High-Dimensional Constrained Discrete Multi-objective Optimization Using Surrogates

[1]  Peter A. N. Bosman,et al.  Convolutional neural network surrogate-assisted GOMEA , 2019, GECCO.

[2]  Kaisa Miettinen,et al.  A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms , 2017, Soft Computing.

[3]  Tapabrata Ray,et al.  A Multiple Surrogate Assisted Decomposition-Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization , 2019, IEEE Transactions on Evolutionary Computation.

[4]  Ruck Thawonmas,et al.  Evolutionary algorithm using surrogate assisted model for simultaneous design optimization benchmark problem of multiple car structures , 2018, GECCO.

[5]  Akira Oyama,et al.  Benchmarking multiobjective evolutionary algorithms and constraint handling techniques on a real-world car structure design optimization benchmark problem , 2018, GECCO.

[6]  Akira Oyama,et al.  Proposal of benchmark problem based on real-world car structure design optimization , 2018, GECCO.

[7]  Kaisa Miettinen,et al.  A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization , 2018, IEEE Transactions on Evolutionary Computation.

[8]  Thomas Bäck,et al.  Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets , 2017, Appl. Soft Comput..

[9]  Juliane Müller,et al.  SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems , 2017, INFORMS J. Comput..

[10]  Thomas Bartz-Beielstein,et al.  Model-based methods for continuous and discrete global optimization , 2017, Appl. Soft Comput..

[11]  Julien Bect,et al.  A Bayesian approach to constrained single- and multi-objective optimization , 2015, Journal of Global Optimization.

[12]  Rommel G. Regis,et al.  Multi-objective constrained black-box optimization using radial basis function surrogates , 2016, J. Comput. Sci..

[13]  Pramudita Satria Palar,et al.  Framework for Robust Optimization Combining Surrogate Model, Memetic Algorithm, and Uncertainty Quantification , 2016, ICSI.

[14]  Taimoor Akhtar,et al.  Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection , 2016, J. Glob. Optim..

[15]  Jonathan A. Wright,et al.  Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation , 2015, Appl. Soft Comput..

[16]  Kaisa Miettinen,et al.  A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods , 2015, Structural and Multidisciplinary Optimization.

[17]  Tea Tusar,et al.  GP-DEMO: Differential Evolution for Multiobjective Optimization based on Gaussian Process models , 2015, Eur. J. Oper. Res..

[18]  R. Regis.  Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points , 2014 .

[19]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[20]  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.

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..