Genetic Programming with Archive for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Xu:2021:CEC,
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author = "Meng Xu and Fangfang Zhang and Yi Mei and
Mengjie Zhang",
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booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Genetic Programming with Archive for Dynamic Flexible
Job Shop Scheduling",
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year = "2021",
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editor = "Yew-Soon Ong",
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pages = "2117--2124",
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address = "Krakow, Poland",
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month = "28 " # jun # "-1 " # jul,
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isbn13 = "978-1-7281-8393-0",
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abstract = "Genetic programming (GP) has achieved great success in
evolving effective scheduling rules to make real-time
decisions in dynamic flexible job shop scheduling
(DFJSS). To improve generalization, a commonly used
strategy is to change the training simulation(s) at
each generation of the GP process. However, with such a
simulation rotation, GP may lose potentially promising
individuals that happen to perform poorly in one
particular generation. To address this issue, this
paper proposed a new multi-tree GP with archive (MTAGP)
to evolve the routing and sequencing rules for DFJSS.
The archive is used to store the potentially promising
individuals of each generation during evolution of
genetic programming. The individuals in the archive can
then be fully used when the simulation is changed in
subsequent generations. Through extensive experimental
tests, the MTAGP algorithm proposed in this paper is
more effective than the multi-tree GP without archive
algorithm in a few scenarios. Further experiments were
carried out to analyze the use of the archive and some
possible guesses were ruled out. We argue that the use
of archives does increase the diversity of the
population. However, the number of individuals in the
archive that ranked in the top five of the new
population is small. Therefore, the archive may not be
able to greatly improve the performance. In the future,
we will investigate better ways to use the archive and
better ways to update individuals in the archive.",
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keywords = "genetic algorithms, genetic programming, Training,
Sequential analysis, Job shop scheduling, Sociology,
Dynamic scheduling, Routing, dynamic flexible job shop
scheduling, archive",
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DOI = "doi:10.1109/CEC45853.2021.9504752",
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notes = "Also known as \cite{9504752}",
- }
Genetic Programming entries for
Meng Xu
Fangfang Zhang
Yi Mei
Mengjie Zhang
Citations