Bosman PA, Thierens D: Linkage information processing in distribution estimation algorithms. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-1999. Edited by: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE. 1999, Orlando, FL: Morgan Kaufmann Publishers, San Francisco, CA, I: 60-67.

Google Scholar

Larrañaga P, Lozano JA, Eds: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. 2002, Kluwer Academic Publishers

Google Scholar

Lozano JA, Larrañaga P, Inza I, Bengoetxea E, Eds: Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms. 2006, Springer-Verlag

Google Scholar

Mühlenbein H, Paaß G: From recombination of genes to the estimation of distributions. Binary parameters. Lecture Notes in Computer Science 1411: Parallel Problem Solving from Nature, PPSN IV. 1996, 178-187.

Google Scholar

Pelikan M: Hierarchical Bayesian Optimization Algorithm. Toward a New Generation of Evolutionary Algorithms, of Studies in Fuzziness and Soft Computing. 2005, Springer, 170:

Book
Google Scholar

Larrañaga P: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation, Kluwer Academic Publishers 2002 chap. A review on estimation of distribution algorithms, 55-98.

Goldberg DE: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. 2002, Kluwer Academic

Book
Google Scholar

Pelikan M, Goldberg DE, Lobo F: A survey of optimization by building and using probabilistic models. Computational Optimization and Applications. 2002, 21: 5-20.

Article
Google Scholar

Baluja S: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. 1994, Tech Rep CMU-CS-94–163, Carnegie Mellon University, Pittsburgh, PA

Google Scholar

Harik GR, Lobo FG, Goldberg DE: The compact genetic algorithm. IEEE Transactions on Evolutionary Computation. 1999, 3 (4): 287-297.

Article
Google Scholar

Sebag M, Ducoulombier A: Extending population-based incremental learning to continuous search spaces. Parallel Problem Solving from Nature – PPSN V. 1998, 418-427.

Chapter
Google Scholar

De Bonet JS, Isbell CL, Viola P: MIMIC: Finding optima by estimating probability densities. Advances in Neural Information Processing Systems. Edited by: Mozer MC, Jordan MI, Petsche T. 1997, The MIT Press, 9: 424-430.

Google Scholar

Pelikan M, Mühlenbein H: The bivariate marginal distribution algorithm. Advances in Soft Computing – Engineering Design and Manufacturing. Edited by: Roy R, Furuhashi T, Chawdhry PK. 1999, London: Springer-Verlag, 521-535.

Google Scholar

Baluja S, Davies S: Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space. Proceedings of the 14th International Conference on Machine Learning. 1997, 30-38.

Google Scholar

Santana R, Ponce de León E, Ochoa A: The edge incident model. Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99). 1999, 352-359.

Google Scholar

Mühlenbein H, Mahnig T, Ochoa A: Schemata, distributions and graphical models in evolutionary optimization. Journal of Heuristics. 1999, 5 (2): 213-247.

Article
Google Scholar

Etxeberria R, Larrañaga P: Global optimization using Bayesian networks. Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99). 1999, 151-173.

Google Scholar

Pelikan M, Goldberg D, Cantú-Paz E: BOA: The Bayesian optimization algorithm. Evol Comput. 2000, 8 (3): 311-340.

Article
CAS
PubMed
Google Scholar

Alden MA: MARLEDA: Effective Distribution Estimation Through Markov Random Fields. PhD thesis. 2007, Faculty of the Graduate Schoool, University of Texas at Austin, USA

Google Scholar

Shakya S, McCall J: Optimization by estimation of distribution with DEUM framework based on Markov random fields. International Journal of Automation and Computing. 2007, 4 (3): 262-272.

Article
Google Scholar

Santana R: Estimation of distribution algorithms with Kikuchi approximations. Evolutionary Computation. 2005, 13: 67-97.

Article
PubMed
Google Scholar

Gámez JA, Mateo JL, Puerta JM: EDNA: Estimation of dependency networks algorithm. Bio-inspired Modeling of Cognitive Tasks, Second International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC, of Lecture Notes in Computer Science. Edited by: Mira J, Alvarez JR. 2007, 4527: 427-436.

Google Scholar

Geman S, Geman D: Stochastic relaxation, Gibbs distributions, and Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1984, 721-741. 6

Mühlenbein H, Mahnig T: Evolutionary synthesis of Bayesian networks for optimization. Advances in Evolutionary Synthesis of Intelligent Agents. Edited by: Patel M, Honavar V, Balakrishnan K. 2001, MIT Press, 429-455.

Google Scholar

Ochoa A, Mühlenbein H, Soto M: Factorized distribution algorithms using Bayesian networks bounded complexity. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2000. 2000, 212-215.

Google Scholar

Ochoa A, Mühlenbein H, Soto MR: A factorized distribution algorithm using single connected Bayesian networks. Parallel Problem Solving from Nature – PPSN VI 6th International Conference. Edited by: Schoenauer M, Deb K, Rudolph G, Yao X, Lutton E, Merelo JJ, Schwefel H. 2000, Springer Verlag, 787-796.

Chapter
Google Scholar

Pelikan M, Sastry K, Cantú-Paz E, Eds: Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. 2006, Studies in Computational Intelligence, Springer

Google Scholar

Bengoetxea E: Inexact Graph Matching Using Estimation of Distribution Algorithms. PhD thesis. 2003, Ecole Nationale Supérieure des Télécommunications

Google Scholar

Hauschild M, Pelikan M, Lima C, Sastry K: Analyzing probabilistic models in hierarchical BOA on traps and spin glasses. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2007. 2007, I: 523-530.

Google Scholar

Echegoyen C, Santana R, Lozano JA, Larrañaga P: Linkage in evolutionary computation. Studies in Computational Intelligence 2008 chap. The impact of probabilistic learning algorithms in EDAs based on Bayesian networks

Hauschild M, Pelikan M, Sastry K, Goldberg DE: Using previous models to bias structural learning in the hierarchical BOA. 2008, MEDAL Report No. 2008003, Missouri Estimation of Distribution Algorithms Laboratory (MEDAL)

Chapter
Google Scholar

Mathé C, Sagot M, Schiex T, Rouzé P: Current methods of gene prediction, their strengths and weaknesses. Nucleic Acids Research. 2002, 30 (19): 4103-4117.

Article
PubMed
PubMed Central
Google Scholar

Majoros W: Methods for Computational Gene Prediction. 2007, Cambridge University Press

Book
Google Scholar

Liu H, Yu L: Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering. 2005, 17 (4): 491-502.

Article
Google Scholar

Saeys Y, Inza I, Larrañaga P: A review of feature selection techniques in bioinformatics. Bioinformatics. 2007, 23 (19): 2507-2517.

Article
CAS
PubMed
Google Scholar

Inza I, Larrañaga P, Etxebarria R, Sierra B: Feature subset selection by Bayesian networks based optimization. Artificial Intelligence. 1999, 27: 143-164.

Google Scholar

Inza I, Merino M, Larrañaga P, Quiroga J, Sierra B, Girala M: Feature subset selection by genetic algorithms and estimation of distribution algorithms – A case study in the survival of cirrhotic patients treated with TIPS. Artificial Intelligence in Medicine. 2001, 23 (2): 187-205.

Article
CAS
PubMed
Google Scholar

Saeys Y, Degroeve S, Aeyels D, Peer Van de Y, Rouzé P: Fast feature selection using a simple estimation of distribution algorithm: A case study on splice site prediction. Bioinformatics. 2003, 19 (Suppl 2): 179-188.

Article
Google Scholar

Saeys Y, Degroeve S, Peer Van de Y: Towards a New Evolutionary Computation: Advances in Estimation of Distribution Algorithms. Springer 2006 chap. Feature ranking using an EDA-based wrapper approach, 243-257.

Saeys Y: Feature Selection for Classification of Nucleic Acid Sequences. PhD thesis. 2004, Ghent University, Belgium

Google Scholar

Saeys Y, Degroeve S, Aeyels D, Rouzé P, Peer Van de Y: Feature selection for splice site prediction: A new method using EDA-based feature ranking. BMC Bioinformatics. 2004, 5: 64-

Article
PubMed
PubMed Central
Google Scholar

Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caliguri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science. 1999, 286: 531-537.

Article
CAS
PubMed
Google Scholar

Blanco R, Larrañaga P, Inza I, Sierra B: Gene selection for cancer classification using wrapper approaches. International Journal of Pattern Recognition and Artificial Intelligence. 2004, 18 (8): 1373-1390.

Article
Google Scholar

Paul TK, Iba H: Identification of informative genes for molecular classification using probabilistic model building genetic algorithms. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2004. Lecture Notes in Computer Science 3102. 2004, 414-425.

Google Scholar

Paul T, Iba H: Gene selection for classification of cancers using probabilistic model building genetic algorithm. BioSystems. 2005, 82 (3): 208-225.

Article
CAS
PubMed
Google Scholar

Bielza C, Robles V, Larrañaga P: Estimation of distribution algorithms as logistic regression regularizers of microarray classifiers. Methods of Information in Medicine. 2008,

Google Scholar

Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2001, Springer-Verlag

Book
Google Scholar

Ben-Dor A, Shamir R, Yakhini Z: Clustering gene expression patterns. Journal of Computational Biology. 1999, 6 (3/4): 281-297.

Article
CAS
PubMed
Google Scholar

Peña J, Lozano J, Larrañaga P: Unsupervised learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2004, 12: 63-82.

Article
Google Scholar

Cano C, Blanco A, García F, López FJ: Evolutionary algorithms for finding interpretable patterns in gene expression data. International Journal on Computer Science and Information System. 2006, 1 (2): 88-99.

Google Scholar

Morgan J, Sonquistz J: Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association. 1963, 58: 415-434.

Article
Google Scholar

Cheng Y, Church GM: Biclustering of expression data. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology. 2000, AAAI Press, 93-103.

Google Scholar

Palacios P, Pelta DA, Blanco A: Obtaining biclusters in microarrays with population-based heuristics. Evo Workshops, Springer. 2006: 115-126.

Armañanzas R, Inza I, Larrañaga P: Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers. Comput Methods Programs Biomed. 2008, 91 (2): 110-121.

Article
PubMed
Google Scholar

Dai C, Liu J: Inducing pairwise gene interactions from time series data by EDA based Bayesian network. Conf Proc IEEE Eng Med Biol Soc. 2005, 7: 7746-7749.

PubMed
Google Scholar

Steipe B: Protein design concepts. The Encyclopedia of Computational Chemistry. Edited by: Schleyer PVR, Allinger NL, Clark T, Gasteiger J, Kollman PA, Schaefer III HF, Schreiner PR. 1998, Chichester: John Wiley & Sons, 2168-2185.

Google Scholar

Bacardit J, Stout M, Hirst JD, Sastry K, Llorà X, Krasnogor N: Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2007. 2007, I: 346-353.

Google Scholar

Santana R, Larrañaga P, Lozano JA: Protein folding in 2-dimensional lattices with estimation of distribution algorithms. Proceedings of the First International Symposium on Biological and Medical Data Analysis, of Lecture Notes in Computer Science. 2004, Barcelona: Springer Verlag, 3337: 388-398.

Google Scholar

Santana R: Advances in Probabilistic Graphical Models for Optimization and Learning Applications in Protein Modelling. PhD thesis. 2006, University of the Basque Country

Google Scholar

Santana R, Larrañaga P, Lozano JA: Protein folding in simplified models with estimation of distribution algorithms. IEEE Transactions on Evolutionary Computation. 2008, 12 (4): 418-438.

Article
Google Scholar

Belda I, Madurga S, Llorá X, Martinell M, Tarragó T, Piqueras M, Nicolás E, Giralt E: ENPDA: An evolutionary structure-based de novo peptide design algorithm. Journal of Computer-Aided Molecular Design. 2005, 19 (8): 585-601.

Article
CAS
PubMed
Google Scholar

Santana R, Larrañaga P, Lozano JA: Side chain placement using estimation of distribution algorithms. Artificial Intelligence in Medicine. 2007, 39: 49-63.

Article
PubMed
Google Scholar

Santana R, Larrañaga P, Lozano JA: Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem. Journal of Heuristics. 2007,

Google Scholar

Santana R, Larrañaga P, Lozano JA: The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms. Proceedings of the Fifth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, of Lecture Notes in Computer Science. Edited by: Marchiori E, Moore JH, Rajapakse JC. 2007, 4447: 247-257.

Google Scholar

Dill KA: Theory for the folding and stability of globular proteins. Biochemistry. 1985, 24 (6): 1501-1509.

Article
CAS
PubMed
Google Scholar

Hirst JD: The evolutionary landscape of functional model proteins. Protein Engineering. 1999, 12: 721-726.

Article
CAS
PubMed
Google Scholar

Santana R, Ochoa A, Soto MR: The mixture of trees factorized distribution algorithm. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2001. Edited by: Spector L, Goodman E, Wu A, Langdon W, Voigt H, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M, Burke E. 2001, San Francisco, CA: Morgan Kaufmann Publishers, 543-550.

Google Scholar

Mladenović N: A variable neighborhood algorithm – a new metaheuristics for combinatorial optimization. Abstracts of Papers Presented at Optimization Days. Montréal. 1995, 112-

Google Scholar

Harik GR, Lobo FG, Sastry K: Linkage learning via probabilistic modeling in the EcGA. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications, Studies in Computational Intelligence. Edited by: Pelikan M, Sastry K, Cantú-Paz E. 2006, Springer-Verlag, 39-62.

Chapter
Google Scholar