A Markovianity based optimisation algorithm
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Siddhartha Shakya | Roberto Santana | José Antonio Lozano | J. A. Lozano | Roberto Santana | S. Shakya
[1] Donald Geman,et al. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .
[2] Siddhartha Shakya,et al. Solving the Ising Spin Glass Problem using a Bivariate EDA based on Markov Random Fields , 2006, 2006 IEEE International Conference on Evolutionary Computation.
[3] David E. Goldberg,et al. Bayesian Optimization Algorithm: From Single Level to Hierarchy , 2002 .
[4] Siddhartha Shakya,et al. An EDA based on local markov property and gibbs sampling , 2008, GECCO '08.
[5] Pedro Larrañaga,et al. Optimization by Max-Propagation Using Kikuchi Approximations , 2007 .
[6] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[7] Pedro Larrañaga,et al. Mixtures of Kikuchi Approximations , 2006, ECML.
[8] R. Santana,et al. The mixture of trees Factorized Distribution Algorithm , 2001 .
[9] Shumeet Baluja,et al. A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .
[10] J. A. Lozano,et al. Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing) , 2006 .
[11] J. Besag. Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .
[12] David E. Goldberg,et al. Hierarchical BOA Solves Ising Spin Glasses and MAXSAT , 2003, GECCO.
[13] Pedro Larrañaga,et al. Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.
[14] H. Mühlenbein,et al. From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.
[15] Heinz Mühlenbein,et al. A Factorized Distribution Algorithm Using Single Connected Bayesian Networks , 2000, PPSN.
[16] Michael I. Jordan. Learning in Graphical Models , 1999, NATO ASI Series.
[17] Michael I. Jordan. Graphical Models , 1998 .
[18] D. Goldberg,et al. A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap , 2006 .
[19] Alexander E. I. Brownlee,et al. Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm , 2009 .
[20] William T. Freeman,et al. Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.
[21] Heinz Mühlenbein,et al. FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions , 1999, Evolutionary Computation.
[22] Stuart German,et al. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1988 .
[23] David Maxwell Chickering,et al. Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..
[24] Stuart J. Russell,et al. Dynamic bayesian networks: representation, inference and learning , 2002 .
[25] D. E. Goldberg,et al. Simple Genetic Algorithms and the Minimal, Deceptive Problem , 1987 .
[26] BayesiannetworksPedro,et al. Combinatorial optimization by learning and simulation of , 2000 .
[27] Judea Pearl,et al. Probabilistic reasoning in intelligent systems , 1988 .
[28] Roberto Santana. A Markov Network Based Factorized Distribution Algorithm for Optimization , 2003, ECML.
[29] Hisashi Handa. EDA-RL: estimation of distribution algorithms for reinforcement learning problems , 2009, GECCO '09.
[30] Thilo Mahnig,et al. Comparing the adaptive Boltzmann selection schedule SDS to truncation selection , 2007 .
[31] J. A. Lozano,et al. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .
[32] Martin Pelikan,et al. Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).
[33] J. M. Hammersley,et al. Markov fields on finite graphs and lattices , 1971 .
[34] Martin Pelikan,et al. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) , 2006 .
[35] David E. Goldberg,et al. Hierarchical Problem Solving and the Bayesian Optimization Algorithm , 2000, GECCO.
[36] Roberto Santana,et al. Estimation of Distribution Algorithms with Kikuchi Approximations , 2005, Evolutionary Computation.
[37] Jose Miguel Puerta,et al. Improved EDNA (estimation of dependency networks algorithm) using combining function with bivariate probability distributions , 2008, GECCO '08.
[38] Qingfu Zhang,et al. Structure learning and optimisation in a Markov-network based estimation of distribution algorithm , 2009, 2009 IEEE Congress on Evolutionary Computation.
[39] Pedro Larrañaga,et al. Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation , 2010, Evolutionary Computation.
[40] C. Bron,et al. Algorithm 457: finding all cliques of an undirected graph , 1973 .
[41] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[42] David J. Spiegelhalter,et al. Local computations with probabilities on graphical structures and their application to expert systems , 1990 .
[43] Roberto Santana,et al. The Factorized Distribution Algorithm and The Junction Tree: A Learning Perspective , 2005 .
[44] David E. Goldberg,et al. A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..
[45] Carlos A. Coello Coello,et al. Objective reduction using a feature selection technique , 2008, GECCO '08.
[46] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[47] David E. Goldberg,et al. Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm , 2003, GECCO.
[48] Max Henrion,et al. Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.
[49] Zoubin Ghahramani,et al. Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms , 2004, UAI.
[50] D. E. Goldberg,et al. Genetic Algorithms in Search , 1989 .
[51] D. Goldberg,et al. BOA: the Bayesian optimization algorithm , 1999 .
[52] J. Davenport. Editor , 1960 .
[53] Martin V. Butz,et al. Hierarchical BOA on random decomposable problems , 2006, GECCO '06.
[54] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[55] Siddhartha Shakya,et al. Optimization by estimation of distribution with DEUM framework based on Markov random fields , 2007, Int. J. Autom. Comput..
[56] Pedro Larrañaga,et al. Protein Folding in 2-Dimensional Lattices with Estimation of Distribution Algorithms , 2004, ISBMDA.
[57] Heinz Mühlenbein,et al. Schemata, Distributions and Graphical Models in Evolutionary Optimization , 1999, J. Heuristics.
[58] John McCall,et al. Updating the probability vector using MRF technique for a Univariate EDA , 2004 .
[59] Goldberg,et al. Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.
[60] Pedro Larrañaga,et al. Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.
[61] Martin Pelikan,et al. Bayesian Optimization Algorithm , 2005 .
[62] Stan Z. Li,et al. Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.
[63] S. Baluja,et al. Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .
[64] H. Mühlenbein. Convergence of Estimation of Distribution Algorithms for Finite Samples , 2007 .
[65] Kumara Sastry,et al. Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) , 2006, Scalable Optimization via Probabilistic Modeling.
[66] R. Kikuchi. A Theory of Cooperative Phenomena , 1951 .
[67] Pedro Larrañaga,et al. Exact Bayesian network learning in estimation of distribution algorithms , 2007, 2007 IEEE Congress on Evolutionary Computation.
[68] Jose Miguel Puerta,et al. EDNA: Estimation of Dependency Networks Algorithm , 2007, IWINAC.
[69] Siddhartha Shakya,et al. DEUM : a framework for an estimation of distribution algorithm based on Markov random fields , 2006 .
[70] Grahame B. Smith. Stuart Geman and Donald Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images”; , 1987 .
[71] Siddhartha Shakya,et al. Using a Markov network model in a univariate EDA: an empirical cost-benefit analysis , 2005, GECCO '05.
[72] G. Harik. Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .
[73] Gilbert Owusu,et al. A fully multivariate DEUM algorithm , 2009, 2009 IEEE Congress on Evolutionary Computation.