Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been
applied in a wide range of applications. However, they still suffer from a high computational …

In this work, we consider multitasking in the context of solving multiple optimization problems
simultaneously by conducting a single search process. The principal goal when dealing with …

Locating multiple roots of nonlinear equation systems (NESs) remains a challenging and
meaningful task in the numerical optimization community. Although a large number of NES …

Abstract Most applications of Artificial Intelligence (AI) are designed for a confined and
specific task. However, there are many scenarios that call for a more general AI, capable of …

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

Knowledge transfer (KT) plays a key role in multitask optimization. However, most of the
existing KT methods still face two challenges. First, the tasks may commonly have different …

The evolutionary multitask optimization (EMTO) algorithm is a promising approach to solve
many-task optimization problems (MaTOPs), in which similarity measurement and …

Evolutionary multi-task optimization (EMTO) is a new branch of evolutionary algorithm (EA)
that aims to optimize multiple tasks simultaneously within a same problem and output the …

Evolutionary multitasking (EMT) with the ability to tackle multiple different tasks has attracted
more and more attention. The transferred knowledge among tasks can simultaneously …

As an emerging research paradigm in the field of evolutionary computation, evolutionary
multi-task optimization (EMTO) has received an increasing amount of attention due to its …