The performance of most metaheuristic algorithms depends on parameters whose settings
essentially serve as a key function in determining the quality of the solution and the …

Evolutionary multi-task optimization (EMTO) is an optimization algorithm designed to
optimize multiple tasks simultaneously. In real life, different tasks often correlate to each …

Evolutionary multitasking (EMT) is a newly emerging research topic in the community of
evolutionary computation, which aims to improve the convergence characteristic across …

Knowledge transfer plays a vastly important role in solving multitask optimization problems
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …

Feature selection (FS) is an important data preprocessing technique in data mining and
machine learning, which aims to select a small subset of information features to increase the …

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 …

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) has recently attracted widespread attention in
the evolutionary computation community, which solves two or more tasks simultaneously to …

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) …

Many-task problem (MaTOP) is a kind of challenging multitask optimization problem with
more than three tasks. Two significant issues in solving MaTOPs are measuring intertask …