A Two-stage Genetic Programming Hyper-heuristic Approach with Feature Selection for Dynamic Flexible Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Zhang:2019:GECCO,
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author = "Fangfang Zhang and Yi Mei and Mengjie Zhang",
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title = "A Two-stage Genetic Programming Hyper-heuristic
Approach with Feature Selection for Dynamic Flexible
Job Shop Scheduling",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2019",
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editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
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isbn13 = "978-1-4503-6111-8",
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pages = "347--355",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3321707.3321790",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, Feature
Selection, Dynamic Flexible Job Shop Scheduling,
Genetic Programming Hyper-heuristics",
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size = "9 pages",
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abstract = "Dynamic flexible job shop scheduling (DFJSS) is an
important and a challenging combinatorial optimisation
problem. Genetic programming hyper-heuristic (GPHH) has
been widely used for automatically evolving the routing
and sequencing rules for DFJSS. The terminal set is the
key to the success of GPHH. There are a wide range of
features in DFJSS that reflect different
characteristics of the job shop state. However, the
importance of a feature can vary from one scenario to
another, and some features may be redundant or
irrelevant under the considered scenario. Feature
selection is a promising strategy to remove the
unimportant features and reduce the search space of
GPHH. However, no work has considered feature selection
in GPHH for DFJSS so far. In addition, it is necessary
to do feature selection for the two terminal sets
simultaneously. In this paper, we propose a new
two-stage GPHH approach with feature selection for
evolving routing and sequencing rules for DFJSS. The
experimental studies show that the best solutions
achieved by the proposed approach are better than that
of the baseline method in most scenarios. Furthermore,
the rules evolved by the proposed approach involve a
smaller number of unique features, which are easier to
interpret.",
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notes = "Also known as \cite{3321790} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Fangfang Zhang
Yi Mei
Mengjie Zhang
Citations