Evolving dynamic Bayesian networks with Multi-objective genetic algorithms

Applied Intelligence

Volumn 26, Issue 1, 2007, Pages 13-23

  Ross, Brian J ^a   Zuviria, Eduardo ^a,b  

^a BROCK UNIVERSITY   (Canada)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

Dynamic Bayesian networks; Genetic algorithms; Multi objective optimization

Indexed keywords

COMPUTATIONAL COMPLEXITY; GENETIC ALGORITHMS; HEURISTIC METHODS; OPTIMIZATION; PROBABILISTIC LOGICS;

DYNAMIC BAYESIAN NETWORKS; MULTI OBJECTIVE OPTIMIZATION;

TELECOMMUNICATION NETWORKS;

EID: 33846533094     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-006-0002-6     Document Type: Article

References (47)

1. Charniak E (1991) Bayesian networks without tears. AI Magazine, Winter, pp 50-63
2. Pearl J (2000) Causality. Cambridge
3. Russell S, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall
4. Castillo E, Gutierrez JM, Hadi AS (1997) Expert systems and probabilistic network models. Springer Verlag
5. Markowetz F (2005) A bibliography on learning causal networks of gene interactions, http://www.molgen.mpg.de/~markowet/docs/network-bib.pdf. Last accessed April 1, 2005
6. Nefian AV, Liang L, Pi X, Liu X, Murphy K (2002) Dynamic Bayesian networks for audio-visual speech recognition. EURASIP J Appl Signal Process pp 1-15
7. Davies S, Moore A (1999) Bayesian networks for lossless dataset compression. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining, pp 387-391
8. Pelikan M, Goldberg DE, Cantu-Paz E (1999) BOA: the Bayesian optimization algorithm. In: Banzhaf W et al. (eds) Proceedings of the GECCO-99, pp 525-532
9. Friedman N, Murphy K, Russell S (1998) Learning the structure of dynamic probabilistic networks. In: Proceedings of the uncertainty in AI (UAI'98), Morgan Kaufman
10. Murphy KP (2005) Dynamic Bayesian networks. www.cs.ubc.ca/~murphyk/ Papers/dbnchapter.pdf. To appear in Probabilistic Graphical Models, M. Jordan. Last accessed April 13 2005
11. Husmeier D (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19(17):2271-2282
12. Kim SY, Imoto S, Miyano S (2003) Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings in Bioinformatics 4(3):228-235
13. Perrin B-E, Ralaivola L, Mazurie A, Bottani S, Mallet J, d'Alche Bue F (2003) Gene networks inference using dynamic Bayesian networks. Bioinformatics 19:ii138-ii148, Supplement 2
14. Wu CC, Huang HC, Juan HF, Chen ST (2004) GeneNetwork: an interactive tool for reconstruction of genetic networks using microarray data. Bioinformatics 20(18):3691-3693
15. Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20(18):3594-3603
16. Buntine WL (1994) Operations for learning with graphical models. J Artif Intell Res 2:159-225
17. Heckerman D (1995) A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, March 1995
18. Chrisman L (2005) A roadmap to research on Bayesian networks and other decomposable probabilistic models, citeseer.nj.nec.com/chrisman98roadmap.html. Last accessed April 1 2005
19. Stephenson TA (2000) An introduction to Bayesian network theory and usage. Technical Report 00-03, IDIAP, February 2000
20. Guo H, Hsu W (2002) A survey of algorithms for real-time Bayesian network inference. In: Proceedings of the joint AAAI-02/KDD-02/UAI-02 workshop on realtime decision support and diagnosis systems
21. CooperG, Herskovits E(1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9:309-347
22. Chickering DM (1996) Learning Bayesian networks is NP-complete. In: Fisher D, Lenz H-J (eds) Learning from data: AI and statistics. Springer Verlag
23. Bouckaert RR (1994) Probabilistic network construction using the minimum description length principle. Technical Report UU-CS-1994-27, Utrecht University, Dept. of Computer Science, July 1994
24. Lam W, Wong ML, Leung KS, Ngan PS (1998) Discovering probabilistic knowledge from databases using evolutionary computation and minimum description length principle. In: Koza JR et al (eds) Proceeding of the genetic programming 1998. Morgan Kaufmann, pp 786-794
25. Sierra B, Larranaga P (1998) Predicting the survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches. Artif Intell Med 14(1-2):215-230
26. de Campos LM, Huete JF (1999) Approximating causal orderings for Bayesian networks using genetic algorithms and simulated Annealing. Technical Report #DECSAI-99021, U. of Grenada, Uncertainty Treatment in Artificial Intelligence Group, May 1999
27. Myers JW, Laskey KB, DeJong KA (1999) Learning Bayesian networks from incomplete data using evolutionary algorithms. In Banzhaf W et al (eds) Proceedings of the GECCO-99, pp 458-465
28. Cotta C, Murzabal J (2002) Towards a more efficient evolutionary induction of Bayesian networks. In: Proceedings of the PPSN 2002, pp 730-739
29. Hsu WH, Guo H, Perry BB, Stilson JA (2002) A permutation genetic algorithm for variable ordering in learning Bayesian networks from data. In: Langdon WB et al (eds) Proceedings of the GECCO 2002, pp. 383-390, Morgan Kaufmann
30. Harwood S, Scheines R (2002) Genetic algorithm search over causal models. Technical Report CMU-PHIL-131, Carnegie Mellon University, Dept. of Philosophy
31. Wong ML, Lee SY, Leung KS (2002) A hybrid data mining approach to discover bayesian networks using evolutionary programming. In: Langdon WB et al (eds) Proceedings of the GECCO 2002, pp 214-221
32. van Dijk S, Thierens D, van der Gaag LC (2003) Building a GA from design principles for learning Bayesian networks. In: Cantu-Paz E et al (eds) Proceedings of the GECCO 2003, Springer-Verlag, pp 886-897
33. Larranaga P, Poza M, Yurramendi Y, Murga RH, Kuijpers CMH (1996) Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Trans Pattern Anal Mach Intell 18(9):912-926
34. Tucker A, Lui X, Ogden-Swift A (2001) Evolutionary learning of dynamic probabilistic models with large time lags. Int J Intell Syst 16(5):621-646
35. Tucker A, Liu X, Garway-Heath D (2003) Spatial operators for evolving dynamic Bayesian networks from spatio-temporal data. In: Cantu-Paz E et al (eds) Proceedings of the GECCO 2003, Springer-Verlag, pp 2360-2371
36. Tucker A, Liu X (2003) Learning dynamic Bayesian networks from multivariate time series with changing dependencies. In: Proceedings of the 5th intelligent data analysis conference (IDA 2003), Springer-Verlag, pp 100-110, 2003. LNCS 2810
37. Coello Coello CA, van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, Kluwer Academic Publishers
38. Fonseca CM, Fleming PJ (1995) An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Comput 3(1): 1-16
39. van Veldhuizen DA, Lamont GB (2000) Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary Computation 8(2): 125-147
40. Holland JH (1992) Adaptation in natural and artificial systems. MIT Press
41. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley
42. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461-464
43. Rissanen J (1986) Stochastic complexity and modeling. Ann Stat 14(3): 1080-1100
44. Barabasi A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(15):509-512
45. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi A-L (2000) The large-scale organization of metabolic networks. Nature 407:651-654
46. Murphy KP (2005) Bayes net toolbox for matlab. www.cs.ubc.ca/~murphyk/ Software/BNT/bnt.html. Last accessed March 16 2005.
47. Gruau F (1994) Genetic micro programming of neural networks. In: Kinnear KE (ed) Advances in genetic programming, MIT Press, pp 495-518