Evolutionary multitask optimization is an emerging research topic that aims to solve multiple
tasks simultaneously. A general challenge in solving multitask optimization problems …

Evolutionary multitask optimization (EMTO) studies how to simultaneously solve multiple
optimization tasks via evolutionary algorithms (EAs) while making the useful knowledge …

Multitask optimization aims to solve two or more optimization tasks simultaneously by
leveraging intertask knowledge transfer. However, as the number of tasks increases to the …

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 …

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 …

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

Currently, the most of multitask evolutionary algorithms views multiple tasks as factors
influencing the evolution of individuals. However, this consideration causes difficulty to …

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 …

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 …