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 …

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 multitasking (EMT) is an emerging research topic in the field of evolutionary
computation. In contrast to the traditional single-task evolutionary search, EMT conducts …

Feature selection (FS) is an important preprocessing technique for improving the quality of
feature sets in many practical applications. Particle swarm optimization (PSO) has been …

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 …

A multifactorial evolutionary algorithm (MFEA) is a recently proposed algorithm for
evolutionary multitasking, which optimizes multiple optimization tasks simultaneously. With …

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

Evolutionary multitask optimization (EMTO) is a newly emerging research area in the field of
evolutionary computation. It investigates how to solve multiple optimization problems (tasks) …

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