Statistical Improvement Criteria for Use in Multiobjective Design Optimization

By Andy J. Keane. 2006

325 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2126e76a4559f9bddbf9f9295f28d1f070567b6b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Kriging Hyperparameter Tuning Strategies (2008, background): “and variance ( 7 ) can be found by taking the relevant partial derivatives of the log likelihood function and equating them to zero.”
Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted -Metric Selection (2008, background): “Recent approaches [2,4,13] transfer concepts of the popular EGO approach to multiple objectives and are described and reviewed in the next sections.”
Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark (2015, background): “Later, also set-based improvement criteria, specifically designed for multi-objective optimization, have been defined [1,9,14,18,23].”
Recent Advances in Bayesian Optimization (2022, background): “Expected Euclidean distance improvement (EEuI) [95] defines the product of the probability improvement function and an Euclidean distance-based improvement function for a closed-form expression of a bi-objective optimization problem.”
Infill sampling criteria for surrogate-based optimization with constraint handling (2012, methodology): “…2002) and weighted expected improvement (Sóbester et al. 2005) assign different weightings to exploration and exploitation; quantile-based expected improvement (Picheny et al. 2010) deals with noisy experiments; and multiobjective expected improvement (Keane 2006) handles multiobjective problems.”
Recent advances in surrogate-based optimization (2009, background): “Here we will consider only points which dominate the Pareto set (for formulations which deal with Pareto set augmentation see Keane [45]).”
A Tutorial on Bayesian Optimization (2018, background): “…developed in that same literature include multi-ﬁdelity optimization (Huang et al., 2006; S´obester et al., 2004), multi-objective optimization (Keane, 2006; Knowles, 2006; Moˇckus and Moˇckus, 1991), and a study of convergence rates (Calvin, 1997; Calvin and ˇZilinskas, 2000; Calvin and…”
Review of surrogate modeling in water resources (2012, methodology): “…by a weighting scheme) to be approximated by a single response surface surrogate [e.g., di Pierro et al., 2009; Knowles, 2006; Zhang et al., 2010], and (3) using multiple surrogate models for multiple objectives [e.g., Baú and Mayer, 2006; Keane, 2006; Li et al., 2008; Ponweiser et al., 2008].”
Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model (2010, background): “Keane [21] and Emmerich et al [23] have generalized the probability of improvement and the expected improvement to multiobjective optimization.”
Predictive quantification of surrogate model fidelity based on modal variations with sample density (2015, background): “In the literature, there exist various criteria for determining the locations of the infill points (Jones et al. 1998; Williams et al. 2011; Keane 2006; Booker et al. 1999; Audet et al. 2000).”