Task Relatedness Based Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling
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- @Article{Fangfang_Zhang:ieeeTEC3,
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author = "Fangfang Zhang and Yi Mei and Su Nguyen and
Kay Chen Tan and Mengjie Zhang",
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title = "Task Relatedness Based Multitask Genetic Programming
for Dynamic Flexible Job Shop Scheduling",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2023",
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volume = "27",
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number = "6",
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pages = "1705--1719",
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month = dec,
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2022.3199783",
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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