A Comprehensive Analysis on Reusability of GP-Evolved Job Shop Dispatching Rules
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Mei:2016:CEC,
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author = "Yi Mei and Mengjie Zhang",
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title = "A Comprehensive Analysis on Reusability of GP-Evolved
Job Shop Dispatching Rules",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "3590--3597",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7744244",
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abstract = "Genetic Programming (GP) has been extensively used to
automatically design dispatching rules for job shop
scheduling problems. However, the previous studies only
focus on the performance on the training instances. So
far, there is no systematic investigation of the
reusability of the GP-evolved rules on unseen
instances. In practice, it is desirable to train the
rules on smaller job shop instances, and apply them to
larger instances with more jobs and machines to save
training time. In this case, the reusability of the
GP-evolved rules under different numbers of jobs and
machines is an important issue. In this paper, a
comprehensive investigation is conducted to analyse how
the variation in the numbers of jobs and machines from
the training set to the test set affects the
reusability of the GP-evolved rules. It is found that
in terms of minimizing makespan, the reusability of the
GP-evolved rules highly depends on variation in the
numbers of jobs and machines. A better reusability can
be achieved by choosing training instances whose
numbers of jobs and machines (or at least the ratio
between the numbers of jobs and machines) are closer to
that of the test instances. Furthermore, the ratio
between the numbers of jobs and machines is
demonstrated to be an important factor to reflect the
complexity of an instance for dispatching rules. This
study is the first systematic investigation on the
reusability of GP-evolved dispatching rules.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Yi Mei
Mengjie Zhang
Citations