Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

Surrogate-assisted multiobjective evolutionary algorithms (MOEAs) have advanced the field
of computationally expensive optimization, but their progress is often restricted to low …

Many expensive optimization problems exist in various real-world applications. However
traditional evolutionary algorithms are inadequate for solving these problems directly …

In surrogate-assisted multi-/many-objective evolutionary optimization, each solution
normally has an approximated value on each objective, resulting in increased difficulties in …

In dealing with the expensive multiobjective optimization problem, some algorithms convert
it into a number of single-objective subproblems for optimization. At each iteration, these …

This work proposes a population evolution algorithm to deal with optimization problems
based on the evolution characteristics of the Phasmatodea (stick insect) population, called …

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling
computationally expensive multiobjective optimization problems (EMOPs), as the surrogate …

Acquisition functions for surrogate-assisted many-objective optimization require a delicate
balance between convergence and diversity. However, the conflicting nature between many …

Surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) have been
developed for solving expensive optimization problems. According to the roles that the …