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 …

Dynamic flexible job shop scheduling (JSS) is an important combinatorial optimization
problem with complex routing and sequencing decisions under dynamic environments …

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

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

Conventional evolutionary algorithms (EAs) are not well suited for solving expensive
optimization problems due to the fact that they often require a large number of fitness …

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 learning has achieved great success due to its ability to handle
multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which …

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) …