificial Intelligence, 32:179 - 220, 2001.
15. P. Larrañaga, R. Etxeberria, J. A. Lozano, and J. M. Peña. Combinatonal Optimization by Learning and Simulation of Bayesian Networks. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, pages 343-352, 2000.
16. J. L. Mateo and L. de la Ossa. LiO: an easy and flexible library of metaheuristics. Technical Report DIAB-06-04-1, Departamento de Sistemas InformÃa̧ticos, Escuela PolitAl'cnica Superior de Albacete, Universidad de Castilla-La Mancha, 2006.
17. H. Mühlenbein. The equation for response to selection an its use for prediction. Evolutionary Computation, 5:303-346, 1998.
18. H. Mühlenbein, T. Mahnig, and A. Ochoa. Schemata, distributions and graphical models in evolutionary optimization. Journal of Heuristics, 5:215-247, 1999.
19. H. Mühlenbein and G. Paaß. From recombination of genes to the estimation of distributions I. Binary patameters. In 4th Internatial Conference on Parallel Problem Solving from Nature, pages 178-187, 1996.
20. J. Neville and D. Jensen. Relational dependency networks. Journal of Machine Learning Research, 8:653-692, 2007.
21. A. Onisko, M. J. Druzdzel, and H. Wasyluk. Learning bayesian network parameters from small data sets: Application of noisy-or gates. International Journal of Approximate Reasoning, 27(2):165-182, 2001.
22. J. Pearl. Fusion, propagation and structuring in belief networks. Artificial Intelligence, 29:241-288, 1986.
23. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
24. M. Pelikan and D. E. Goldberg. Escaping hierarchical traps with competent genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 511-518, San Francisco, California, USA, 7-11 2001. Morgan Kaufmann.
25. R. Santana. Estimation of distribution algorithms with kikuchi approximations. Evolutionary Computation, 13(1):67-97, 2005.
26. G. Schwarz. Estimating the dimension of a model. Annals of Statistics, 6(2):461-464, 1978.
27. Y. Tian, Q. Yang, T. Huang, C. X. Ling, and W. Gao. Learning Contextual Dependency Network Models for Link-Based Classification. IEEE Transactions on Knowledge and Data Engineering, 18:1482-1496, Nov 2006.
28. R. A. Watson, G. Hornby, and J. B. Pollack. Modeling building-block interdependency. In Proceedings of the 5th International Conference on Parallel Problem Solving From Nature, pages 480-490, 1998.