High-Dimensional Constrained Discrete Expensive Black-Box Optimization Using a Two-Phase Surrogate Approach

[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 .