Ensemble learning has gained success in machine learning with major advantages over
other learning methods. Bagging is a prominent ensemble learning method that creates …

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

Traditional optimization solvers tend to start the search from scratch by assuming zero prior
knowledge about the task at hand. Generally speaking, the capabilities of solvers do not …

Until recently, the potential to transfer evolved skills across distinct optimization problem
instances (or tasks) was seldom explored in evolutionary computation. The concept of …

Humans have the ability to identify recurring patterns in diverse situations encountered over
a lifetime, constantly understanding relationships between tasks and efficiently solving them …

Multitasking optimization can achieve better performance than traditional single-tasking
optimization by leveraging knowledge transfer between tasks. However, the current …

Recently, evolutionary multi-tasking (EMT) has surfaced as a new search paradigm in the
field of evolutionary computation to solve two or more tasks simultaneously. EMT algorithms …

The exponential spread of news and posts related to the COVID-19 pandemic on social
media platforms led to the emergence of the disinformation phenomenon. The phenomenon …

Evolutionary computation has largely exhibited its potential to complement conventional
learning algorithms in a variety of machine learning tasks, especially those related to …

Multi-task optimization is a hot research topic in the field of evolutionary computation. This
paper proposes an efficient surrogate-assisted multi-task evolutionary framework (named …