Realistic problems typically have many conflicting objectives. Therefore, it is instinctive to
look at the engineering problems as multi-objective optimization problems. This paper briefly …

Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Computers have undergone tremendous improvements in performance over the last 60
years, but those improvements have significantly slowed down over the last decade, owing …

Aquatic ecologists routinely count animals to provide critical information for conservation
and management. Increased accessibility to underwater recording equipment such as action …

Because of the tremendous growth in the number of smart vehicular devices and 5G mobile
technologies, the Internet of Things (IoT) has experienced rapid expansion. This has led to a …

In the application of machine learning to real-life decision-making systems, eg, credit scoring
and criminal justice, the prediction outcomes might discriminate against people with …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

With the rapid growth of Internet-of-Things (IoT) applications, data volumes have been
considerably increased. The processing resources of IoT nodes cannot cope with such huge …

Ensemble learning methods have already shown to be powerful techniques for creating
classifiers. However, when dealing with real-world engineering problems, class imbalance …

To solve dynamic multi-objective optimization problems, evolutionary algorithms must be
capable of quickly and accurately tracking the changing Pareto front such that they can …