Incorporating a priori Knowledge in Probabilistic-Model Based Optimization

[1]  David E. Goldberg,et al.  Combining The Strengths Of Bayesian Optimization Algorithm And Adaptive Evolution Strategies , 2002, GECCO.

[2]  Pedro Larraanaga,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[3]  Pedro Larrañaga,et al.  Combinatonal Optimization by Learning and Simulation of Bayesian Networks , 2000, UAI.

[4]  Marcus Gallagher,et al.  Real-valued Evolutionary Optimization using a Flexible Probability Density Estimator , 1999, GECCO.

[5]  Dirk Thierens,et al.  Linkage Information Processing In Distribution Estimation Algorithms , 1999, GECCO.

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

[7]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[8]  Michèle Sebag,et al.  Extending Population-Based Incremental Learning to Continuous Search Spaces , 1998, PPSN.

[9]  Shumeet Baluja,et al.  Fast Probabilistic Modeling for Combinatorial Optimization , 1998, AAAI/IAAI.

[10]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[11]  Paul A. Viola,et al.  MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.

[12]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.

[13]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[14]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[15]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[16]  C. Chow,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[17]  Roberto Santana,et al.  The Factorized Distribution Algorithm and The Junction Tree: A Learning Perspective , 2005 .

[18]  Markus H ohfeld Towards a Theory of Population Based Incremental Learning , 2005 .

[19]  J. A. Lozano,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[20]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[21]  Pedro Larrañaga,et al.  The Convergence Behavior of the PBIL Algorithm: A Preliminary Approach , 2001 .

[22]  P. Bosman,et al.  Continuous iterated density estimation evolutionary algorithms within the IDEA framework , 2000 .

[23]  Pedro Larrañaga,et al.  Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks , 2000 .

[24]  Fernando G. Lobo,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[25]  J. A. Lozano,et al.  Analyzing the PBIL Algorithm by Means of Discrete Dynamical Systems , 2000 .

[26]  S. Baluja,et al.  Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .

[27]  Keith E. Mathias,et al.  Convergence Controlled Variation , 1996, FOGA.

[28]  A. Juels,et al.  Topics in black-box combinatorial optimization , 1996 .

[29]  David Heckerman,et al.  Learning Bayesian Networks: Search Methods and Experimental Results , 1995 .

[30]  Gilbert Syswerda,et al.  Simulated Crossover in Genetic Algorithms , 1992, FOGA.

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

[32]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[33]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .