Expensive multi-objective optimization problems can be found in many real-world
applications, where their objective function evaluations involve expensive computations or …

This open access book provides a wealth of hands-on examples that illustrate how
hyperparameter tuning can be applied in practice and gives deep insights into the working …

Due to the instability of the pitching moment induced by the strong vortical flow at moderate
to high angles of attack in the unmanned combat aerial vehicle (UCAV) configuration …

A number of sparse multi-objective optimization problems (SMOPs) exist in the real world.
Decision variables in their Pareto optimal solutions are not only large-scale but also very …

It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for
optimizing numerically costly functions. However, BO is not often compared to widely …

Abstract This paper presents Adaptive Batch-ParEGO, an adaptive batch Bayesian
optimization method for expensive multi-objective problems. This method extends the …

This paper proposes the Self-Adaptive algorithm for Multi-Objective Constrained
Optimization by using Radial Basis Function Approximations, SAMO-COBRA. This algorithm …

Many transportation system problems can be formulated as high-dimensional expensive
multi-objective problems. They are challenging for Gaussian process-based Bayesian …

This paper proposes a novel Self-Adaptive algorithm for Multi-Objective Constrained
Optimization by using Radial Basis Function Approximations, SAMO-COBRA. The algorithm …

One method to solve expensive black-box optimization problems is to use a surrogate model
that approximates the objective based on previous observed evaluations. The surrogate …