Instance Rotation Based Surrogate in Genetic Programming with Brood Recombination for Dynamic Job Shop Scheduling
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gp-bibliography.bib Revision:1.8098
- @Article{Fangfang_Zhang:ieeeTEC4,
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author = "Fangfang Zhang and Yi Mei and Su Nguyen and
Kay Chen Tan and Mengjie Zhang",
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title = "Instance Rotation Based Surrogate in Genetic
Programming with Brood Recombination for Dynamic Job
Shop Scheduling",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2023",
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volume = "27",
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number = "5",
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pages = "1192--1206",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Brood
recombination, dynamic job-shop scheduling, JSS,
instance rotation, surrogate",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2022.3180693",
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size = "15 pages",
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abstract = "Genetic programming has achieved great success for
learning scheduling heuristics in dynamic job shop
scheduling. In theory, generating a large number of
offspring for genetic programming, known as brood
recombination, can improve its heuristic generation
ability. However, it is time-consuming to evaluate
extra individuals. Phenotypic characterisation based
surrogates with K-nearest neighbours have been
successfully used for genetic programming to preselect
only promising individuals for real fitness evaluations
in dynamic job shop scheduling. However, sample
individuals used by surrogate are from only the current
generation, since the fitness of individuals across
generations are not comparable due to the rotation of
training instances. The surrogate cannot accurately
estimate the fitness of an offspring that is far away
from all the limited sample individuals at the current
generation. This paper proposes an effective instance
rotation based surrogate to address the abo",
-
notes = "also known as \cite{9789507}",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Su Nguyen
Kay Chen Tan
Mengjie Zhang
Citations