As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

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 …

Due to the volatility and uncertainty of the financial market, investors often use the form of
portfolio to actively manage their assets. Portfolio optimization (PO) is becoming more and …

Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …

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 …

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

In the context of collaborative manufacturing, integrated optimization of spare parts
production and inventory management is practically important. This paper investigates an …

Evolutionary computation (EC) is a kind of meta-heuristic algorithm that takes inspiration
from natural evolution and swarm intelligence behaviors. In the EC algorithm, there is a …

The convergence and diversity enhancement of multiobjective evolutionary algorithms
(MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active …

Dual-resource-constrained scheduling problems (DRCSP) have been hotly debated during
the last two decades. DRCSPs focus on the causes and consequences of problems arising …