Evolutionary algorithms (EAs) have received increasing interests both in the academy and
industry. One main difficulty in applying EAs to real-world applications is that EAs usually …

Multiobjective evolutionary algorithms have incorporated surrogate models in order to
reduce the number of required evaluations to approximate the Pareto front of …

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 …

Although researchers have successfully incorporated metamodels in evolutionary
algorithms to solve computational-expensive optimization problems, they have scarcely …

Both multiple objectives and computation-intensive black-box functions often exist
simultaneously in engineering design problems. Few of existing multiobjective optimization …

Process modeling and optimization of polymerization processes with long chain branching
is currently an area of extensive research owing to the advantages and growing popularity of …

Two different methods to accelerate the search of a Multi-Objective Evolutionary Algorithm
(MOEA) using Artificial Neural Networks are presented. Two different methods are proposed …

In many practical engineering design and other scientific optimization problems, the
objective function is not given in closed form in terms of the design variables. Given the …

The layout design of complex engineering systems (such as satellite-module layout design)
is very difficult to solve in polynomial time. This is not only a complex coupled system design …