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 …

Humans rarely tackle every problem from scratch. Given this observation, the motivation for
this paper is to improve optimization performance through adaptive knowledge transfer …

Recent analytical studies have revealed that in spite of promising success in problem
solving, the performance of evolutionary multitasking deteriorates with decreasing similarity …

In this report, we suggest nine test problems for multi-task multi-objective optimization
(MTMOO), each of which consists of two multiobjective optimization tasks that need to be …

Evolutionary multitasking aims to explore implicit synergy among multiple optimization tasks.
Through the effect of hitchhiking, evolutionary multitasking is capable of improving the …

Recent research efforts have provided hints towards the innate ability of population-based
evolutionary algorithms to tackle multiple distinct optimization tasks at once by combining …

Evolutionary multitasking has recently emerged as a novel paradigm that enables the
similarities and/or latent complementarities (if present) between distinct optimization tasks to …

Ensemble learning is a powerful machine learning paradigm which leverages a collection of
diverse base learners to achieve better prediction performance than that could be achieved …

Multi-tasking optimization has now become a promising research topic that has attracted
increasing attention from researchers. In this paper, an efficient memetic evolutionary multi …

In recent years, multi-task optimization is one of the emerging topics among evolutionary
computation researchers. Multi-Factorial Evolutionary Algorithm (MFEA) is developed based …