Expected improvement for expensive optimization: a review
The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
Surrogate-assisted autoencoder-embedded evolutionary optimization algorithm to solve high-dimensional expensive problems
Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve
computationally expensive problems with some success. However, traditional EAs are not …
computationally expensive problems with some success. However, traditional EAs are not …
Evolutionary optimization methods for high-dimensional expensive problems: A survey
Evolutionary computation is a rapidly evolving field and the related algorithms have been
successfully used to solve various real-world optimization problems. The past decade has …
successfully used to solve various real-world optimization problems. The past decade has …
Surrogate-assisted multipopulation particle swarm optimizer for high-dimensional expensive optimization
Surrogate-assisted evolutionary algorithms (SAEAs) are well suited for computationally
expensive optimization. However, most existing SAEAs only focus on low-or medium …
expensive optimization. However, most existing SAEAs only focus on low-or medium …
A surrogate-assisted multiswarm optimization algorithm for high-dimensional computationally expensive problems
This article presents a surrogate-assisted multiswarm optimization (SAMSO) algorithm for
high-dimensional computationally expensive problems. The proposed algorithm includes …
high-dimensional computationally expensive problems. The proposed algorithm includes …
A surrogate-assisted differential evolution algorithm for high-dimensional expensive optimization problems
The radial basis function (RBF) model and the Kriging model have been widely used in the
surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global …
surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global …
[HTML][HTML] Multi-surrogate assisted multi-objective evolutionary algorithms for feature selection in regression and classification problems with time series data
Feature selection wrapper methods are powerful mechanisms for reducing the complexity of
prediction models while preserving and even improving their precision. Meta-heuristic …
prediction models while preserving and even improving their precision. Meta-heuristic …
A multi-strategy surrogate-assisted competitive swarm optimizer for expensive optimization problems
Evolutionary computation is a powerful tool for solving nonconvex optimization problems.
Generally, evolutionary algorithms take numerous fitness evaluations to obtain the potential …
Generally, evolutionary algorithms take numerous fitness evaluations to obtain the potential …
A fast kriging-assisted evolutionary algorithm based on incremental learning
Kriging models, also known as Gaussian process models, are widely used in surrogate-
assisted evolutionary algorithms (SAEAs). However, the cubic time complexity of the …
assisted evolutionary algorithms (SAEAs). However, the cubic time complexity of the …
A bi-population cooperative optimization algorithm assisted by an autoencoder for medium-scale expensive problems
This study presents an autoencoder-embedded optimization (AEO) algorithm which involves
a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge …
a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge …