Task Relatedness Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Fangfang_Zhang:ieeeTEC3,

• author = "Fangfang Zhang and Yi Mei and Su Nguyen and Kay Chen Tan and Mengjie Zhang",
• title = "Task Relatedness Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2023",
• volume = "27",
• number = "6",
• pages = "1705--1719",
• month = dec,
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2022.3199783",
• abstract = "Multitasking learning has been successfully used in handling multiple related tasks simultaneously. In reality, there are often many tasks to be solved together, and the relatedness between them is unknown in advance. In this paper, we focus on multitask genetic programming for the dynamic flexible job shop scheduling problems, and address two challenges. The first is how to measure the relatedness between tasks accurately. The second is how to select task pairs to transfer knowledge during the multitask learning process. To measure the relatedness between dynamic flexible job shop scheduling tasks, we propose a new relatedness metric based on the behaviour distributions of the variable-length genetic programming individuals. In addition, for more effective knowledge transfer, we develop an adaptive strategy to choose the most suitable assisted task for the target task based on the relatedness information between tasks. The findings show that in all of the multitask scena",
• notes = "also known as \cite{9861686}",

}

Genetic Programming entries for Fangfang Zhang Yi Mei Su Nguyen Kay Chen Tan Mengjie Zhang

Citations