Five facets of 6G: Research challenges and opportunities
While the fifth-generation systems are being rolled out across the globe, researchers have
turned their attention to the exploration of radical next-generation solutions. At this early …
turned their attention to the exploration of radical next-generation solutions. At this early …
Evolutionary dynamic multi-objective optimisation: A survey
Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly
growing area of investigation. EDMO employs evolutionary approaches to handle multi …
growing area of investigation. EDMO employs evolutionary approaches to handle multi …
Automatically designing CNN architectures using the genetic algorithm for image classification
Convolutional neural networks (CNNs) have gained remarkable success on many image
classification tasks in recent years. However, the performance of CNNs highly relies upon …
classification tasks in recent years. However, the performance of CNNs highly relies upon …
Evolutionary transfer optimization-a new frontier in evolutionary computation research
The evolutionary algorithm (EA) is a nature-inspired population-based search method that
works on Darwinian principles of natural selection. Due to its strong search capability and …
works on Darwinian principles of natural selection. Due to its strong search capability and …
A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection
In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …
Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II
Humans rarely tackle every problem from scratch. Given this observation, the motivation for
this paper is to improve optimization performance through adaptive knowledge transfer …
this paper is to improve optimization performance through adaptive knowledge transfer …
A knowledge guided transfer strategy for evolutionary dynamic multiobjective optimization
Y Guo, G Chen, M Jiang, D Gong… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-
optima closer to the true one as soon as possible once a new environment occurs. Previous …
optima closer to the true one as soon as possible once a new environment occurs. Previous …
Unleashing the power of artificial intelligence in materials design
The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing
the field of materials engineering thanks to their power to predict material properties, design …
the field of materials engineering thanks to their power to predict material properties, design …
A survey on evolutionary machine learning
Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that
function like humans. AI has been applied to many real-world applications. Machine …
function like humans. AI has been applied to many real-world applications. Machine …
Machine learning into metaheuristics: A survey and taxonomy
EG Talbi - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …
effective, and robust metaheuristics has become increasingly popular. Many of those …