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 modeling, also called metamodeling, has evolved and been extensively used
over the past decades. A wide variety of methods and tools have been introduced for …

Identification of variable interaction is essential for an efficient implementation of a divide-
and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an …

This paper concerns multiobjective optimization in scenarios where each solution evaluation
is financially and/or temporally expensive. We make use of nine relatively low-dimensional …

Evolutionary algorithms often have to solve optimization problems in the presence of a wide
range of uncertainties. Generally, uncertainties in evolutionary computation can be divided …

In some expensive multiobjective optimization problems (MOPs), several function
evaluations can be carried out in a batch way. Therefore, it is very desirable to develop …

This paper presents and analyzes in detail an efficient search method based on evolutionary
algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created …

During the last two decades, the water resources planning and management profession has
seen a dramatic increase in the development and application of various types of …

For expensive constrained optimization problems (ECOPs), the computation of objective
function and constraints is very time-consuming. This paper proposes a novel global and …

Using surrogate models in evolutionary search provides an efficient means of handling
today's complex applications plagued with increasing high-computational needs. Recent …