Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Zhang:2020:EuroGP,
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author = "Fangfang Zhang and Yi Mei and Su Nguyen and
Mengjie Zhang",
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title = "Guided Subtree Selection for Genetic Operators in
Genetic Programming for Dynamic Flexible Job Shop
Scheduling",
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booktitle = "EuroGP 2020: Proceedings of the 23rd European
Conference on Genetic Programming",
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year = "2020",
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month = "15-17 " # apr,
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editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
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series = "LNCS",
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volume = "12101",
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publisher = "Springer Verlag",
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address = "Seville, Spain",
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pages = "262--278",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Guided
subtree selection, JSS, Scheduling heuristic, Dynamic
flexible job shop scheduling",
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isbn13 = "978-3-030-44093-0",
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video_url = "https://www.youtube.com/watch?v=qf7hzHmxuAE",
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DOI = "doi:10.1007/978-3-030-44094-7_17",
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abstract = "Dynamic flexible job shop scheduling (DFJSS) has been
widely studied in both academia and industry. Both
machine assignment and operation sequencing decisions
need to be made simultaneously as an operation can be
processed by a set of machines in DFJSS. Using
scheduling heuristics to solve the DFJSS problems
becomes an effective way due to its efficiency and
simplicity. Genetic programming (GP) has been
successfully applied to evolve scheduling heuristics
for job shop scheduling automatically. However, the
subtrees of the selected parents are randomly chosen in
traditional GP for crossover and mutation, which may
not be sufficiently effective, especially in a huge
search space. This paper proposes new strategies to
guide the subtree selection rather than picking them
randomly. To be specific, the occurrences of features
are used to measure the importance of each subtree of
the selected parents. The probability to select a
subtree is based on its importance and the type of
genetic operators. This paper examines the proposed
algorithm on six DFJSS scenarios. The results show that
the proposed GP algorithm with the guided subtree
selection for crossover can converge faster and achieve
significantly better performance than its counterpart
in half of the scenarios while no worse in all other
scenarios without increasing the computational time.",
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notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of
\cite{Hu:2020:GP} EuroGP'2020 held in conjunction with
EvoCOP2020, EvoMusArt2020 and EvoApplications2020",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Su Nguyen
Mengjie Zhang
Citations