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References
Ahn, C. W., Ramakrishna, R. S. and Goldberg, D. E. (2004). Real-coded Bayesian optimization algorithm: Bringing the strength of BOA into the continuous world, in K. Debet al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2004, Springer, Berlin Heidelberg New York, pp. 840-851
Anderson, T. W. (1958). An Introduction to Multivariate Statistical Analysis, Wiley, New York
Bäck, T. and Schwefel, H.-P. (1993). An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation 1(1): 1-23
Baluja, S. and Caruana, R. (1995). Removing the genetics from the standard genetic algorithm, in A. Prieditis and S. Russell (Eds.), Proceedings of the Twelfth International Conference on Machine Learning, Morgan Kauffman, Madison, WI, pp. 38-46
Bilmes, J. (1997). A gentle tutorial of the EM algorithm and its application to para-meter estimation for Gaussian mixture and hidden markov models, Technical Report ICSI TR-97-021, Department of Electrical Engineering, University of Berkeley, Berkeley, CA
Bishop, C. M. (1999). Latent variable models, in M. I. Jordan (Ed.), Learning in Graphical Models, MIT, Cambridge, MA
Bosman, P. A. N. (2003). Design and Application of Iterated Density-Estimation Evolutionary Algorithms, PhD thesis, Utrecht University, Utrecht, The Netherlands
Bosman, P. A. N. and Grahl, J. (2005). Matching inductive search bias and problem structure in continuous estimation of distribution algorithms, Technical report, Mannheim Business School, Department of Logistics, http://www.bwl. unimannheim.de/Minner/hp/ files/forschung/reports/tr-2005-03.pdf
Bosman, P. A. N. and Thierens, D. (2000a). Continuous iterated density estimation evolutionary algorithms within the IDEA framework, in M. Pelikan et al. (Eds.), Proceedings of the Optimization by Building and Using Probabilistic Mod-els OBUPM Workshop at the Genetic and Evolutionary Computation Conference - GECCO - 2000, Morgan Kaufmann, San Francisco, CA, pp. 197-200
Bosman, P. A. N. and Thierens, D. (2000b). Expanding from discrete to continuous estimation of distribution algorithms: The IDEA, in M. Schoenauer et al. (Eds.), Parallel Problem Solving from Nature - PPSN VI, Springer, Berlin Heidelberg New York, pp. 767-776
Bosman, P. A. N. and Thierens, D. (2001a). Advancing continuous IDEAs with mix-ture distributions and factorization selection metrics, in M. Pelikan and K. Sastry (Eds.), Proceedings of the Optimization by Building and Using Probabilistic Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2001, Morgan Kaufmann, San Francisco, CA, pp. 208-212
Bosman, P. A. N. and Thierens, D. (2001b). Exploiting gradient information in continuous iterated density estimation evolutionary algorithms, in B. Kröse et al. (Eds.), Proceedings of the Thirteenth Belgium-Netherlands Artificial Intelligence Conference BNAIC-2001, pp. 69-76
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and Regression Trees, Wadsworth, Pacific Grove, CA
Buntine, W. (1994). Operations for learning with graphical models, Journal of Artificial Intelligence Research 2: 159-225
Cho, D.-Y. and Zhang, B.-T. (2001). Continuous estimation of distribution algorithms with probabilistic principal component analysis, Proceedings of the 2001 Congress on Evolutionary Computation - CEC - 2001, IEEE, Piscataway, NJ, pp. 521-526
Cho, D.-Y. and Zhang, B.-T. (2002). Evolutionary optimization by distribution estimation with mixtures of factor analyzers, Proceedings of the 2002 Congress on Evolutionary Computation - CEC - 2002, IEEE, Piscataway, NJ, pp. 1396-1401
Cho, D.-Y. and Zhang, B.-T. (2004). Evolutionary continuous optimization by distribution estimation with variational Bayesian independent component analyzers mixture model, in X. Yao et al. (Eds.), Parallel Problem Solving from Nature -PPSN VIII, Springer, Berlin Heidelberg New York, pp. 212-221
Choudrey, R. A. and Roberts, S. J. (2003). Variational mixture of Bayesian independent component analyzers, Neural Computation 15(1): 213-252
Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistic Society Series B 39: 1-38
Edmonds, J. (1967). Optimum branchings, Journal of Research of the National Bureau of Standards 71B: 233-240. Reprinted in Math. of the Decision Sciences, American Mathematical Society Lecture notes in Applied Mathematics, 11: 335-345, 1968
Edwards, D.(1995). Introduction to Graphical Modelling, Springer, Berlin Heidelberg New York
Etxeberria, R. and Larrañaga, P. (1999). Global optimization using Bayesian networks, in A. A. O. Rodriguez et al. (Eds.), Proceedings of the Second Symposium on Artificial Intelligence CIMAF-1999, Institute of Cybernetics, Mathematics and Physics, pp. 332-339
Friedman, N. and Goldszmidt, M. (1996). Learning Bayesian networks with local structure, in E. Horvits and F. Jensen (Eds.), Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence UAI-1996, Morgan Kaufmann, San Francisco, CA, pp. 252-262
Gallagher, M., Frean, M. and Downs, T. (1999). Real-valued evolutionary optimization using a flexible probability density estimator, in W. Banzhaf et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference -GECCO-1999, Morgan Kaufmann, San Francisco, CA, pp. 840-846
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learing, Addison-Wesley, Reading, MA
Goldberg, D. E. (2002a). The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Vol. 7 of Series on Genetic Algorithms and Evolutionary Computation, Kluwer, Dordrecht
Goldberg, D. E. (2002b). The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Vol. 7 of Series on Genetic Algorithms and Evolutionary Computation, Kluwer, Dordrecht
Grahl, J., Minner, S. and Rothlauf, F. (2005a). An analysis of iterated density estimation and sampling in the UMDAc algorithm, Late-Breaking Papers of the Genetic and Evolutionary Computation Conference - GECCO - 2005
Grahl, J., Minner, S. and Rothlauf, F. (2005b). Behaviour of UMDAc with trun-cation selection on monotonous functions, Proceedings of the 2005 Congress on Evolutionary Computation - CEC - 2005, IEEE, Piscataway, NJ
Hansen, N. and Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies, Evolutionary Computation 9(2): 159-195
Hansen, N., Müller, S. D. and Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), Evolutionary Computation 11(1): 1-18
Hartigan, J. (1975). Clustering Algorithms, Wiley, New York
Holland, J. H. (1975). Adaptation in Natural and Artifical Systems, University of Michigan Press, Ann Arbor, MI
Jolliffe, I. T.(1986). Principal Component Analysis, Springer, Berlin Heidelberg New York
Kern, S., Müller, S. D., Hansen, N., Büche, D., Ocenasek, J. and Koumoutsakos, P. (2004). Learning probability distributions in continuous evolutionary algorithms - a comparative review, Natural Computing 3(1): 77-112
Larrañaga, P., Etxeberria, R., Lozano, J. A. and Peña, J. M. (2000). Optimization in continuous domains by learning and simulation of Gaussian networks, in M. Pelikan et al. (Eds.), Proceedings of the Optimization by Building and Using Probabilistic Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2000, Morgan Kaufmann, San Francisco, CA, pp. 201-204
Lauritzen, S. L. (1996). Graphical Models, Clarendon, Oxford
Mühlenbein, H. and Höns, R. (2005). The estimation of distributions and the minimum relative entropy principle, Evolutionary Computation 13(1): 1-27
Ocenasek, J. and Schwarz, J. (2002). Estimation of distribution algorithm for mixed continuous-discrete optimization problems, 2nd Euro-International Symposium on Computational Intelligence, pp. 227-232
Ocenasek, J., Kern, S., Hansen, N., Müller, S. and Koumoutsakos, P. (2004). A mixed Bayesian optimization algorithm with variance adaptation, in X. Yao et al. (Eds.), Parallel Problem Solving from Nature - PPSN VIII, Springer, Berlin Heidelberg New York, pp. 352-361
Pelikan, M. and Goldberg, D. E. (2000). Genetic algorithms, clustering, and the breaking of symmetry, in M. Schoenauer et al. (Eds.), Parallel Problem Solving from Nature - PPSN VI, Springer, Berlin Heidelberg New York, pp. 385-394
Pelikan, M. and Goldberg, D. E. (2001). Escaping hierarchical traps with competent genetic algorithms, in L. Spector et al. (Eds.), Proceedings of the GECCO-2001 Genetic and Evolutionary Computation Conference, Morgan Kaufmann, San Francisco, CA, pp. 511-518
Pelikan, M. and Goldberg, D. E. (2003). Hierarchical BOA solves Ising spin glasses and MAXSAT, in E. Cantú-Paz et al. (Eds.), Proceedings of the GECCO-2003 Genetic and Evolutionary Computation Conference, Springer, Berlin Heidelberg New York, pp. 1271-1282
Pelikan, M., Goldberg, D. E. and Cantú-Paz, E. (1999). BOA: The Bayesian optimization algorithm, in W. Banzhaf et al. (Eds.), Proceedings of the GECCO-1999 Genetic and Evolutionary Computation Conference, Morgan Kauffman, San Francisco, CA, pp. 525-532
Pošík, P. (2004). Distribution tree-building real-valued evolutionary algorithm, in X. Yao et al. (Eds.), Parallel Problem Solving from Nature - PPSN VIII, Springer, Berlin Heidelberg New York, pp. 372-381
Priebe, C. E. (1994). Adaptive mixtures, Journal of the American Statistical Asso-ciation 89(427): 796-806
Rudlof, S. and Köppen, M. (1996). Stochastic hill climbing with learning by vectors of normal distributions, in T. Furuhashi (Ed.), Proceedings of the First Online Workshop on Soft Computing (WSC1), Nagoya University, Nagoya, Japan, pp. 60-70
Russel, S. and Norvig, P.(2003). Artificial Intelligence: A Modern Approach, Prentice-Hall, Englewood Cliffs, NJ
Sebag, M. and Ducoulombier, A. (1998). Extending population-based incremental learning to continuous search spaces, in A. E. Eiben et al. (Eds.), Parallel Problem Solving from Nature - PPSN V, Springer, Berlin Heidelberg New York, pp. 418-427
Servet, I., Trave-Massuyes, L. and Stern, D. (1997). Telephone network traffic overloading diagnosis and evolutionary computation technique, in J. K. Hao et al. (Eds.), Proceedings of Artificial Evolution ’97, Springer, Berlin Heidelberg New York, pp. 137-144
Shin, S.-Y. and Zhang, B.-T. (2001). Bayesian evolutionary algorithms for continuous function optimization, Proceedings of the 2001 Congress on Evolutionary Computation - CEC - 2001, IEEE, Piscataway, NJ, pp. 508-515
Shin, S.-Y., Cho, D.-Y., and Zhang, B.-T. (2001). Function optimization with latent variable models, in A. Ochoa et al. (Eds.), Proceedings of the Third International Symposium on Adaptive Systems ISAS-2001 - Evolutionary Computation and Probabilistic Graphical Models, Institute of Cybernetics, Mathematics and Physics, pp. 145-152
Tatsuoka, M. M. (1971). Multivariate Analysis: Techniques for Educational and Psychological Research, Wiley, New York
Thierens, D. and Goldberg, D.(1993). Mixing in genetic algorithms, in S. Forrest(ed.), Proceedings of the fifth conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, pp. 38-45
Tsutsui, S., Pelikan, M. and Goldberg, D. E. (2001). Evolutionary algorithm using marginal histogram in continuous domain, in M. Pelikan and K. Sastry (Eds.), Proceedings of the Optimization by Building and Using Probabilistic Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference - GECCO - 2001, Morgan Kaufmann, San Francisco, CA, pp. 230-233
Yuan, B. and Gallagher, M. (2005). On the importance of diversity maintenance in estimation of distribution algorithms, in H.-G. Beyer et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference - GECCO - 2005, ACM, New York, USA, pp. 719-726
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Bosman, P.A.N., Thierens, D. (2006). Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms. In: Pelikan, M., Sastry, K., CantúPaz, E. (eds) Scalable Optimization via Probabilistic Modeling. Studies in Computational Intelligence, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-34954-9_5
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