Metamodel-Based Global Optimization Methodologies for High Dimensional Expensive Black-box Problems
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[1] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[2] Devadatta M. Kulkarni,et al. Hierarchical overlapping coordination for large-scale optimization by decomposition , 1999 .
[3] Tao Jiang,et al. Target Cascading in Optimal System Design , 2003, DAC 2000.
[4] Manolis Papadrakakis,et al. Structural optimization using evolution strategies and neural networks , 1998 .
[5] G. Kroes,et al. Chebyshev high-dimensional model representation (Chebyshev-HDMR) potentials: application to reactive scattering of H2 from Pt(111) and Cu(111) surfaces. , 2012, Physical chemistry chemical physics : PCCP.
[6] Donald R. Jones,et al. Direct Global Optimization Algorithm , 2009, Encyclopedia of Optimization.
[7] Eliot Winer,et al. Development of visual design steering as an aid in large-scale multidisciplinary design optimization. Part I: method development , 2002 .
[8] H. Rabitz,et al. Random sampling-high dimensional model representation (RS-HDMR) and orthogonality of its different order component functions. , 2006, The journal of physical chemistry. A.
[9] N. Cressie. Spatial prediction and ordinary kriging , 1988 .
[10] C. Bloebaum,et al. Development of visual design steering as an aid in large-scale multidisciplinary design optimization. Part II: method validation , 2002 .
[11] Samuel Kaski,et al. Dimensionality reduction by random mapping: fast similarity computation for clustering , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[12] Donald R. Jones,et al. Variable Screening in Metamodel Design by Cross-Validated Moving Least Squares Method , 2003 .
[13] Hüseyin Kaya,et al. A recursive algorithm for finding HDMR terms for sensitivity analysis , 2003 .
[14] Kambiz Haji Hajikolaei,et al. Adaptive Orthonormal Basis Functions for High Dimensional Metamodeling With Existing Sample Points , 2012, DAC 2012.
[15] G. Gary Wang,et al. Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.
[16] Nanda Kambhatla,et al. Dimension Reduction by Local Principal Component Analysis , 1997, Neural Computation.
[17] Farrokh Mistree,et al. Statistical Approximations for Multidisciplinary Design Optimization: The Problem of Size , 1999 .
[18] Panos Y. Papalambros,et al. DECOMPOSITION ANALYSIS AND OPTIMIZATION OF AN AUTOMOTIVE POWERTRAIN DESIGN MODEL , 1999 .
[19] Dinh Quoc Tran,et al. Combining Lagrangian decomposition and excessive gap smoothing technique for solving large-scale separable convex optimization problems , 2011, Comput. Optim. Appl..
[20] Eva Riccomagno,et al. Experimental Design and Observation for Large Systems , 1996, Journal of the Royal Statistical Society: Series B (Methodological).
[21] Raphael T. Haftka,et al. Optimization and experiments , 1995 .
[22] H. Rabitz,et al. High Dimensional Model Representations , 2001 .
[23] Goldberg,et al. Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.
[24] J. M. Gablonsky. Direct version 2.0 user guide , 2001 .
[25] Richard H. Byrd,et al. A Trust Region Algorithm for Nonlinearly Constrained Optimization , 1987 .
[26] Chris H. Q. Ding,et al. Adaptive dimension reduction for clustering high dimensional data , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[27] Kenneth A. De Jong,et al. A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.
[28] Cheng-Ho Tho,et al. Crashworthiness Design Optimization Using Surrogate Models , 2007 .
[29] G. Gary Wang,et al. ADAPTIVE RESPONSE SURFACE METHOD - A GLOBAL OPTIMIZATION SCHEME FOR APPROXIMATION-BASED DESIGN PROBLEMS , 2001 .
[30] Richard Baumgartner,et al. Mapping high-dimensional data onto a relative distance plane - an exact method for visualizing and characterizing high-dimensional patterns , 2004, J. Biomed. Informatics.
[31] Metin Demiralp,et al. A factorized high dimensional model representation on the nodes of a finite hyperprismatic regular grid , 2005, Appl. Math. Comput..
[32] G. Gary Wang,et al. Design Optimization on "White-Box" Uncovered by Metamodeling , 2012 .
[33] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[34] L. Watson,et al. Trust Region Augmented Lagrangian Methods for Sequential Response Surface Approximation and Optimization , 1998 .
[35] H. Rabitz,et al. Practical Approaches To Construct RS-HDMR Component Functions , 2002 .
[36] R. Grandhi,et al. A global structural optimization technique using an interval method , 2001 .
[37] Shahryar Rahnamayan,et al. Cooperative Co-evolution with a new decomposition method for large-scale optimization , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).
[38] M. Alper Tunga. Factorized form of the indexing HDMR method for multivariate data modeling , 2013, Math. Comput. Model..
[39] J. Freidman,et al. Multivariate adaptive regression splines , 1991 .
[40] James Kennedy,et al. Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.
[41] Xiaodong Li,et al. Cooperatively Coevolving Particle Swarms for Large Scale Optimization , 2012, IEEE Transactions on Evolutionary Computation.
[42] Kemper Lewis,et al. A method for using legacy data for metamodel-based design of large-scale systems , 2004 .
[43] G. Gary Wang,et al. Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions , 2010 .
[44] Raphael T. Haftka,et al. Global structural optimization of a stepped cantilever beam using quasi-separable decomposition , 2010 .
[45] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[46] Panos Y. Papalambros,et al. Optimal Partitioning and Coordination Decisions in Decomposition-Based Design , 2007, DAC 2007.
[47] T. Simpson,et al. Fuzzy clustering based hierarchical metamodeling for design space reduction and optimization , 2004 .
[48] R. Srinivasan,et al. A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .
[49] D. Agrafiotis,et al. Nonlinear mapping of massive data sets by fuzzy clustering and neural networks , 2001 .
[50] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[51] G. Vanderplaats,et al. Survey of Discrete Variable Optimization for Structural Design , 1995 .
[52] M. Demiralp,et al. Hybrid high dimensional model representation (HHDMR) on the partitioned data , 2006 .
[53] M. Alper Tunga. An approximation method to model multivariate interpolation problems: Indexing HDMR , 2011, Math. Comput. Model..
[54] Herschel Rabitz,et al. Multicut‐HDMR with an application to an ionospheric model , 2004, J. Comput. Chem..
[55] I. Sobol. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .
[56] Herschel Rabitz,et al. High‐dimensional model representations generated from low order terms—lp‐RS‐HDMR , 2003, J. Comput. Chem..
[57] Ruichen Jin,et al. Analytical Metamodel-Based Global Sensitivity Analysis and Uncertainty Propagation for Robust Design , 2004 .
[58] Yeh-Liang Hsu,et al. A sequential approximation method using neural networks for engineering design optimization problems , 2003 .
[59] Douglas C. Montgomery,et al. Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .
[60] Klaus Schittkowski,et al. Test examples for nonlinear programming codes , 1980 .
[61] Panos Y. Papalambros,et al. Reduced representations of vector-valued coupling variables in decomposition-based design optimization , 2011 .
[62] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[63] H. Lilliefors. On the Kolmogorov-Smirnov Test for the Exponential Distribution with Mean Unknown , 1969 .
[64] Dimitrios Gunopulos,et al. Non-linear dimensionality reduction techniques for classification and visualization , 2002, KDD.
[65] G. G. Wang,et al. Design Space Reduction for Multi-Objective Optimization and Robust Design Optimization Problems , 2004 .
[66] M. Alper Tunga,et al. Introductory steps for an indexing based HDMR algorithm: lumping HDMR , 2008 .
[67] H. Fang,et al. Global response approximation with radial basis functions , 2006 .
[68] Songqing Shan,et al. Turning Black-Box Functions Into White Functions , 2011 .
[69] G. Gary Wang,et al. Trust Region based MPS Method for Global Optimization of High Dimensional Design Problems , 2012 .
[70] Klaus Schittkowski,et al. More test examples for nonlinear programming codes , 1981 .
[71] Hongfei Teng,et al. Cooperative Co-evolutionary Differential Evolution for Function Optimization , 2005, ICNC.
[72] H. Rabitz,et al. General foundations of high‐dimensional model representations , 1999 .
[73] D. Eichmann. More Test Examples For Nonlinear Programming Codes , 2016 .
[74] Herschel Rabitz,et al. Ratio control variate method for efficiently determining high‐dimensional model representations , 2006, J. Comput. Chem..
[75] Kambiz Haji Hajikolaei,et al. High Dimensional Model Representation With Principal Component Analysis , 2014 .
[76] Panos G Georgopoulos,et al. Correlation method for variance reduction of Monte Carlo integration in RS‐HDMR , 2003, J. Comput. Chem..
[77] Xin Yao,et al. Large scale evolutionary optimization using cooperative coevolution , 2008, Inf. Sci..
[78] M. Alper Tunga,et al. A Factorized High Dimensional Model Representation on the Partitioned Random Discrete Data , 2004 .
[79] Panos Y. Papalambros,et al. Optimal model-based decomposition of powertrain system design , 1995 .
[80] Herschel Rabitz,et al. Regularized random-sampling high dimensional model representation (RS-HDMR) , 2008 .
[81] G. Gary Wang,et al. Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .
[82] Hu Wang,et al. Adaptive MLS-HDMR metamodeling techniques for high dimensional problems , 2011, Expert Syst. Appl..
[83] Robert E. Tarjan,et al. Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..
[84] Aoying Zhou,et al. An adaptive and dynamic dimensionality reduction method for high-dimensional indexing , 2007, The VLDB Journal.
[85] Herschel Rabitz,et al. Efficient Implementation of High Dimensional Model Representations , 2001 .
[86] J. Friedman. Multivariate adaptive regression splines , 1990 .
[87] X. Yao,et al. Scaling up fast evolutionary programming with cooperative coevolution , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).
[88] Juan J. Alonso,et al. Improving the Performance of Design Decomposition Methods with POD , 2004 .
[89] G. G. Wang,et al. Metamodeling for High Dimensional Simulation-Based Design Problems , 2010 .
[90] Liqun Wang,et al. A Random-Discretization Based Monte Carlo Sampling Method and its Applications , 2002 .
[91] Shahryar Rahnamayan,et al. Toward effective initialization for large-scale search spaces , 2009 .
[92] D. Sofge,et al. A blended population approach to cooperative coevolution for decomposition of complex problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[93] Zoran Filipi,et al. Target cascading in vehicle redesign: a class VI truck study , 2002 .
[94] Christina Bloebaum,et al. NON-HIERARCHIC SYSTEM DECOMPOSITION IN STRUCTURAL OPTIMIZATION , 1992 .
[95] H. Mukai,et al. A New Technique for Nonconvex Primal-Dual Decomposition of a Large-Scale Separable Optimization Problem , 1983, 1983 American Control Conference.
[96] C. D. Perttunen,et al. Lipschitzian optimization without the Lipschitz constant , 1993 .
[97] G. G. Wang,et al. Mode-pursuing sampling method for global optimization on expensive black-box functions , 2004 .
[98] Herschel Rabitz,et al. Global uncertainty assessments by high dimensional model representations (HDMR) , 2002 .