Search Results for author:
Found 7 papers, 2 papers with code
Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature
Using the predictive densities, we can compute the expected hypervolume improvement (EHVI) due to a solution.
Mono-surrogate vs Multi-surrogate in Multi-objective Bayesian Optimisation
In this work, we overcome these limitations by building a surrogate model for each objective function and show that the scalarising function distribution is not Gaussian.
R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation
In many real-world problems, a decision-maker has some preferences on the objective functions.
MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation
In this work we present MBORE: multi-objective Bayesian optimisation by density-ratio estimation, and compare it to BO across a range of synthetic and real-world benchmarks.
Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation
Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change.
What Makes an Effective Scalarising Function for Multi-Objective Bayesian Optimisation?
Performing multi-objective Bayesian optimisation by scalarising the objectives avoids the computation of expensive multi-dimensional integral-based acquisition functions, instead of allowing one-dimensional standard acquisition functions\textemdash such as Expected Improvement\textemdash to be applied.
Scalarizing Functions in Bayesian Multiobjective Optimization
However, their use in solving (computationally) expensive multi- and many-objective optimization problems in Bayesian multiobjective optimization is scarce.
