High-Dimensional Constrained Discrete Expensive Black-Box Optimization Using a Two-Phase Surrogate Approach
暂无分享,去创建一个
[1] Rommel G. Regis,et al. A two-phase surrogate approach for high-dimensional constrained discrete multi-objective optimization , 2021, GECCO Companion.
[2] Fani Boukouvala,et al. Surrogate-based optimization for mixed-integer nonlinear problems , 2020, Comput. Chem. Eng..
[3] Peng Wang,et al. Kriging-assisted Discrete Global Optimization (KDGO) for black-box problems with costly objective and constraints , 2020, Appl. Soft Comput..
[4] Eduardo C. Garrido-Merchán,et al. Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes , 2017, Neurocomputing.
[5] Santu Rana,et al. Bayesian Optimization with Discrete Variables , 2019, Australasian Conference on Artificial Intelligence.
[6] Peter A. N. Bosman,et al. Convolutional neural network surrogate-assisted GOMEA , 2019, GECCO.
[7] Loïc Brevault,et al. Efficient global optimization of constrained mixed variable problems , 2018, Journal of Global Optimization.
[8] Rommel G. Regis,et al. Accelerated Random Search for constrained global optimization assisted by Radial Basis Function surrogates , 2018, J. Comput. Appl. Math..
[9] Akira Oyama,et al. Benchmarking multiobjective evolutionary algorithms and constraint handling techniques on a real-world car structure design optimization benchmark problem , 2018, GECCO.
[10] Ruck Thawonmas,et al. Evolutionary algorithm using surrogate assisted model for simultaneous design optimization benchmark problem of multiple car structures , 2018, GECCO.
[11] Akira Oyama,et al. Proposal of benchmark problem based on real-world car structure design optimization , 2018, GECCO.
[12] Joseph Morlier,et al. Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method , 2018 .
[13] Thomas Bäck,et al. Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets , 2017, Appl. Soft Comput..
[14] Thomas Bartz-Beielstein,et al. Model-based methods for continuous and discrete global optimization , 2017, Appl. Soft Comput..
[15] Julien Bect,et al. A Bayesian approach to constrained single- and multi-objective optimization , 2015, Journal of Global Optimization.
[16] Christine A. Shoemaker,et al. SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications , 2014, J. Glob. Optim..
[17] R. Regis. Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points , 2014 .
[18] James M. Parr,et al. Infill sampling criteria for surrogate-based optimization with constraint handling , 2012 .
[19] Rommel G. Regis,et al. Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions , 2011, Comput. Oper. Res..
[20] Andy J. Keane,et al. Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .