Dispatching Rules Selection Mechanism Using Support Vector Machine for Genetic Programming in Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{SALAMA:2023:ifacol,
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author = "Shady Salama and Toshiya Kaihara and
Nobutada Fujii and Daisuke Kokuryo",
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title = "Dispatching Rules Selection Mechanism Using Support
Vector Machine for Genetic Programming in Job Shop
Scheduling",
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journal = "IFAC-PapersOnLine",
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volume = "56",
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number = "2",
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pages = "7814--7819",
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year = "2023",
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note = "22nd IFAC World Congress",
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ISSN = "2405-8963",
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DOI = "doi:10.1016/j.ifacol.2023.10.1149",
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URL = "https://www.sciencedirect.com/science/article/pii/S2405896323015525",
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keywords = "genetic algorithms, genetic programming,
hyper-heuristics, support vector machine, SVM,
dispatching rules, job shop scheduling",
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abstract = "Several scholars have suggested using AI techniques to
automatically develop algorithms, which is known as
{"}hyper-heuristics{"}, to reduce the time and effort
required in conventional methods. Although the Genetic
Programming (GP) approach is the most popular
hyper-heuristic approach used to generate dispatching
rules to solve Job Shop Scheduling Problems (JSSPs),
high computational requirements remain a major
challenge for its wide applicability. Therefore, this
paper proposes a mechanism to reduce the computational
time needed to evaluate the solution quality of evolved
rules. The proposed mechanism uses training data
collected from the initial generation using a new
representation to train a Support Vector Machine (SVM)
classifier with a kernel of radial basis function.
Then, in subsequent generations, the trained classifier
is used to select the most promising (high-quality)
rules for fitness assessment and discard
low-performance ones. Consequently, only high-quality
rules are evaluated, and the computational power that
could have been used to evaluate poor rules is
preserved. The performance of the proposed mechanism is
analyzed using ten job shop instances from the
literature, with respect to prediction accuracy and
computational time. The results verify the
effectiveness of the proposed approach in reducing the
computational budget of the GP algorithm for JSSPs
while achieving high training and testing accuracy",
- }
Genetic Programming entries for
Shady Salama
Toshiya Kaihara
Nobutada Fujii
Daisuke Kokuryo
Citations