Surrogate-Assisted Genetic Programming for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{zhang:2018:AJCAI,
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author = "Fangfang Zhang and Yi Mei and Mengjie Zhang",
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title = "{Surrogate-Assisted} Genetic Programming for Dynamic
Flexible Job Shop Scheduling",
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booktitle = "Australasian Joint Conference on Artificial
Intelligence",
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year = "2018",
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editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li",
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volume = "11320",
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series = "LNCS",
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pages = "766--772",
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address = "Wellington, New Zealand",
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month = dec # " 11-14",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Surrogate,
Dynamic flexible job shop scheduling",
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isbn13 = "978-3-030-03990-5",
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URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_69",
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DOI = "doi:10.1007/978-3-030-03991-2_69",
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abstract = "Genetic programming (GP) has been widely used for
automatically evolving priority rules for solving job
shop scheduling problems. However, one of the main
drawbacks of GP is the intensive computation time. This
paper aims at investigating appropriate surrogates for
GP to reduce its computation time without sacrificing
its performance in solving dynamic flexible job shop
scheduling (DFJSS) problems. Firstly, adaptive
surrogate strategy with dynamic fidelities of
simulation models are proposed. Secondly, we come up
with generation-range-based surrogate strategy in which
homogeneous (heterogeneous) surrogates are used in same
(different) ranges of generations. The results show
that these two surrogate strategies with GP are
efficient. The computation time are reduced by
22.9percent to 27.2percent and 32.6percent to
36.0percent, respectively. The test performance shows
that the proposed approaches can obtain rules with at
least the similar quality to the rules obtained by the
GP approach without using surrogates. Moreover, GP with
adaptive surrogates achieves significantly better
performance in one out of six scenarios. This paper
confirms the potential of using surrogates to solve
DFJSS problems.",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Mengjie Zhang
Citations