Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary
optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

Practical optimization problems often involve a large number of variables, and solving them
in a reasonable amount of time becomes a challenge. Competitive swarm optimizer (CSO) is …

In order to effectively schedule railway train delay, an adaptive cooperative co-evolutionary
differential evolution with dynamic hybrid mechanism of the quantum evolutionary algorithm …

Large-scale optimization problems (LSOPs) have drawn researchers' increasing attention
since their resemblance to real-world problems. However, due to the complex search space …

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 …

Evolutionary paradigms have been successfully applied to neural network designs for two
decades. Unfortunately, these methods cannot scale well to the modern deep neural …

Humans rarely tackle every problem from scratch. Given this observation, the motivation for
this paper is to improve optimization performance through adaptive knowledge transfer …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …