Niching Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling: An Investigation into Distance Metrics
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Park:2016:GECCOcomp,
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author = "John Park and Yi Mei and Gang Chen2 and
Mengjie Zhang",
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title = "Niching Genetic Programming based Hyper-heuristic
Approach to Dynamic Job Shop Scheduling: An
Investigation into Distance Metrics",
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booktitle = "GECCO '16 Companion: Proceedings of the Companion
Publication of the 2016 Annual Conference on Genetic
and Evolutionary Computation",
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year = "2016",
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editor = "Tobias Friedrich and Frank Neumann and
Andrew M. Sutton and Martin Middendorf and Xiaodong Li and
Emma Hart and Mengjie Zhang and Youhei Akimoto and
Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and
Daniele Loiacono and Julian Togelius and
Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and
Faustino Gomez and Carlos M. Fonseca and
Heike Trautmann and Alberto Moraglio and William F. Punch and
Krzysztof Krawiec and Zdenek Vasicek and
Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and
Boris Naujoks and Enrique Alba and Gabriela Ochoa and
Simon Poulding and Dirk Sudholt and Timo Koetzing",
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pages = "109--110",
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keywords = "genetic algorithms, genetic programming: Poster",
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month = "20-24 " # jul,
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organisation = "SIGEVO",
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address = "Denver, USA",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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isbn13 = "978-1-4503-4323-7",
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DOI = "doi:10.1145/2908961.2908985",
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abstract = "investigates the application of fitness sharing to a
coevolutionary genetic programming based
hyper-heuristic (GP-HH) approach to a dynamic job shop
scheduling (DJSS) problem that evolves an ensemble of
dispatching rules. Evolving ensembles using GP-HH for
DJSS problem is a relatively unexplored area, and has
been shown to outperform standard GP-HH procedures that
evolve single rules. As a fitness sharing algorithm has
not been applied to the specific GP-HH approach, we
investigate four different phenotypic distance measures
as part of a fitness sharing algorithm. The fitness
sharing algorithm may potentially improve the diversity
of the constituent members of the ensemble and improve
the quality of the ensembles. The results show that the
niched co-evolutionary GP approaches evolve smaller
sized rules than the base coevolutionary GP approaches,
but have similar performances.",
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notes = "Distributed at GECCO-2016.",
- }
Genetic Programming entries for
John Park
Yi Mei
Aaron Chen
Mengjie Zhang
Citations