Genetic Programming based Hyper-heuristics for Dynamic Job Shop Scheduling: Cooperative Coevolutionary Approaches
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Park:2016:EuroGP,
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author = "John Park and Yi Mei and Su Nguyen and Gang Chen2 and
Mengjie Zhang",
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title = "Genetic Programming based Hyper-heuristics for Dynamic
Job Shop Scheduling: Cooperative Coevolutionary
Approaches",
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booktitle = "EuroGP 2016: Proceedings of the 19th European
Conference on Genetic Programming",
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year = "2016",
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month = "30 " # mar # "--1 " # apr,
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editor = "Malcolm I. Heywood and James McDermott and
Mauro Castelli and Ernesto Costa and Kevin Sim",
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series = "LNCS",
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volume = "9594",
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publisher = "Springer Verlag",
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address = "Porto, Portugal",
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pages = "115--132",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-30668-1",
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DOI = "doi:10.1007/978-3-319-30668-1_8",
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abstract = "Job shop scheduling (JSS) problems are optimisation
problems that have been studied extensively due to
their computational complexity and application in
manufacturing systems. This paper focuses on a dynamic
JSS problem to minimise the total weighted tardiness.
In dynamic JSS, attributes of a job are only revealed
after it arrives at the shop floor. Dispatching rule
heuristics are prominent approaches to dynamic JSS
problems, and Genetic Programming based Hyper-heuristic
(GP-HH) approaches have been proposed to automatically
generate effective dispatching rules for dynamic JSS
problems. Research on static JSS problems shows that
high quality ensembles of dispatching rules can be
evolved by a GP-HH that uses cooperative coevolution.
Therefore, we compare two coevolutionary GP approaches
to evolve ensembles of dispatching rules for dynamic
JSS problems. First, we adapt the Multilevel Genetic
Programming (MLGP) approach, which has never been
applied to JSS problems. Second, we extend an existing
approach for a static JSS problem, called Ensemble
Genetic Programming for Job Shop Scheduling (EGP-JSS),
by adding less-myopic terminals that take job and
machine attributes outside of the scope of the
attributes commonly used in the literature. The results
show that MLGP for JSS evolves ensembles that are
significantly better than single less-myopic rules
evolved using GP with only little difference in
computation time. In addition, the rules evolved using
EGP-JSS perform better than the MLGP-JSS rules, but
MLGP-JSS evolves rules significantly faster than
EGP-JSS.",
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notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
conjunction with EvoCOP2016, EvoMusArt2016 and
EvoApplications2016",
- }
Genetic Programming entries for
John Park
Yi Mei
Su Nguyen
Aaron Chen
Mengjie Zhang
Citations