A review of surrogate-assisted evolutionary algorithms for expensive optimization problems
C He, Y Zhang, D Gong, X Ji - Expert Systems with Applications, 2023 - Elsevier
Many problems in real life can be seen as Expensive Optimization Problems (EOPs).
Compared with traditional optimization problems, the evaluation cost of candidate solutions …
Compared with traditional optimization problems, the evaluation cost of candidate solutions …
Evolutionary deep learning: A survey
As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …
(DL) has achieved great success in many real-world applications and attracted increasing …
A two-stage estimation of distribution algorithm with heuristics for energy-aware cloud workflow scheduling
Y Xie, XY Wang, ZJ Shen, YH Sheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the enormous increase in energy usage by cloud data centers for handling various
workflow applications, the energy-aware cloud workflow scheduling has become a hot …
workflow applications, the energy-aware cloud workflow scheduling has become a hot …
A meta-knowledge transfer-based differential evolution for multitask optimization
Knowledge transfer plays a vastly important role in solving multitask optimization problems
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …
Learning-aided evolution for optimization
Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …
solving. Similarly, computer scientists have made great efforts to design artificial neural …
Dynamic movement primitives based robot skills learning
In this article, a robot skills learning framework is developed, which considers both motion
modeling and execution. In order to enable the robot to learn skills from demonstrations, a …
modeling and execution. In order to enable the robot to learn skills from demonstrations, a …
Distributed differential evolution with adaptive resource allocation
Distributed differential evolution (DDE) is an efficient paradigm that adopts multiple
populations for cooperatively solving complex optimization problems. However, how to …
populations for cooperatively solving complex optimization problems. However, how to …
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 …
A multipopulation multiobjective ant colony system considering travel and prevention costs for vehicle routing in COVID-19-like epidemics
As transportation system plays a vastly important role in combatting newly-emerging and
severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing …
severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing …
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 …