Genetic programming approach to learning multi-pass heuristics for resource constrained job scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{Nguyen:2018:GECCOc,
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author = "Su Nguyen and Dhananjay Thiruvady and
Andreas Ernst and Damminda Alahakoon",
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title = "Genetic programming approach to learning multi-pass
heuristics for resource constrained job scheduling",
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booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2018",
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editor = "Hernan Aguirre and Keiki Takadama and
Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and
Andrew M. Sutton and Satoshi Ono and Francisco Chicano and
Shinichi Shirakawa and Zdenek Vasicek and
Roderich Gross and Andries Engelbrecht and Emma Hart and
Sebastian Risi and Ekart Aniko and Julian Togelius and
Sebastien Verel and Christian Blum and Will Browne and
Yusuke Nojima and Tea Tusar and Qingfu Zhang and
Nikolaus Hansen and Jose Antonio Lozano and
Dirk Thierens and Tian-Li Yu and Juergen Branke and
Yaochu Jin and Sara Silva and Hitoshi Iba and
Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and
Federica Sarro and Giuliano Antoniol and Anne Auger and
Per Kristian Lehre",
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isbn13 = "978-1-4503-5618-3",
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pages = "1167--1174",
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address = "Kyoto, Japan",
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DOI = "doi:10.1145/3205455.3205485",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming",
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abstract = "this study considers a resource constrained job
scheduling problem. Jobs need to be scheduled on
different machines satisfying a due time. If delayed,
the jobs incur a penalty which is measured as a
weighted tardiness. Furthermore, the jobs use up some
proportion of an available resource and hence there are
limits on multiple jobs executing at the same time. Due
to complex constraints and a large number of decision
variables, the existing solution methods, based on
meta-heuristics and mathematical programming, are very
time-consuming and mainly suitable for small-scale
problem instances. We investigate a genetic programming
approach to automatically design reusable scheduling
heuristics for this problem. A new representation and
evaluation mechanisms are developed to provide the
evolved heuristics with the ability to effectively
construct and refine schedules. The experiments show
that the proposed approach is more efficient than other
genetic programming algorithms previously developed",
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notes = "Also known as \cite{3205485} GECCO-2018 A
Recombination of the 27th International Conference on
Genetic Algorithms (ICGA-2018) and the 23rd Annual
Genetic Programming Conference (GP-2018)",
- }
Genetic Programming entries for
Su Nguyen
Dhananjay Thiruvady
Andreas Ernst
Damminda Alahakoon
Citations