Dynamic flexible job shop scheduling (JSS) is an important combinatorial optimization
problem with complex routing and sequencing decisions under dynamic environments …

Against the background of smart manufacturing and Industry 4.0, how to achieve real-time
scheduling has become a problem to be solved. In this regard, automatic design for shop …

Traditional optimization solvers tend to start the search from scratch by assuming zero prior
knowledge about the task at hand. Generally speaking, the capabilities of solvers do not …

Evolutionary multitask learning has achieved great success due to its ability to handle
multiple tasks simultaneously. However, it is rarely used in the hyperheuristic domain, which …

Dynamic flexible job shop scheduling (JSS) has received widespread attention from
academia and industry due to its practical application value. It requires complex routing and …

To solve travelling salesman problems (TSPs), most existing evolutionary algorithms search
for optimal solutions from zero initial information without taking advantage of the historical …

Genetic programming (GP) is a well-known evolutionary computation technique, which has
been successfully used to solve various problems, such as optimization, image analysis …

In recent years, Internet of Things (IoT) security has attracted significant interest by
researchers due to new characteristics of IoT such as heterogeneity of devices, resource …

Transfer learning (TL) is the process by which some aspects of a machine learning model
generated on a source task is transferred to a target task, to simplify the learning required to …

Lack of knowledge is a common consequence of data incompleteness when learning from
real-world data. To deal with such a situation, this work utilizes transfer learning (TL) to …