Efficient Pareto Frontier Exploration using Surrogate Approximations

By B. Wilson, D. Cappelleri, Timothy W. Simpson, Mary Frecker. 2000

200 citations recorded by the source corpus.

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Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging (2008, background): “More particularly, grid sampling is unadapted to build Kriging because optimization of the directional correlation parameters is biased by the linear dependence existing within the spatial distribution [31, 32].”
Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, methodology): “[128] used the surrogate approximation in lieu of the computationally expensive analyses to”
Response surface approximation of pareto optimal front in multi-objective optimization (2004, methodology): “[26] and Cappelleri et al.”
A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, background): “The performance of MOEAs, in particularly those based on dominance relation comparisons, dramatically degenerates on MOPs with more than three objectives, also known as many-objective optimization problems (MaOPs) [11].”
Recent advances in surrogate-based optimization (2009, methodology): “To overcome the problem of long run-times a number of workers have advocated the use of surrogate modelling approaches within Pareto front frameworks [49, 84].”