Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System
暂无分享,去创建一个
[1] Luc De Raedt,et al. Active Learning for High Throughput Screening , 2008, Discovery Science.
[2] Silvana Castano,et al. Global Viewing of Heterogeneous Data Sources , 2001, IEEE Trans. Knowl. Data Eng..
[3] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[4] D. Wolfe,et al. Nonparametric Statistical Methods. , 1974 .
[5] Bernhard Sendhoff,et al. On Evolutionary Optimization with Approximate Fitness Functions , 2000, GECCO.
[6] Bernhard Sendhoff,et al. Comparing neural networks and Kriging for fitness approximation in evolutionary optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[7] Jürgen Branke,et al. Faster convergence by means of fitness estimation , 2005, Soft Comput..
[8] Qingfu Zhang,et al. Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..
[9] A. E. Eiben,et al. Interactive Evolutionary Algorithms , 2015 .
[10] António Gaspar-Cunha,et al. A Multi-Objective Evolutionary Algorithm Using Neural Networks to Approximate Fitness Evaluations , 2005, Int. J. Comput. Syst. Signals.
[11] Michèle Sebag,et al. Comparison-Based Optimizers Need Comparison-Based Surrogates , 2010, PPSN.
[12] Lamjed Ben Said,et al. Steady state IBEA assisted by MLP neural networks for expensive multi-objective optimization problems , 2014, GECCO.
[13] Yaochu Jin,et al. Quality Measures for Approximate Models in Evolutionary Computation , 2003 .
[14] Bin Li,et al. An evolution strategy assisted by an ensemble of local Gaussian process models , 2013, GECCO '13.
[15] Qingfu Zhang,et al. A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems , 2014, IEEE Transactions on Evolutionary Computation.
[16] Xin Yao,et al. Using Class Imbalance Learning for Software Defect Prediction , 2013, IEEE Transactions on Reliability.
[17] Handing Wang,et al. Feasibility and utility of population-level geospatial injury profiling: prospective, national cohort study , 2015, The journal of trauma and acute care surgery.
[18] John W. Tukey,et al. Data Analysis and Regression: A Second Course in Statistics , 1977 .
[19] Kaisa Miettinen,et al. Nonlinear multiobjective optimization , 1998, International series in operations research and management science.
[20] Bernhard Sendhoff,et al. Generalizing Surrogate-Assisted Evolutionary Computation , 2010, IEEE Transactions on Evolutionary Computation.
[21] Handing Wang,et al. Optimizing trauma system design: The GEOS (Geospatial Evaluation of Systems of Trauma Care) approach , 2014, The journal of trauma and acute care surgery.
[22] Xin Yao,et al. Corner Sort for Pareto-Based Many-Objective Optimization , 2014, IEEE Transactions on Cybernetics.
[23] Kaisa Miettinen,et al. A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods , 2015, Structural and Multidisciplinary Optimization.
[24] Bernhard Sendhoff,et al. Structure optimization of neural networks for evolutionary design optimization , 2005, Soft Comput..
[25] Peter J. Fleming,et al. Evolutionary algorithms in control systems engineering: a survey , 2002 .
[26] Daniel O Scharfstein,et al. The value of trauma center care. , 2010, The Journal of trauma.
[27] Yoel Tenne,et al. A framework for memetic optimization using variable global and local surrogate models , 2009, Soft Comput..
[28] Handing Wang,et al. Access to specialist care: Optimizing the geographic configuration of trauma systems , 2015, The journal of trauma and acute care surgery.
[29] Andy J. Keane,et al. Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[30] Licheng Jiao,et al. A Sparse Spectral Clustering Framework via Multiobjective Evolutionary Algorithm , 2016, IEEE Transactions on Evolutionary Computation.
[31] Graham J. Williams,et al. Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum] , 2014, IEEE Computational Intelligence Magazine.
[32] Qingfu Zhang,et al. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.
[33] G. De Soete,et al. Clustering and Classification , 2019, Data-Driven Science and Engineering.
[34] Zbigniew Michalewicz,et al. Evolutionary Algorithms in Engineering Applications , 1997, Springer Berlin Heidelberg.
[35] Grad Dip Biostat,et al. Improved Functional Outcomes for Major Trauma Patients in a Regionalized, Inclusive Trauma System , 2012, Annals of surgery.
[36] Qingfu Zhang,et al. Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model , 2010, IEEE Transactions on Evolutionary Computation.
[37] Rommel G. Regis,et al. Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization Using Radial Basis Functions , 2014, IEEE Transactions on Evolutionary Computation.
[38] Michèle Sebag,et al. A mono surrogate for multiobjective optimization , 2010, GECCO '10.
[39] Lothar Thiele,et al. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..
[40] Bernhard Sendhoff,et al. Efficient evolutionary optimization using individual-based evolution control and neural networks: A comparative study , 2005, ESANN.
[41] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[42] Ujjwal Maulik,et al. A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I , 2014, IEEE Transactions on Evolutionary Computation.
[43] Petros Koumoutsakos,et al. Accelerating evolutionary algorithms with Gaussian process fitness function models , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[44] Hideyuki Takagi,et al. Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.
[45] James L. Arbuckle,et al. Full Information Estimation in the Presence of Incomplete Data , 1996 .
[46] Charles Mock,et al. Guidelines for essential trauma care , 2004 .
[47] Hong Cheng,et al. Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data , 2015, IEEE Transactions on Cybernetics.
[48] LiuBo,et al. A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems , 2014 .
[49] Andreas Zell,et al. Evolution strategies assisted by Gaussian processes with improved preselection criterion , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..
[50] Hirotaka Nakayama,et al. Meta-Modeling in Multiobjective Optimization , 2008, Multiobjective Optimization.
[51] K. Alagappan,et al. Guidelines for Essential Trauma Care. World Health Organization (2005), 103 pages, $21.60, ISBN: 92-4-154640-9 , 2006 .
[52] Scott D. Sudhoff,et al. An Evolutionary Computing Approach to Robust Design in the Presence of Uncertainties , 2010, IEEE Transactions on Evolutionary Computation.
[53] Bernhard Sendhoff,et al. Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles , 2004, GECCO.
[54] Michael T. M. Emmerich,et al. Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.
[55] Yaochu Jin,et al. Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..
[56] Qingfu Zhang,et al. Regularity Model for Noisy Multiobjective Optimization , 2016, IEEE Transactions on Cybernetics.
[57] Xindong Wu,et al. Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.
[58] Bernhard Sendhoff,et al. A framework for evolutionary optimization with approximate fitness functions , 2002, IEEE Trans. Evol. Comput..
[59] Kyriakos C. Giannakoglou,et al. Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence , 2002 .
[60] Bernhard Sendhoff,et al. A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation , 2007, GECCO '07.
[61] Pierre Hansen,et al. Variable Neighborhood Search , 2018, Handbook of Heuristics.
[62] Marion K Campbell,et al. The GEOS study: designing a geospatially optimised trauma system for Scotland. , 2014, The surgeon : journal of the Royal Colleges of Surgeons of Edinburgh and Ireland.
[63] Qingfu Zhang,et al. Fitness Modeling With Markov Networks , 2013, IEEE Transactions on Evolutionary Computation.
[64] Bernhard Sendhoff,et al. A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling , 2015, IEEE Transactions on Evolutionary Computation.
[65] G. W. Milligan,et al. A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis. , 1986, Multivariate behavioral research.
[66] Yaochu Jin,et al. A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..
[67] Qingfu Zhang,et al. Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition , 2009 .
[68] Yew-Soon Ong,et al. A study on polynomial regression and Gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm , 2005, 2005 IEEE Congress on Evolutionary Computation.
[69] Xin Yao,et al. A framework for finding robust optimal solutions over time , 2013, Memetic Comput..
[70] Yaochu Jin,et al. Reconstructing biological gene regulatory networks: where optimization meets big data , 2014, Evol. Intell..
[71] Aimin Zhou,et al. A Multioperator Search Strategy Based on Cheap Surrogate Models for Evolutionary Optimization , 2015, IEEE Transactions on Evolutionary Computation.
[72] Martin Holena,et al. Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks , 2010, IDEAL.
[73] Alessandro Turco,et al. Metamodels for Fast Multi-objective Optimization: Trading Off Global Exploration and Local Exploitation , 2010, SEAL.
[74] Sanja Petrovic,et al. Hybrid variable neighbourhood approaches to university exam timetabling , 2010, Eur. J. Oper. Res..
[75] Qingfu Zhang,et al. Stable Matching-Based Selection in Evolutionary Multiobjective Optimization , 2014, IEEE Transactions on Evolutionary Computation.
[76] Eckart Zitzler,et al. Indicator-Based Selection in Multiobjective Search , 2004, PPSN.
[77] Pierre Hansen,et al. Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..
[78] G. Dunteman. Principal Components Analysis , 1989 .
[79] Xin Yao,et al. Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization , 2015, IEEE Transactions on Evolutionary Computation.
[80] JinYaochu,et al. A systems approach to evolutionary multiobjective structural optimization and beyond , 2009 .
[81] Bernhard Sendhoff,et al. A systems approach to evolutionary multiobjective structural optimization and beyond , 2009, IEEE Computational Intelligence Magazine.
[82] Sanghamitra Bandyopadhyay,et al. Multiobjective GAs, quantitative indices, and pattern classification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[83] Saúl Zapotecas Martínez,et al. MOEA/D assisted by rbf networks for expensive multi-objective optimization problems , 2013, GECCO '13.
[84] Jürgen Branke,et al. Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.
[85] A. Peitzman,et al. The Effects of Trauma Center Care, Admission Volume, and Surgical Volume on Paralysis After Traumatic Spinal Cord Injury , 2009, Annals of surgery.