Heterogeneous Ensemble-Based Infill Criterion for Evolutionary Multiobjective Optimization of Expensive Problems
暂无分享,去创建一个
Tianyou Chai | Jinliang Ding | Yaochu Jin | Dan Guo | T. Chai | Yaochu Jin | Jinliang Ding | Dan Guo
[1] Marco Laumanns,et al. SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .
[2] Ferat Sahin,et al. A survey on feature selection methods , 2014, Comput. Electr. Eng..
[3] Wolfgang Ponweiser,et al. Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted -Metric Selection , 2008, PPSN.
[4] Huanhuan Chen,et al. Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[5] 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..
[6] Yaochu Jin,et al. Multi‐objective ensemble generation , 2015, WIREs Data Mining Knowl. Discov..
[7] John Shawe-Taylor,et al. Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.
[8] Kaisa Miettinen,et al. A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization , 2018, IEEE Transactions on Evolutionary Computation.
[9] Amanda J. C. Sharkey,et al. Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems , 1999 .
[10] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.
[11] Hirotaka Nakayama,et al. Approximate Optimization Using Computaional Intelligence and its Application to Reinforcement of Cable-stayed Bridges , 2006, Integrated Intelligent Systems for Engineering Design.
[12] Bernhard Sendhoff,et al. Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles , 2004, GECCO.
[13] James Kennedy,et al. Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.
[14] Handing Wang,et al. Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System , 2016, IEEE Transactions on Evolutionary Computation.
[15] Michael T. M. Emmerich,et al. Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.
[16] Pablo A. Estévez,et al. A review of feature selection methods based on mutual information , 2013, Neural Computing and Applications.
[17] Khaled Rasheed,et al. Comparison of methods for developing dynamic reduced models for design optimization , 2002, Soft Comput..
[18] Nicola Beume,et al. SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..
[19] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[20] R. Lyndon While,et al. A faster algorithm for calculating hypervolume , 2006, IEEE Transactions on Evolutionary Computation.
[21] Yaochu Jin,et al. Heterogeneous classifier ensembles for EEG-based motor imaginary detection , 2012, 2012 12th UK Workshop on Computational Intelligence (UKCI).
[22] Yaochu Jin,et al. Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..
[23] Jianchao Zeng,et al. Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems , 2017, IEEE Transactions on Evolutionary Computation.
[24] Hugo Jair Escalante,et al. A hybrid surrogate-based approach for evolutionary multi-objective optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.
[25] Søren Nymand Lophaven,et al. DACE - A Matlab Kriging Toolbox, Version 2.0 , 2002 .
[26] R. Haftka,et al. Ensemble of surrogates , 2007 .
[27] Yaochu Jin,et al. Pareto-based Multi-Objective Machine Learning , 2007, 7th International Conference on Hybrid Intelligent Systems (HIS 2007).
[28] Ping Zhu,et al. A method for selecting surrogate models in crashworthiness optimization , 2012 .
[29] Yaochu Jin,et al. Feature selection for high-dimensional classification using a competitive swarm optimizer , 2016, Soft Computing.
[30] Lamjed Ben Said,et al. Steady state IBEA assisted by MLP neural networks for expensive multi-objective optimization problems , 2014, GECCO.
[31] Bernhard Schölkopf,et al. Kernel Principal Component Analysis , 1997, ICANN.
[32] R. Lyndon While,et al. A review of multiobjective test problems and a scalable test problem toolkit , 2006, IEEE Transactions on Evolutionary Computation.
[33] Bernhard Sendhoff,et al. A systems approach to evolutionary multiobjective structural optimization and beyond , 2009, IEEE Computational Intelligence Magazine.
[34] Xin Yao,et al. Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.
[35] Qingfu Zhang,et al. Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model , 2010, IEEE Transactions on Evolutionary Computation.
[36] Jürgen Branke,et al. Faster convergence by means of fitness estimation , 2005, Soft Comput..
[37] Peter Tiño,et al. Managing Diversity in Regression Ensembles , 2005, J. Mach. Learn. Res..
[38] Yaochu Jin,et al. Classifier ensembles for image identification using multi-objective Pareto features , 2017, Neurocomputing.
[39] Qingfu Zhang,et al. Stable Matching-Based Selection in Evolutionary Multiobjective Optimization , 2014, IEEE Transactions on Evolutionary Computation.
[40] Saúl Zapotecas Martínez,et al. MOEA/D assisted by rbf networks for expensive multi-objective optimization problems , 2013, GECCO '13.
[41] Song Li,et al. A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection , 2016, IEEE Transactions on Power Systems.
[42] Marco Laumanns,et al. Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[43] Thomas Bäck,et al. A framework for evaluating meta-models for simulation-based optimisation , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).
[44] Yaochu Jin,et al. A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..
[45] John Doherty,et al. Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems , 2017, IEEE Transactions on Cybernetics.
[46] Verónica Bolón-Canedo,et al. A review of feature selection methods on synthetic data , 2013, Knowledge and Information Systems.
[47] Jonathon Shlens,et al. A Tutorial on Principal Component Analysis , 2014, ArXiv.
[48] Joshua D. Knowles,et al. ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems , 2006, IEEE Transactions on Evolutionary Computation.
[49] Bernhard Sendhoff,et al. Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[50] Bernd Bischl,et al. Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark , 2015, EMO.
[51] Robert Tibshirani,et al. A Comparison of Some Error Estimates for Neural Network Models , 1996, Neural Computation.
[52] Yaochu Jin,et al. Multi-train: A semi-supervised heterogeneous ensemble classifier , 2017, Neurocomputing.
[53] Qingfu Zhang,et al. Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems , 2014, IEEE Transactions on Evolutionary Computation.
[54] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[55] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[56] Yuhanis Yusof,et al. LSSVM parameters tuning with enhanced artificial bee colony , 2014, Int. Arab J. Inf. Technol..
[57] Mengjie Zhang,et al. Particle Swarm Optimisation for Feature Selection in Classification , 2014 .
[58] Eckart Zitzler,et al. Indicator-Based Selection in Multiobjective Search , 2004, PPSN.
[59] Thomas Bäck,et al. Metamodel-Assisted Evolution Strategies , 2002, PPSN.
[60] Bernhard Sendhoff,et al. Generalizing Surrogate-Assisted Evolutionary Computation , 2010, IEEE Transactions on Evolutionary Computation.
[61] Mengjie Zhang,et al. Binary particle swarm optimisation for feature selection: A filter based approach , 2012, 2012 IEEE Congress on Evolutionary Computation.
[62] Qingfu Zhang,et al. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.
[63] Harris Drucker,et al. Improving Regressors using Boosting Techniques , 1997, ICML.
[64] Tianyou Chai,et al. hybrid evolutionary multiobjective optimization strategy for the dynamic ower supply problem in magnesia grain manufacturing , 2013 .
[65] Ye Tian,et al. PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] , 2017, IEEE Computational Intelligence Magazine.
[66] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[67] Ponnuthurai N. Suganthan,et al. Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] , 2016, IEEE Computational Intelligence Magazine.
[68] A. Keane,et al. Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .
[69] Dirk Thierens,et al. The balance between proximity and diversity in multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..
[70] Virginia Torczon,et al. Using approximations to accelerate engineering design optimization , 1998 .
[71] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[72] Søren Nymand Lophaven,et al. DACE - A Matlab Kriging Toolbox , 2002 .
[73] 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).
[74] Maria L. Rizzo,et al. Measuring and testing dependence by correlation of distances , 2007, 0803.4101.