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

As the name suggests, multi-objective optimisation involves optimising a number of
objectives simultaneously. The problem becomes challenging when the objectives are of …

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 …

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

The high computational cost of population based optimization methods, such as multi-
objective genetic algorithms (MOGAs), has been preventing applications of these methods …

This article proposes a three-level radial basis function (TLRBF)-assisted optimization
algorithm for expensive optimization. It consists of three search procedures at each iteration …

Coupled optimization methods based on multi-objective genetic algorithms and
approximation models are widely used in engineering optimizations. In the present paper, a …

Single- and Multi-objective Evolutionary Design Optimization Assisted by Gaussian
Random Field Metamodels Page 1 Single- and Multi-objective Evolutionary Design …

In this paper, we present a multi-surrogates assisted memetic algorithm for solving
optimization problems with computationally expensive fitness functions. The essential …

Evolutionary algorithms (EA s) have amply shown their promise in solving various search
and optimization problems for the past three decades. One of the hallmarks and niches of …