Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Mei:2017:EuroGP,
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author = "Yi Mei and Su Nguyen and Mengjie Zhang",
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title = "Evolving Time-Invariant Dispatching Rules in Job Shop
Scheduling with Genetic Programming",
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booktitle = "EuroGP 2017: Proceedings of the 20th European
Conference on Genetic Programming",
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year = "2017",
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month = "19-21 " # apr,
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editor = "Mauro Castelli and James McDermott and
Lukas Sekanina",
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series = "LNCS",
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volume = "10196",
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publisher = "Springer Verlag",
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address = "Amsterdam",
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pages = "147--163",
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organisation = "species",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-55695-6",
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DOI = "doi:10.1007/978-3-319-55696-3_10",
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abstract = "Genetic Programming (GP) has achieved success in
evolving dispatching rules for job shop scheduling
problems, particularly in dynamic environments.
However, there is still great potential to improve the
performance of GP. One challenge that is yet to be
addressed is the huge search space. In this paper, we
propose a simple yet effective approach to improve the
effectiveness and efficiency of GP. The new approach is
based on a newly defined time-invariance property of
dispatching rules, which is derived from the idea of
translational invariance from machine learning. Then,
we develop a new terminal selection scheme to guarantee
the time-invariance throughout the GP process. The
experimental studies show that by considering the
time-invariance, GP can achieve much better rules in a
much shorter time.",
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notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
inconjunction with EvoCOP2017, EvoMusArt2017 and
EvoApplications2017",
- }
Genetic Programming entries for
Yi Mei
Su Nguyen
Mengjie Zhang
Citations