Using a priori knowledge to create probabilistic models for optimization

International Journal of Approximate Reasoning

Volumn 31, Issue 3, 2002, Pages 193-220

  Baluja, Shumeet ^a,b,c  

^a Carnegie Mellon University   (United States)

^b JAMDAT Mobile ^*  (United States)

^c JAMDAT Mobile   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMBINATORIAL MATHEMATICS; MATHEMATICAL MODELS; PROBABILITY;

SEARCH ALGORITHMS;

OPTIMIZATION;

EID: 0036859018     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0888-613X(02)00094-4     Document Type: Article

References (46)

1. S. Baluja, Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, Technical Report CMUCS- 94-163, Carnegie Mellon University, Pittsburgh, PA, 1994.
2. S. Baluja, An Empirical Comparison of Seven Iterative and Evolutionary Heuristics for Static Function Optimization Technical Report CMU-CS-95-193, Carnegie Mellon University, Pittsburgh, PA, 1995.
3. Baluja S., Caruana R. Removing the genetics from the standard genetic algorithm. Prieditis A., Russel S. Proceedings of the Twelth International Conference on Machine Learning. 1995;38-46 Morgan Kaufmann Publishers, San Mateo, CA.
4. Baluja S., Davies S. Using optimal dependency trees for combinatorial optimization: learning the structure of the search space. Fisher D.H. Proceedings of the Fourteenth International Conference on Machine Learning. 1997;30-38 Morgan Kaufmann Publishers, San Mateo, CA.
5. Baluja S., Davies S. Fast probabilistic modeling for combinatorial optimization. Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98). 1998;469-476 AAAI Press.
6. Bosman P., Thierens D. Linkage information processing in distribution estimation algorithms. Banzhaf W., Daida J., Eiben A.E., Garzon M.H., Honavar V., Jakiela M. Proceedings of the Genetic and Evolutionary Computation Conference. 1999;60-70 Morgan Kaufmann Publishers.
7. P. Bosman, D. Thierens, Continuous Iterated Density Estimation Evolutionary Algorithms within the IDEA Framework, Technical Report UU-CS-2000-15, Utrecht University, 2000.
8. D. Chickering, D. Geiger, D. Heckerman, Learning Bayesian Networks: Search Methods and Experimental Results, Preliminary Papers of the Fifth International Workshop on Artificial Intelligence and Statistics, 1995, pp. 112-128.
9. Chow C., Liu C. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory. 14:1968;462-467.
10. K. De Jong, Analysis of Behavior of a Class of Genetic Adaptive Systems. Ph.D. Thesis. University of Michigan, Ann Arbor, MI, 1975.
11. De Bonet J., Isbell C., Viola P. MIMIC: finding optima by estimating probability densities. Mozer M.C., Jordan M.I., Petsche T. Advances in Neural Information Processing Systems 9. 1997;424-431 The MIT Press, Cambridge.
12. Eshelman L.J., Mathias K.E., Schaffer J.D. Convergence controlled variation. Proceedings Foundations of Genetic Algorithms 4. 1996;203-224 Morgan Kaufmann Publishers, San Mateo, CA.
13. R. Etxeberria, P. Larrañaga, Global optimization using Bayesian networks, in: Proceedings of the Second Symposium on Artificial Intelligence. 1999, pp. 332-339.
14. Gallagher M., Fream M., Downs T. Real-valued evolutionary optimization using a flexible probability density estimator. Banzhaf W., Daida J., Eiben A.E., Garzon M.H., Honavar V., Jakiela M. Proceedings of the Genetic and Evolutionary Computation Conference. 1999;840-846 Morgan Kaufmann Publishers.
15. Goldberg D.E. Genetic Algorithms in Search Optimization and Machine Learning. 1989;Addison-Wesley, Reading, MA.
16. González C., Lozano J.A., Larrañaga P. Analyzing the PBIL algorithm by means of discrete dynamical systems. Complex Systems. 12(4):2000;465-479.
17. González C., Lozano J.A., Larrañaga P. The convergence behavior of the 24 PBIL algorithm: a preliminary approach. Proceedings of the Fifth International Conference on Artificial Neural Networks and Genetic Algorithms. 2001;228-231 Springer.
18. J.R. Greene, Population-based incremental learning as a simple versatile tool for engineering optimization, in: Proceedings of the First International Conference on EC and Applications, 1996, pp. 258-269.
19. D. Heckerman, A Tutorial on Learning with Bayesian Networks, Technical Report MSR-TR-95-06, Microsoft, 1996.
20. Heckerman D., Geiger D., Chickering D. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning. 20:1995;197-243.
21. Hertz J., Krogh A., Palmer R.G. Introduction to the Theory of Neural Computing. 1991;Addison-Wesley, Reading, MA.
22. Hohfeld M., Rudolph G. Towards a theory of population-based incremental learning. Proceedings of the Fourth IEEE Conference on Evolutionary Computation, Piscataway. 1997;1-5 IEEE Press, NJ.
23. Holland J.H. Adaptation in Natural and Artificial Systems. 1975;University of Michigan Press, Ann Arbor, MI.
24. Jensen F.V. Bayesian Networks and Decision Graphs. 2001;Springer-Verlag.
25. A. Juels, Topics in Black-box Combinatorial Optimization. Ph.D. Thesis, University of California-Berkeley, 1996.
26. A. Keane, The Options Design Exploration System: Reference Manual and Users Guide, 2000, available from: http://www.soton.ac.uk/̃ajk/options/welcome.html.
27. Kvasnica V., Pelikan M., Pospical J. Hill climbing with learning (An abstraction of genetic algorithm). Neural Network World (Czech Republic). 6(5):1996;773-796.
28. P. Larrañaga, R. Etxeberria, A. Lozano, J. Peña, Optimization by learning and simulation of Bayesian and Gaussian networks, Technical Report EHU-KZAA-IK-4/99, Department of Computer Science and Artificial Intelligence, University of the Basque Country, 1999.
29. P. Larrañaga, R. Etxeberria, A. Lozano, J. Peña, Optimization in continuous domains by learning and simulation of Gaussian networks, Optimization By Building and Using Probabilistic Models Workshop in the GECCO-2000 Conference, 2000 pp. 201-204.
30. Larrañaga P., Etxeberria R., Lozano J.A., Peña J.M. Combinatorial optimization by learning and simulation of Bayesian networks. proceedings of the Conference in Uncertainty in Artificial Intelligence. 2000;343-352 Morgan Kauffman Publishers.
31. Larrañaga P., Lozano J. Estimation of Distribution Algorithms. A New Tool for Evolutionary Comutation. 2001;Kluwer Academic Publishers, Dordrecht.
32. T. Mahnig, H. Muhlenbein, Mathematical analysis of optimization methods using search distributions, Optimization By Building and Using Probabilistic Models Workshop in the GECCO-2000 Conference, 2001, pp. 205-208.
33. Muhlenbein H. The equation for response to selection and its use for frediction. Evolutionary Computation. 5(3):1997;303-346.
34. Muhlenbein H., Mahnig T. Convergence theory and applications of the factorized distribution algorithm. Journal of Computing and Information Technology. 7:1999;19-32.
35. Muhlenbein H., Mahnig T., Rodriguez A.O. Schemata, distributions and graphical models in evolutionary optimization. Journal of Heuristics. 5:1999;215-247.
36. A. Ochoa, H. Muehlenbein, M. Soto, Factorized distribution algorithms using Bayesian networks of bounded complexity, Optimization By Building and Using Probabilistic Models Workshop in the GECCO-2000 Conference, 2000, pp. 212-215.
37. A. Ochoa, M. Soto, R. Santana, J.C. Madera, N. Jorge, The factorized distribution algorithm and the junction tree: a learning perspective, in: Proceedings of the Second Symposium on Artificial Intelligence, 1999, pp. 368-377.
38. Pearl J. Probabilistic Reasoning in Intelligent Systems. 1988;Morgan Kaufmann.
39. Pelikan M., Goldberg D.E., Lobo F. A survey of optimization by building and using probabilistic models. Computational Optimization and Applications. 21(1):2002;5-20.
40. M. Pelikan, K. Sastry, D.E. Goldberg, Evolutionary Algorithms + Graphical Models = Scalable Black-Box Optimization, Technical Report 2001029, Illinois Genetic Algorithm Laboratory, University of Illinois at Urbana-Champaign, 2001.
41. M. Pelikan D.E. Goldberg, S. Tsutsui, Combining the Strengths of the Bayesian Optimization Algorithm and Adaptive Evolution Strategies, Technical Report 2001023, Illinois Genetic Algorithm Laboratory, University of Illinois at Urbana-Champaign, 2001.
42. Sebag M., Ducoulombier A. Extending population-based incremental learning to continuous search spaces. Parallel Problem Solving from Nature V. 1998;418-427.
43. Syswerda G. Uniform crossover in genetic algorithms. Schaffer D. Proceedings of the Third International Conference on Genetic Algorithms. 1989;2-9 Morgan Kaufmann Publishers, San Mateo, CA.
44. Syswerda G. Simulated crossover in genetic algorithms. Whitley D.L. Foundations of Genetic Algorithms 2. 1993;239-255 Morgan Kaufmann Publishers, San Mateo, CA.
45. Thathachar M., Sastry P.S. Learning optimal discriminant functions through a cooperative game of automata. IEEE Transactions on Systems, Man, and Cybernetics. 17(1):1987;73-85.
46. Q. Zhang, H. Muehlenbein, On Global Convergence of FDA with Proportionate Selection, in: Proceedings of the Second Symposium on Artificial Intelligence, 1999, pp. 340-343.