Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks

[1]  Martin Pelikan,et al.  Analyzing Probabilistic Models in Hierarchical BOA , 2009, IEEE Transactions on Evolutionary Computation.

[2]  David E. Goldberg,et al.  Loopy Substructural Local Search for the Bayesian Optimization Algorithm , 2009, SLS.

[3]  Roberto Santana,et al.  Analyzing the probability of the optimum in EDAs based on Bayesian networks , 2009, 2009 IEEE Congress on Evolutionary Computation.

[4]  Pedro Larrañaga,et al.  Research topics in discrete estimation of distribution algorithms based on factorizations , 2009, Memetic Comput..

[5]  José Antonio Lozano Alonso,et al.  A quantitative analysis of estimation of distribution algorithms based on Bayesian networks , 2009 .

[6]  Concha Bielza,et al.  MATEDA: A suite of EDA programs in Matlab , 2009 .

[7]  Martin Pelikan,et al.  Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions , 2008, PPSN.

[8]  Concha Bielza,et al.  A review of estimation of distribution algorithms in bioinformatics , 2008, BioData Mining.

[9]  Martin Pelikan,et al.  From mating pool distributions to model overfitting , 2008, GECCO '08.

[10]  Qingfu Zhang,et al.  Approaches to selection and their effect on fitness modelling in an Estimation of Distribution Algorithm , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[11]  Pedro Larrañaga,et al.  The Impact of Exact Probabilistic Learning Algorithms in EDAs Based on Bayesian Networks , 2008, Linkage in Evolutionary Computation.

[12]  Meng-Hiot Lim,et al.  Linkage in Evolutionary Computation , 2008, Linkage in Evolutionary Computation.

[13]  David E. Goldberg,et al.  Influence of selection and replacement strategies on linkage learning in BOA , 2007, 2007 IEEE Congress on Evolutionary Computation.

[14]  Pedro Larrañaga,et al.  Exact Bayesian network learning in estimation of distribution algorithms , 2007, 2007 IEEE Congress on Evolutionary Computation.

[15]  John A. W. McCall,et al.  Solving the MAXSAT problem using a multivariate EDA based on Markov networks , 2007, GECCO '07.

[16]  Raj Reddy,et al.  Improving Pronunciation Inference using N-Best List, Acoustics and Orthography , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[17]  Martin Pelikan,et al.  Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) , 2006 .

[18]  David E. Goldberg,et al.  Hierarchical Bayesian Optimization Algorithm , 2006, Scalable Optimization via Probabilistic Modeling.

[19]  Martin Pelikan,et al.  Searching for Ground States of Ising Spin Glasses with Hierarchical BOA and Cluster Exact Approximation , 2006, Scalable Optimization via Probabilistic Modeling.

[20]  Kalyanmoy Deb,et al.  Sufficient conditions for deceptive and easy binary functions , 1994, Annals of Mathematics and Artificial Intelligence.

[21]  Martin Pelikan,et al.  Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[22]  David E. Goldberg,et al.  Hierarchical BOA Solves Ising Spin Glasses and MAXSAT , 2003, GECCO.

[23]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

[24]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[25]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[26]  Yair Weiss,et al.  Approximate Inference and Protein-Folding , 2002, NIPS.

[27]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[28]  Jacob Goldberger,et al.  Sequentially finding the N-Best List in Hidden Markov Models , 2001, IJCAI.

[29]  Thomas Stützle,et al.  SATLIB: An Online Resource for Research on SAT , 2000 .

[30]  Heinz Mühlenbein,et al.  FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions , 1999, Evolutionary Computation.

[31]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[32]  Heinz Mühlenbein,et al.  Schemata, Distributions and Graphical Models in Evolutionary Optimization , 1999, J. Heuristics.

[33]  Thilo Mahnig,et al.  Evolutionary Synthesis of Bayesian Networks for Optimization , 1999 .

[34]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[35]  D. Nilsson,et al.  An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems , 1998, Stat. Comput..

[36]  Enrique F. Castillo,et al.  Expert Systems and Probabilistic Network Models , 1996, Monographs in Computer Science.

[37]  Robert G. Cowell,et al.  Sampling without replacement in junction trees , 1997 .

[38]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[39]  Nir Friedman,et al.  On the Sample Complexity of Learning Bayesian Networks , 1996, UAI.

[40]  Hans-Paul Schwefel,et al.  Parallel Problem Solving from Nature — PPSN IV , 1996, Lecture Notes in Computer Science.

[41]  Wray L. Buntine.  Theory Refinement on Bayesian Networks , 1991, UAI.

[42]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[43]  F. Barahona.  On the computational complexity of Ising spin glass models , 1982 .

[44]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[45]  Stephen A. Cook,et al.  The complexity of theorem-proving procedures , 1971, STOC.

[46]  T. Ruijgrok,et al.  On the theory of ferromagnetism , 1962 .

[47]  E. Ising.  Beitrag zur Theorie des Ferromagnetismus , 1925 .