A review on probabilistic graphical models in evolutionary computation
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Concha Bielza | Pedro Larrañaga | Roberto Santana | Hossein Karshenas | C. Bielza | P. Larrañaga | Roberto Santana | Hossein Karshenas
[1] David E. Goldberg,et al. Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World , 2004, GECCO.
[2] Roberto Santana. A Markov Network Based Factorized Distribution Algorithm for Optimization , 2003, ECML.
[3] Dirk Thierens,et al. Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift , 2008, PPSN.
[4] Martin Pelikan,et al. Fitness Inheritance in the Bayesian Optimization Algorithm , 2004, GECCO.
[5] Shumeet Baluja,et al. A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .
[6] Petr Posík,et al. BBOB-benchmarking a simple estimation of distribution algorithm with cauchy distribution , 2009, GECCO '09.
[7] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[8] Concha Bielza,et al. Mateda-2.0: Estimation of Distribution Algorithms in MATLAB , 2010 .
[9] Petr Posík,et al. Stochastic Local Search Techniques with Unimodal Continuous Distributions: A Survey , 2009, EvoWorkshops.
[10] John R. Koza,et al. Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.
[11] Richard E. Neapolitan,et al. Learning Bayesian networks , 2007, KDD '07.
[12] Heinz Mühlenbein,et al. FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions , 1999, Evolutionary Computation.
[13] Pedro Larrañaga,et al. Combinatonal Optimization by Learning and Simulation of Bayesian Networks , 2000, UAI.
[14] J. A. Lozano,et al. Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing) , 2006 .
[15] Jianchao Zeng,et al. Estimation of distribution algorithm based on archimedean copulas , 2009, GEC '09.
[16] Roberto Santana,et al. Analyzing the probability of the optimum in EDAs based on Bayesian networks , 2009, 2009 IEEE Congress on Evolutionary Computation.
[17] Shigeyoshi Tsutsui,et al. Probabilistic Model-Building Genetic Algorithms in Permutation Representation Domain Using Edge Histogram , 2002, PPSN.
[18] 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).
[19] David E. Goldberg,et al. A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..
[20] David E. Goldberg,et al. Loopy Substructural Local Search for the Bayesian Optimization Algorithm , 2009, SLS.
[21] Martin Pelikan,et al. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) , 2006 .
[22] Peter Gr Unwald. The minimum description length principle and reasoning under uncertainty , 1998 .
[23] Paul A. Viola,et al. MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.
[24] Feng Qian,et al. Evolutionary algorithm using kernel density estimation model in continuous domain , 2009, 2009 7th Asian Control Conference.
[25] Concha Bielza,et al. Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables , 2011, EMO.
[26] Pedro Larrañaga,et al. Evolutionary computation based on Bayesian classifiers , 2004 .
[27] Bin Li,et al. Continuous Optimization based-on Boosting Gaussian Mixture Model , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[28] Pedro Larrañaga,et al. Research topics in discrete estimation of distribution algorithms based on factorizations , 2009, Memetic Comput..
[29] David E. Goldberg,et al. Node Histogram vs . Edge Histogram : A Comparison of PMBGAs in Permutation Domains , 2006 .
[30] Byoung-Tak Zhang,et al. Evolutionary optimization by distribution estimation with mixtures of factor analyzers , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[31] Qingfu Zhang,et al. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 RM-MEDA: A Regularity Model-Based Multiobjective Estimation of , 2022 .
[32] Dirk Thierens,et al. Advancing continuous IDEAs with mixture distributions and factorization selection metrics , 2001 .
[33] José Ignacio Hidalgo,et al. Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms , 2010, IEEE Congress on Evolutionary Computation.
[34] Jinung An,et al. Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs , 2012, Inf. Sci..
[35] Martin Pelikan,et al. Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).
[36] Petr Pos ´ ik. Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting , 2008 .
[37] David Maxwell Chickering,et al. Learning Bayesian Networks is NP-Complete , 2016, AISTATS.
[38] H. Iba,et al. Estimation of distribution programming based on Bayesian network , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[39] Jesús García,et al. MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms , 2011, Oper. Res. Lett..
[40] J. A. Lozano,et al. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .
[41] Roberto Santana,et al. Estimation of distribution algorithms: from available implementations to potential developments , 2011, GECCO.
[42] David Maxwell Chickering,et al. Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2000, J. Mach. Learn. Res..
[43] Amin Nikanjam,et al. Combinatorial effects of local structures and scoring metrics in bayesian optimization algorithm , 2009, GEC '09.
[44] David E. Goldberg,et al. The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..
[45] David E. Goldberg,et al. Genetic Algorithms, Clustering, and the Breaking of Symmetry , 2000, PPSN.
[46] D. Goldberg,et al. Evolutionary Algorithm Using Marginal Histogram Models in Continuous Domain , 2007 .
[47] Franz Rothlauf,et al. The correlation-triggered adaptive variance scaling IDEA , 2006, GECCO.
[48] Little,et al. [Lecture Notes in Mathematics] Combinatorial Mathematics V Volume 622 || Counting unlabeled acyclic digraphs , 1977 .
[49] David Maxwell Chickering,et al. Large-Sample Learning of Bayesian Networks is NP-Hard , 2002, J. Mach. Learn. Res..
[50] Jose Miguel Puerta,et al. EDNA: Estimation of Dependency Networks Algorithm , 2007, IWINAC.
[51] Siddhartha Shakya,et al. DEUM : a framework for an estimation of distribution algorithm based on Markov random fields , 2006 .
[52] Siddhartha Shakya,et al. Markov Networks in Evolutionary Computation , 2012 .
[53] Jun Zhang,et al. HPBILc: A histogram-based EDA for continuous optimization , 2009, Appl. Math. Comput..
[54] Hussein A. Abbass,et al. A Survey of Probabilistic Model Building Genetic Programming , 2006, Scalable Optimization via Probabilistic Modeling.
[55] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[56] David E. Goldberg,et al. Sporadic model building for efficiency enhancement of hierarchical BOA , 2006, GECCO.
[57] Edmondo A. Minisci,et al. MOPED: A Multi-objective Parzen-Based Estimation of Distribution Algorithm for Continuous Problems , 2003, EMO.
[58] Peter A. N. Bosman,et al. Adaptation of a Success Story in GAs: Estimation-of-Distribution Algorithms for Tree-based Optimization Problems , 2008 .
[59] Pedro Larrañaga,et al. EDA-PSO: A Hybrid Paradigm Combining Estimation of Distribution Algorithms and Particle Swarm Optimization , 2010, ANTS Conference.
[60] Dirk Thierens,et al. Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA , 2000, PPSN.
[61] Pedro Larrañaga,et al. Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks , 2005, Evolutionary Computation.
[62] 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.
[63] Pedro Larrañaga,et al. Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.
[64] Josef Schwarz,et al. Estimation Distribution Algorithm for mixed continuous-discrete optimization problems , 2002 .
[65] Raymond Chiong,et al. A Framework for Multi-model EDAs with Model Recombination , 2011, EvoApplications.
[66] Pedro Larrañaga,et al. Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks , 2000 .
[67] Qingfu Zhang,et al. DE/EDA: A new evolutionary algorithm for global optimization , 2005, Inf. Sci..
[68] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[69] Michèle Sebag,et al. Extending Population-Based Incremental Learning to Continuous Search Spaces , 1998, PPSN.
[70] A. Dawid. Conditional Independence in Statistical Theory , 1979 .
[71] Pedro Larrañaga,et al. Evolutionary Bayesian Classifier-Based Optimization in Continuous Domains , 2006, SEAL.
[72] Kumara Sastry,et al. Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) , 2006, Scalable Optimization via Probabilistic Modeling.
[73] Pedro Larrañaga,et al. Probabilistic graphical models in artificial intelligence , 2011, Appl. Soft Comput..
[74] Peter A. N. Bosman,et al. Matching inductive search bias and problem structure in continuous Estimation-of-Distribution Algorithms , 2008, Eur. J. Oper. Res..
[75] Yi Hong,et al. Estimation of distribution algorithms making use of both high quality and low quality individuals , 2009, 2009 IEEE International Conference on Fuzzy Systems.
[76] Hitoshi Iba,et al. A Bayesian Network Approach to Program Generation , 2008, IEEE Transactions on Evolutionary Computation.
[77] Ryszard S. Michalski,et al. LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning , 2004, Machine Learning.
[78] Doug Fisher,et al. Learning from Data: Artificial Intelligence and Statistics V , 1996 .
[79] E. Cantu-Paz,et al. The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1997, Evolutionary Computation.
[80] D. Goldberg,et al. BOA: the Bayesian optimization algorithm , 1999 .
[81] D. Goldberg,et al. Probabilistic Model Building and Competent Genetic Programming , 2003 .
[82] Peter A. Whigham,et al. Grammar-based Genetic Programming: a survey , 2010, Genetic Programming and Evolvable Machines.
[83] Pedro Larrañaga,et al. Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation , 2010, Evolutionary Computation.
[84] David E. Goldberg,et al. The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .
[85] P. Spirtes,et al. Causation, prediction, and search , 1993 .
[86] Roberto Santana,et al. Estimation of Distribution Algorithms with Kikuchi Approximations , 2005, Evolutionary Computation.
[87] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[88] M. Frydenberg. The chain graph Markov property , 1990 .
[89] Dirk Thierens,et al. The Linkage Tree Genetic Algorithm , 2010, PPSN.
[90] Heinz Mühlenbein,et al. Schemata, Distributions and Graphical Models in Evolutionary Optimization , 1999, J. Heuristics.
[91] Petros Koumoutsakos,et al. A Mixed Bayesian Optimization Algorithm with Variance Adaptation , 2004, PPSN.
[92] Byoung-Tak Zhang,et al. Evolutionary Continuous Optimization by Distribution Estimation with Variational Bayesian Independent Component Analyzers Mixture Model , 2004, PPSN.
[93] P. Spirtes,et al. Causation, Prediction, and Search, 2nd Edition , 2001 .
[94] W. Vent,et al. Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .
[95] David E. Goldberg,et al. Influence of selection and replacement strategies on linkage learning in BOA , 2007, 2007 IEEE Congress on Evolutionary Computation.
[96] Lawrence J. Fogel,et al. Artificial Intelligence through Simulated Evolution , 1966 .
[97] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[98] Concha Bielza,et al. Mining probabilistic models learned by EDAs in the optimization of multi-objective problems , 2009, GECCO.
[99] Jürgen Schmidhuber,et al. Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space , 1997, ECML.
[100] Dirk Thierens,et al. Linkage Information Processing In Distribution Estimation Algorithms , 1999, GECCO.
[101] Brendan J. Frey,et al. Mixture Modeling by Affinity Propagation , 2005, NIPS.
[102] David Heckerman,et al. Learning Gaussian Networks , 1994, UAI.
[103] David J. Spiegelhalter,et al. Local computations with probabilities on graphical structures and their application to expert systems , 1990 .
[104] David E. Goldberg,et al. Efficiency enhancement of genetic algorithms via building-block-wise fitness estimation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).
[105] 丁楠,et al. Histogram-Based Estimation of Distribution Algorithm: A Competent Method for Continuous Optimization , 2008 .
[106] Donald Geman,et al. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .
[107] Fernando José Von Zuben,et al. BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks , 2009, Inf. Sci..
[108] M. Pelikán,et al. The Bivariate Marginal Distribution Algorithm , 1999 .
[109] Nichael Lynn Cramer,et al. A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.
[110] P. Bosman,et al. Continuous iterated density estimation evolutionary algorithms within the IDEA framework , 2000 .
[111] H. Mühlenbein,et al. From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.
[112] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[113] R. Bouckaert. Bayesian belief networks : from construction to inference , 1995 .
[114] H. Akaike. A new look at the statistical model identification , 1974 .
[115] Arturo Hernández Aguirre,et al. Approximating the search distribution to the selection distribution in EDAs , 2009, GECCO.
[116] P. Spirtes,et al. An Algorithm for Fast Recovery of Sparse Causal Graphs , 1991 .
[117] Wray L. Buntine. Theory Refinement on Bayesian Networks , 1991, UAI.
[118] Qingfu Zhang,et al. On stability of fixed points of limit models of univariate marginal distribution algorithm and factorized distribution algorithm , 2004, IEEE Transactions on Evolutionary Computation.
[119] Xiao Wang,et al. Evolutionary optimization with Markov random field prior , 2004, IEEE Trans. Evol. Comput..
[120] Alexander E. I. Brownlee,et al. Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm , 2009 .
[121] Petr Posík. Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting , 2008, PPSN.
[122] Pedro Larrañaga,et al. Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions , 2002, Int. J. Approx. Reason..
[123] Shumeet Baluja,et al. Using Optimal Dependency-Trees for Combinational Optimization , 1997, ICML.
[124] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[125] Gregory F. Cooper,et al. A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .
[126] Concha Bielza,et al. Mateda-2.0: A MATLAB package for the implementation and analysis of estimation of distribution algorithms , 2010 .
[127] Jianchao Zeng,et al. Estimation of Distribution Algorithm based on copula theory , 2009, 2009 IEEE Congress on Evolutionary Computation.
[128] Nikolaus Hansen,et al. The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.
[129] R. W. Robinson. Counting unlabeled acyclic digraphs , 1977 .
[130] Arturo Hernández Aguirre,et al. Using Copulas in Estimation of Distribution Algorithms , 2009, MICAI.