An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8120
- @Article{journals/asc/ParkMNCZ18,
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author = "John Park and Yi Mei and Su Nguyen and Gang Chen2 and
Mengjie Zhang",
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title = "An investigation of ensemble combination schemes for
genetic programming based hyper-heuristic approaches to
dynamic job shop scheduling",
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journal = "Applied Soft Computing",
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year = "2018",
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volume = "63",
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pages = "72--86",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Combinatorial
optimisation, Job shop scheduling, Hyper-heuristic,
Ensemble learning",
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ISSN = "1568-4946",
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bibdate = "2018-01-12",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/asc/asc63.html#ParkMNCZ18",
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DOI = "doi:10.1016/j.asoc.2017.11.020",
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size = "15 pages",
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abstract = "Genetic programming based hyper-heuristic (GP-HH)
approaches that evolve ensembles of dispatching rules
have been effectively applied to dynamic job shop
scheduling (JSS) problems. Ensemble GP-HH approaches
have been shown to be more robust than existing GP-HH
approaches that evolve single dispatching rules for
dynamic JSS problems. For ensemble learning in
classification, the design of how the members of the
ensembles interact with each other, e.g., through
various combination schemes, is important for
developing effective ensembles for specific problems.
In this paper, we investigate and carry out systematic
analysis for four popular combination schemes. They are
majority voting, which has been applied to dynamic JSS,
followed by linear combination, weighted majority
voting and weighted linear combination, which have not
been applied to dynamic JSS. In addition, we propose
several measures for analysing the decision making
process in the ensembles evolved by GP. The results
show that linear combination is generally better for
the dynamic JSS problem than the other combination
schemes investigated. In addition, the different
combination schemes result in significantly different
interactions between the members of the ensembles.
Finally, the analysis based on the measures shows that
the behaviours of the evolved ensembles are
significantly affected by the combination schemes.
Weighted majority voting has bias towards single
members of the ensembles.",
- }
Genetic Programming entries for
John Park
Yi Mei
Su Nguyen
Aaron Chen
Mengjie Zhang
Citations