Surrogate-assisted, or meta-model based evolutionary computation uses efficient
computational models, often known as surrogates or meta-models, for approximating the …

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

GOMORS is a parallel response surface-assisted evolutionary algorithm approach to multi-
objective optimization that is designed to obtain good non-dominated solutions to black box …

The development of multi-objective evolutionary algorithms assisted by surrogate models
has increased in the last few years. However, in real-world applications, the high modality …

This chapter presents a short review of the state-of-the-art efforts for understanding and
solving problems with a large number of objectives (usually known as many-objective …

Some local search methods have been incorporated into surrogate-assisted multi-objective
evolutionary algorithms to accelerate the search toward the real Pareto front (PF). In this …

Multi-Objective Optimization (MOO) is very difficult for expensive functions because most
current MOO methods rely on a large number of function evaluations to get an accurate …

In this study, we evaluate several nongradient (evolutionary) search strategies for
minimizing mathematical function expressions. We developed and tested the genetic …

Multi-objective optimization of computationally expensive, multimodal problems is very
challenging, and is even more difficult for problems with many objectives (more than three) …

In recent years, the development of multi-objective evolutionary algorithms (MOEAs)
hybridized with mathematical programming techniques has significantly increased …