As an important research topic of machine learning, multiclass classification has wide
applications ranging from computer vision to bioinformatics. A variety of multiclass …

Evolutionary machine learning has drawn much attentions on solving data-driven learning
problem in the past decades, where classification is a major branch of data-driven learning …

Evolutionary multitasking, which solves multiple optimization tasks simultaneously, has
gained increasing research attention in recent years. By utilizing the useful information from …

As an emerging paradigm in evolutionary computation, multitasking evolutionary algorithm
can solve multiple self-contained tasks simultaneously. Its performance has largely relied on …

Recently, evolutionary multi-tasking (EMT) has been proposed as a new evolutionary search
paradigm that op-timizes multiple problems simultaneously. Due to the knowledge transfer …

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been
proposed to solve multi-objective multi-task optimization problems. However, EMT …

Recently, evolutionary multi-tasking (EMT) has been proposed as a new search paradigm
for optimizing multiple problems simultaneously. Since the beneficial knowledge can be …

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

Multiobjective multitasking optimization (MTO) is an emerging research topic in the field of
evolutionary computation. In contrast to multiobjective optimization, MTO solves multiple …

Task similarity is a major requisite to trigger knowledge sharing in evolutionary multitasking
optimization (EMTO). Unfortunately, most of the existing EMTO algorithms only focus on the …